business analytics research project

15 Business Analyst Project Ideas and Examples for Practice

Explore business analyst real time projects examples curated for aspiring business analysts that will help them start their professional careers.

Your search for business analyst project examples ends here. This blog contains sample projects for business analyst beginners and professionals. So, continue reading this blog to know more about different business analyst projects ideas.

Business analysts are the demand of the twenty-first century! One can easily affirm this by looking at a report by the U.S. Bureau of Labor Statistics, which has revealed that as of May 2020, the median annual salary received by management analysts is $87,660. The bureau’s report also suggests that we are likely to witness an increase in the jobs of management analysts by 11% between 2019 and 2029. The rate is pretty higher than the average for other occupations. Additionally, the bureau mentioned that there is likely to be intense competition for such jobs because the role offers handsome salaries.

Avocado Machine Learning Project Python for Price Prediction

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The role of a business analyst primarily deals with analysing the growth of a business and suggesting methods to improve the existing strategies. Thus, to play such a crucial, one needs to possess a robust set of skills. Let us discuss a few of these to give you a more clear understanding of the skills required to become a business analyst .

Excellent verbal and written communication.

Communicate with different stakeholders and hold different meetings.

Up-to-date knowledge of new technologies and methodologies.

The capability of learning different business processes.

Ability to layout different ways of improving business growth.

Strong time management skills.

Understanding of various analytical tools and their implementation in revealing insights about the business.

Host different workshops and training sessions.

Knowledge of writing formal reports.

Having motivated you with our introduction of this blog, we now present business analyst sample projects that you can try to test/enhance your skills.

Table of Contents

Business analyst practice projects for beginners, business analyst real-time projects for intermediate professionals, advanced business analyst projects examples , top 15 business analyst project ideas for practice.

This section has beginner-friendly projects for business analyst roles that newbies in this domain can start with.

1) Market Basket Analysis  

Have you heard of the Beer-and-diapers story? In 2016, Mark Madsen, a research analyst, asked if there is a correlation between the sales of diapers and beers? It turned out that when a few stores placed beers closer to the diapers section, the beer sales went up. This strategy did not work for all the stores, but for a few, it did. By reflecting on this story, we want you to understand how important it is for a business to analyse the correlation between different purchased products, also called Market Basket Analysis.

Project Idea: In this project, you will work on a retail store’s data and learn how to realize the association between different products. Additionally, you will learn how to implement Apriori and Fpgrowth algorithms over the given dataset. You will also compare the two algorithms to understand the differences between them.

Source Code: Market basket analysis using apriori and fpgrowth algorithm  

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2) Estimating Retail Prices

For any product-selling business, deciding the price of their product is one of the most crucial decisions to make. And, thus for an aspiring business analyst, it becomes essential to understand what factors influence the decision-making process of product prices.

Project Idea: Mercari is a community-driven electronics-shopping application in Japan. In this project, you will build an automated price recommendation system using Mercari’s dataset to suggest prices to their sellers for different products based on the information collected. You will learn how to use Exploratory Data Analysis (EDA) tools and implement different machine learning algorithms like Neural Networks, Support Vector Machines, and Random Forest in R programming language. If you are specifically looking for business analyst finance planning projects for beginners , this project will be a good start. 

Source Code: Machine learning for Retail Price Recommendation with R

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3) Analyzing Customer Feedback

Collecting feedback from customers has become a norm for most companies. It provides them with the user’s perspective and guides them on what changes they should make to their product to increase its sales. Additionally, if the product reviews are public, potential customers feel motivated to trust the genuineness of the seller.

Project Idea: This project deals with the analysis of reviews of products available on an eCommerce website. You will work on textual data and implement data pre-processing methods like Gibberish Detection, Language Detection, Spelling Correction, and Profanity Detection. You will learn how to use the Random Forest model for ranking different reviews. Furthermore, you will explore the method of extracting sentiments and subjectivity from the reviews.

Source Code: Ecommerce product reviews - Pairwise ranking and sentiment analysis  

Recommended Reading: How to learn NLP from scratch in 2021?

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4) Predicting Avocado Prices

Did you know that more than 3 million new photos of avocado toasts were uploaded to Instagram every day in 2107? As per the British Vogue Magazine , this is indeed true. No doubt that so many of us enjoy avocado toasts in our breakfast. If you are also one of such people, this project idea will keep you hooked as it is all about avocados.

Project Idea: In this project, you will learn how a business analyst can use data analysis methods and help promote the growth of a business. You will work on the dataset of a Mexican-based company and layout an Avocado-price-map for them as they plan to expand their reach to different regions in the US. You will be testing the implementation of various models like the Adaboost Regressor, ARIMA time series model, and Facebook Prophet model to predict the Avocado prices.

Source Code: Avocado Price Prediction

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5) Predicting the Fate of a Loan Application

Those interested in banking projects for business analysts will indeed consider this one their favorite from this section as this project deals with loans. For understanding banks’ business model, it is crucial to learn the whole process of approving a loan application.

Project Idea: In this project, you will explore the different factors that influence the eligibility of a loan application’s approval. You will utilise different machine learning algorithms for predicting the chances of success of a loan application. This project will also help you learn about various statistical metrics used widely by business analysts like ROC curve, Gradient boosting, MCC Scorer, Synthetic Minority Over-sampling Technique, and XGBoost.

Source Code: Loan Eligibility Prediction 

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6) Predicting Customer Churn Rate

When customers start declining at an unexpected rate, various stakeholders go to business analysts for guidance. It is indeed one of the critical responsibilities of a business analyst to check the rate of customers churning out.

Project Idea: This project will guide you about performing univariate and bivariate analysis on the given dataset of a bank. You will learn how different statistical methods like SHAP (SHapley Additive exPlanations), RandomSearch, GridSearch, etc. should be used and interpreted. This project is another instance of a banking project for business analysts . So, if that’s your bias in sample business analysis projects , do check this one out. Source Code: Customer Churn Prediction

Recommended Reading: 

• Is Data Science Hard to Learn? (Answer: NO!)
• 15 Machine Learning Projects GitHub for Beginners in 2021
• Access Job Recommendation System Project with Source Code

After you have completely solved the above-mentioned projects, proceed to the sample business analyst projects listed in this section to further enhance your skills. These projects are slightly more challenging as they are closer to real-world problems. So, please refer to the source code links for help.

Explore SQL Database Projects to Add them to Your Data Engineer Resume.

7) Prediction of Selling Price for different Products

You must have noticed a few brands sometimes send their loyal customers' coupon codes to attract them. These coupons are often customized according to their purchase history with the brand and thus the offer varies from customer to customer.

Project Idea: In this project, you will work on the dataset of a retail company to estimate the price at which a customer is likely to buy a specific product. Once that is complete, you will use your estimation to design offers for different customers. For the solution, you will use machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks and use different metrics to test each of their performances.

You can add this project under the heading of business analyst finance projects on your resume to highlight the diversity of your skillset.

Source Code : Predict purchase amount of customers against various products

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8) Store Sales Prediction

In most firms, investors are usually external stakeholders that are not directly involved in the firm’s business but are definitely affected by it. And, it is the business analyst’s responsibility to keep the investors up-to-date with the existing and expected growth of the firm’s business model.

Project Idea: In this project, you will work on the dataset of 45 stores of the famous Walmart store chain. The goal is to predict the sales and revenue of different stores based on historical data. You will work with numeric and categorical feature variables and perform univariate & bivariate analysis to find the redundancy in variables. Additionally, you will learn the implementation of the ARIMA time series model and other machine learning models.

Source Code: Walmart Store Sales Forecasting

9) Analyzing Customer Churn

 It's the customer who pays the wages. --Henry Ford

Customer churn is painful for all the stakeholders in a company. A business analyst must thus look for ways in which the customer churn rate can be minimised. Additionally, they have to identify the cause behind customer churn to improving business growth. Having a fair idea of which customer is likely to churn out will help a business analyst develop better strategies.

Project Idea: In this project, you will be introduced to one of the popular classification machine learning algorithms , logistic regression. The goal is to use logistic regression for estimating the chances of churn for each customer. Through this project, you will get to explore different statistical methods, including confusion metric, recall, accuracy, precision, f1-score, AUC, and ROC.

Source Code: Churn Analysis for Streaming App using Logistic Regression

10) Estimating Future Inventory Demand

While inventory management does not directly fall in the bucket of a business analyst’s responsibilities, one may still find it there as inventory demand directly impacts several other aspects of a business including sales, marketing , finance, etc. With so many advancements taking place in the IT industry, a business analyst can easily use various tools to forecast the inventory demand. Project Idea: Through this project, you will explore the application of various machine learning models, including Bagging, Boosting, XGBoost, GBM, light GBM, and SVM for predicting the inventory demand of a bakery. This project will also introduce you to the implementation of autoML/H 2 0 and LSTM models.

Source Code: Inventory Demand Forecasting using Machine Learning in R

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11) Predicting Coupon Sales

In the previous section, we mentioned a project that will help you in creating customised coupons for a business’s customers. The next step will be to keep track of which coupons have been purchased. This will further help in understanding customer behaviour and preferences.

Project Idea: In this project, you will work on the dataset of one of Japan’s famous joint coupon websites, Recruit Ponpare. The goal is to estimate which coupons a customer is likely to buy based on their previous purchases and browsing behaviour on the website. You will use different graphical methods to visualise the data and various methods of handling missing values in a dataset. You will evaluate the cosine similarities of coupons and users and use them to make the desired predictions.

Source Code: Build a Coupon Purchase Prediction Model in R

12) Creating Product Bundles

Often when we visit a McDonald’s outlet, we intend to buy only a burger, but when we look at the meal menu, we end up buying the full mean instead of a single burger. This method of combining a few products and selling them as a single unit is called product bundling. It helps in increasing the sales of a business.

Project Idea: In this project, you will identify product bundles from the given sales data. While market basket analysis is commonly used for solving such problems, you will be using the time series clustering method. The two techniques will be compared to understand the significance of both methods.

Source Code: Identify Product Bundles from Sales Data

Recommended Reading: 50 Business Analyst Interview Questions and Answers

Professional Business Analysts planning to aim for senior roles will find business analyst projects samples in this section. A senior business analyst is often expected to possess knowledge of Big Data tools . Thus, you will find the projects described below rely on these tools.

13) Analyzing Log Files

If you are new to Big data projects and want to learn the basics of data analysis using Hive, then this project will be a good start. This simple project has been added to this section to prepare you for the next two projects.

Project Idea: This project is simply about analyzing log files of different users of a website. You will learn how to use Apache Hive to extract meaningful data insights by executing real-time queries.

Source Code: Hive Sample Projects-Learn data analysis using sample data for Hive

14) Retain Analytics

Retail Analytics refers to the complete analysis of various aspects of a business, including customer behavior and demands, supply chain analysis, sales, marketing, and inventory management. Such deeper analysis assists in deeply understanding the business model and smoothens various decision-making processes.

Project Idea: In this project, you will work with the Walmart stores dataset and use various Big Data techniques and tools to perform retail analytics. You will explore how to use tools like AWS EC2, Docker -composer, HDFS, Apache Hive, and MySQL for implementing the full solution.

Source Code: Retail Analytics Project Example using Sqoop, HDFS, and Hive

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15) Analyzing Airline Data

Data has become a huge asset for many industries, and the airline industry is no exception. They rely on big data to answer a few of the most vital questions like when the customers are likely to witness minimum delay in flight timings? Are older planes more prone to delays? etc. Project Idea: For this project, you will work on the dataset of an airline and find answers to questions like the ones mentioned above. You will be guided on how to ingest data and extract it using Cloudera VMware. After that, you will learn about preprocessing the data using Apache Pig. Next, you will use Hive for making tables and performing Exploratory Data Analysis. You will also get to explore the application of HCatloader and parquet through this project. Source Code: Hadoop Hive Project on Airline Dataset Analysis

Hey, Hey! The blog hasn’t ended yet. Going by what Steve Jobs said. “ ‘Learn continually. There's always “one more thing” to learn.’, we don’t want your learning journey to stop here. Check out more such Data Science Projects and Big Data projects from our repository to work on more exciting projects like the ones discussed in this blog.

About the Author

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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Top Business Analytics Projects to Sharpen Your Skills and Build Your Business Analytics Portfolio

Business analytics is the tool used by professionals to make sound business decisions. It fosters the profitability of a business and helps increase a business’s market value. This is why business owners are keen on employing business analysts or business intelligence analysts to help achieve their objectives. 

With the increase in demand for such professionals, you will need to develop cutting-edge skills to land a position. If you don’t want to get certified yet, you should consider completing business analytics projects. These projects will help you gain hands-on experience and showcase to employers your expertise and skill level in business analytics .

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5 skills that business analytics projects can help you practice.

Completing business analytics projects can set you apart from other job applicants. These projects will help you develop real-world experience. Whether your focus is on customer relationship management, financial management, human resources, marketing, or supply chain management, these projects will be invaluable to your growth. 

• SQL. SQL is a popular coding language used in databases. Through it, analysts and developers write queries to retrieve data from transaction databases. Data scientists and data analysts also rely on the coding language. After retrieving data from the databases, business analysts present the data visually to stakeholders. 
• Statistical Languages. Working on business analytics projects will expose you to statistical languages such as R and Python. Analysts rely on R for statistical analysts and Python for programming. A combination of these languages can help you work easily with big data sets.
• Statistical Software. These projects are also quite beneficial in helping you build skills in statistical software. Through the projects, you will become familiar with SAS, SPS, and Excel. 
• Data Visualization . A significant part of business analytics involves data visualization . As part of the projects, you will not only learn how to fetch data from different databases but also present them to stakeholders. This means you will get to familiarize yourself with data visualization tools and techniques. 
• Machine Learning. As you work on the projects, you will encounter many instances where machine learning is vital . You should expect to use business intelligence tools for curating friendly user interfaces and augmented analytics to receive accurate insights. 

Best Business Analytics Project Ideas for Beginners 

Being a beginner in the field of business analytics does not mean you cannot pursue projects to boost your portfolio. There are plenty of beginner-friendly business analytics project ideas to help you grow your skills in business analytics. Below we curated a list of the best beginner project ideas to jumpstart your career.

Data on Employee Performance and Resignation

• Business Analytics Skills Practiced: Data Visualization, Machine Learning 

In this project, you will provide a company with data that can explain why employees are resigning. The goal is to take these results and use them to improve the business environment. You can take into account the employee’s distance from home, work culture, or job role. You should evaluate each factor with the relationship to resignation. 

Forecasting Sales of a Mall During December

• Business Analytics Skills Practiced: Machine Learning, Data Visualization

A mall features a variety of shops and stalls that see high traffic during the holiday season. In this project, you should be able to determine which is the most popular product and how to ensure the shop does not run out of stock. You should check the current inventory and the customer segmentation to ensure you can forecast the sales properly. 

Predicting the Success of a Product

In this project, you can rely on your analytical skills to determine if a particular product will sell well in a specified market. For instance, you can focus on the entertainment industry. With thousands of hours of content being disseminated daily, it is quite challenging to establish which song or movie will do well. You will need to make use of historical data and models to make predictions. 

Predicting Sales for an Upcoming Car Design

This project involves taking a deep dive into customer needs and wants. You can work on a project to determine if a new car design, color, or shape will appeal to the target audience. There is a wide variety of cars available in the market to help you determine the most popular vehicle. 

Customer Segmentation

• Business Analytics Skills Practiced: Machine Learning, Data Visualization, Statistical Languages

In this project, you will deal with a wide customer base of an organization. The main aim of the project is to provide the best customer segmentation to the business leader, development, and marketing team to design campaigns. You should check on the spending ability of the customers and the most popular products. 

Best Intermediate Business Analytics Project Ideas 

If you have confidence in your business analytics skills and would like to take on new challenges, you should pursue intermediate business analytics projects. These mid-level projects will open you up to new horizons in business analytics. They can also help in landing a well-paying job position in tech or other fields. 

Project Management and Business Analysis

• Business Analytics Skills Practiced: Machine Learning

You can get plenty of ideas by reading this paper on project management and business analytics. The paper covers lessons from A Guide to the Project Management Body of Knowledge and A Guide to the Business Analysis Body of Knowledge . The latter provides a comprehensive guide in the elicitation process, also referred to as enterprise analysis. 

The above-mentioned books are instrumental to improving your project management skills and informing you on best practices in the industry. You will start with identifying the organizational problem and finish by defining systems capabilities. 

Human Resources

• Business Analytics Skills Practiced: Data Visualization 

This project involves automating processes, multidimensional analysis, self-service access, and recruitment methods. Your goal is to find ways to improve recruitment and retainment for a company while remaining within a set budget. This project will help you develop analytical skills in establishing the sensitive areas of a business that can lead to potential losses.

Business Analytics Capstone

• Business Analytics Skills Practiced: Data Visualization

If you have a computer science or business degree, you should consider working on this capstone project. This project-based course will help you learn real-world applications for data-driven decision making. By working on this project, you will familiarize yourself with using data to optimize businesses, maximize value, and make operations efficient. 

This capstone project will take business professionals through challenges faced by global companies such as Yahoo and Google. You will learn how to use data for addressing business challenges. It’s a curated project by Yahoo to help you master how to make data-driven decisions after complete evaluation. 

Sales Conversion Optimization

• Business Analytics Skills Practiced: Machine Learning, Statistical Software, Statistical Programming

This is a great project to work on Return on Investment. Through this project, you will be able to develop campaign strategies that will positively impact business operations. You will also optimize the budget to be more impactful by utilizing methods such as email blasting and social media marketing.

Optical Character Recognition

• Business Analytics Skills Practiced: Machine Learning, Statistical Programming

You can choose to work on optical character recognition, which deals with converting text in images to typed text. You can find open-source project templates for creating optical character recognition software with Python and Swift. You can program an application that turns handwritten documents into typed ones. 

Advanced Business Analytics Project Ideas

These advanced business analytics project ideas can take your expertise to a professional level. These projects feature advanced concepts such as pattern matching, forecasting, sentiment analysis, graph analysis, and neural networks. Find out more about the business analytics project details and the skills you will gain below.

Credit Risk Classification Analysis

• Business Analytics Skills Practiced: Machine Learning, Statistical Programming 

In this project, you can choose to focus on a particular financial organization or simply generalize. However, the more specific, the easier it will be to analyze the credit risk. You will start by analyzing the historical data of the customer, financial information, and loan purpose. 

You should check on factors like age, gender, marital status, job type, and income in your project. This classification tool should inform the business on the best cause of action when issuing credit or loans. 

Sales Data Exploration and Reduction

This project will help inform business leaders on the best course of action when it comes to remaining profitable. You can take a deep dive into the project to make it advanced by including the products or services that will generate more value or a higher ROI. You can also add customer segmentation to the project to help the leaders identify the target audience. 

Music Sales in America

• Business Analytics Skills Practiced: Data Visualization, Machine Learning

This project involves assessing factors in music sales like genre, popular artists, and sales distribution. The project will require you to work with Tableau for data visualization. By the end of the project, you will be familiar with top musicians, data mining, data visualizations, and machine learning concepts. 

University Fundraising

• Business Analytics Skills Practiced: Statistical Programming, Data Visualization

To complete this project, you will need to include the degree that attracts the most funds, gift donors, and pledge deals. It is best to present this data individually in Excel or a similar tool. Your project should display your ability to conduct in-depth research, data analysis, data visualization, and statistical programming. 

Exploring Aircraft Hardware Suppliers

• Business Analytics Skills Practiced: Machine Learning, Data Analysis, Statistical Programming

This is an excellent advanced project idea in business analytics that tackles the demand and supply of aircraft hardware. In the project, you will be expected to create a menu, explore orders, minimum purchases, forms of payment, and customer preferences. To make it more complex, you can also feature the shopping time. 

Business Analytics Starter Project Templates

To complete the named business analytics projects, you do not need to start from scratch. There are exceptional template samples that can help you work on your projects seamlessly. Find a list of business analytics starter project samples below. 

• Business Analyst Template Toolkit . Whether you are a beginner or a seasoned business analyst, this template toolkit provides templates to address your needs. You will find about 12 sample templates, work samples, and guidebooks. Each of these templates can be customized to fit your needs. 
• Software Requirements Documentation Template . This template features business requirements, rules, reports, user interfaces, and data requirements. It also comes with the process flows, use cases, service level agreements, business continuities, and data security plans. 
• Attribute Metadata Template . Data features entities and attributes. Entities are identifiable classes of people or things, and attributes are characteristics that give further descriptions. You can rely on this template for the names, attributes, data types, values, and definitions of entities. You can also add extra segments like risk, priority, complexity, stability, and status. 
• Business Analysis Plan Template . This template will help you develop a reliable business analysis plan. Through this template, you can document your business planning activities regarding the project.
• Templates for Business Analysts . Tech Canvas features several business analytics templates to provide a solid structure to use in your organization. For example, they feature a strategy analysis template, solicitation and collaboration template, requirement analysis template, and Pareto analysis template. 

Next Steps: Start Organizing Your Business Analytics Portfolio

A well-curated portfolio might be what you need to get to the next level in your career. After you have amassed solid real-world skills from the business analytics projects, you need to know how to present them for job applications. The tips we list below will guide you to designing a winning resume.

Pinpoint Your Achievements 

Use your portfolio for marketing your skills and experience. Always try to capture the recruiter’s attention from the onset by displaying your best work. Often hiring managers receive hundreds of applications, so it’s important for you to highlight your achievements to showcase your skills. 

"Career Karma entered my life when I needed it most and quickly helped me match with a bootcamp. Two months after graduating, I found my dream job that aligned with my values and goals in life!"

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Key in Relevant Information 

Align your portfolio to the job requirements and description. Use these job sections to guide you in adding relevant information to your portfolio. You will gauge the skills and experience needed, which will help you curate the best-suited portfolio. 

Make It Simple 

You must present a straightforward portfolio. Your portfolio should have concise documents which are organized. Keep it updated so that it can be easier for the employer to track your progress over the years. 

Business Analytics Projects FAQ

Yes, a well-curated business analytics portfolio can lead to a well-paying career. As a professional, consider aligning your documents according to the job requirements. This will significantly increase your chances of employment. 

No, you do not need to learn how to code to complete a business analytics project. However, having basic knowledge of software programming can be highly beneficial. You will have a broad understanding of the technical side of the business.

There are four different types of business analytics. There are descriptive, diagnostic, prescriptive, and predictive.

No, business analytics projects are not difficult to complete. As long as you have the motivation and experience to complete a project, you will be able to see it through. Ensure you have a strict schedule to help you remain consistent. 

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Top 13 Tips For Conducting Successful BI Projects With Examples & Templates

Table of Contents

1) What Is A BI Project?

2) Why Do You Need A BI Project?

3) Tips To Create A BI Project

4) Real-Life BI Projects Examples

BI projects aren’t just for the big fish in the sea anymore; the technology has developed rapidly, and the software has become more accessible while business intelligence and analytics projects are implemented in various industries regularly, no matter the shape and size, small businesses or large enterprises. With the assistance of an online data analysis tool, these kinds of projects have become easy to manage and agile in performance.

But sometimes, they can also be tricky: it’s not just pushing one button and expecting your business intelligence to fly over a rainbow. To truly harness the power of a successful BI project, companies must develop a solid plan of action and in this post, we will provide the top tips for developing and executing analytics and BI projects with the help of BI tools, followed by business intelligence examples from different industries. Ultimately, this will bring a solid level of understanding and indispensable potential that business users could implement in their own working environment. From Fortune 100 companies to small business owners, BI tools and technology are becoming the standard to oversee historical, present, and future data of business operations. But what makes these projects successful and what to look out for? We will cover this question, and much more, but first, let’s start with a bit of background.

What Is A BI Project?

A BI (business intelligence) project is a term used to describe the planning, assessment, development, and implementation of business intelligence in a company, mainly BI tools that will help managers to solve business problems and derive actionable insights.

These projects require cooperation between various companies’ processes, technology objectives, and data while contributing to set business goals, usually defined by a detailed business intelligence strategy.

Why Do You Need A Modern Business Intelligence Project Plan?

Now that you know what a business intelligence project is, let’s consider why you need one for your organization.

Business analytics projects are key to sustainable commercial success as they help you gain a detailed and objective view of specific departmental activities or behaviors. Working through the necessary steps to create cohesive business intelligence projects will ensure your best data offers you the most powerful insights for the task at hand.

If you’re looking to improve your order fulfillment times, for example, developing a solid intelligence plan will give you the tools as well as the strategy to break down key aspects of your supply chain as well as delivery strategies.

Setting your goals and working with the right tools will empower you to understand exactly where any inefficiencies lie in your fulfillment activities while understanding your strengths. Having all of this information in one place will enable you to make beneficial decisions quickly while carrying out activities like switching to better suppliers, improving your picking and packing initiatives, and streamlining delivery routes.

This is a concept that works for every internal department and strategy imaginable. By implementing the right BI-based project, you will become more:

• Communicative

As you can see, investing in business intelligence projects will enhance your business in every conceivable way. You will gain the insight and the confidence required to ultimately push your company ahead of the pack and remain in the front seat.

Top Tips To Create A Modern BI & Analytics Project

To get started on this journey and ensure maximum value is generated in the long run, here are the top 13 tips to successfully generate a BI project.

1. Create a solid BI implementation project plan

It is of utmost importance to generate a compact BI project plan that you can refer to periodically and track your progress. When conducting business intelligence projects, the more information you gather at your starting point, the better control you will have during the process. That said, you need to translate strategies into specific operational statements and plans in order to answer all your data problems and improve efficiency. In practice, that could mean answering questions such as:

• How much time do we spend on reporting strategies?
• Do we need a tool that will be used by the whole company or specific departments? How will it affect it?
• Who will be responsible for the BI project implementation?
• What are the desired outcomes?

These are just some of the business intelligence project ideas that you can adjust based on your specific business requirements. In the next steps, you will go into more detail but the foundation needs to be well-structured in order to avoid potential bottlenecks. As mentioned, gathering as much information as possible beforehand will ensure that you have a steady BI project management flow and anticipate potential pitfalls. It would also make sense to keep in mind that outcomes line up with reality and that you have enough agile BI resources to adjust the process where needed. While you can't predict the future, you would need to know how to adapt.

2. Define goals and objectives

In correlation with the planning strategies, defining your endgame, and setting the right KPIs will lead to success. While there are numerous KPI examples you can choose from, only a few of them will help you answer specific business questions. If you work in finance, financial analytics will be the backbone of your operations. On the other hand, if you’re in the HR industry, then an HR dashboard could be the best answer you’re looking for. The essential element in this step is to be able to answer in what way your company or organization makes business decisions, and how the quality of these decisions is measured. Another useful piece of advice is to start small; you have to walk before you can run.

3. Consult with key stakeholders

It's critical to involve stakeholders since you need to identify the specific needs and wants, and how they will use the data to generate actionable insights. A thorough analysis of each department's stakeholders can save you a lot of time in future strategies and make or break success in your projects in business intelligence.

Involve relevant stakeholders and answer questions such as who will work with the BI? Is it intended for analysts, C-level executives, or department managers? You can also conduct interviews and ask each relevant person directly to avoid communication issues between departments after the project and online BI tools are already implemented. This kind of structure will ensure a proper foundation so later you won’t have to face pitfalls and misinterpreted information.

4. Keep in mind your team and budget

Even in traditional projects, getting the right team on board can be quite challenging. Technical expertise is not enough, each member needs to provide industry-specific knowledge, therefore, give yourself some extra time in order to identify quality candidates and ensure your BI implementation project plan ticks the right boxes.

Document the responsibility and resources needed, plan the engagement of the stakeholders and bring specialists on board such as data analysts, business analysts, subject matter experts, or ETL developers, depending on the size and scope of the project.

Project roles and responsibilities will differ depending on the company, but here is the rundown of some business intelligence project tips to keep in mind:

• Identify the project budget before implementing BI solutions
• Determine the project resources and staffing requirements
• Include the specific responsibilities of each team member

5. Clear the clutter and define a timeframe

After you have established your plan and defined goals, you need to clear all the information clutter and define a timeframe. To be able to fully reap the rewards that an analytics project and BI can deliver to your organization, it is not just significant to own the KPI management process. By now, you should have already identified business questions you need to answer, and it’s time to get your hands dirty. As the old saying goes, timing is everything , so make sure you develop a schedule for implementing and approving all the relevant processes. Do you need one month or six months to finish the project and start using the BI tool? If you’re confronting setbacks, it might be useful to engage with additional business intelligence consulting to be on the safe side.

6. Step back from the computer

Once you've done your research, identified critical business questions, gathered a team, and defined your timeframe, it's essential to start ideating your BI dashboards .

To get a clearer picture of a project in BI, you need to implement dashboards as a critical component that needs consideration and thoughtful development. Stepping back from the computer and drafting essential dashboard elements should be one of the business intelligence project steps that will give you a wider perspective and mockup for modeling the dashboard based on project scope and needs.

Additionally, it might make sense to create a project management dashboard in order to keep your future monitoring processes at hand and up-to-date, but in this step, it's important to draft and visualize elements that you want to implement. That way, you will have a clearer understanding of what to expect and how to deal with the technicalities, tools you will be using, and people you need to communicate with. We can't stress enough how preparation is key and if you do it right and detailed today, you will reap great rewards tomorrow.

7. Define your data sources and gather the data

 Once you’ve taken a step back and refreshed your faculties, you’ll be ready to jump back in and continue with your BI project plan.

With a refreshed mindset, you can start to define the most valuable data sources for your business intelligence project. Consult your core business analysis project goals and the data sources you need to work with will become apparent.

To gain a balanced view of your organization’s performance, it’s important to look at a variety of sources, from consumer behavior and customer service to logistics, fulfillment, and beyond. Once you’ve clearly defined your relevant data sources, you should set about gathering the insights that will ultimately ensure you gain the most intelligence.

At this point, it's worth mentioning: you must be objective, and work with every relevant business intelligence project plan stakeholder. Be ruthless, explore your shortlisted data sources as a team, and cut away any informational outlets that you don’t believe to be 100% trustworthy. When it comes to the success of any BI project, it’s vital to lead with value and cut through the noise—which brings us to our next point.

8. Clean your data and assess the quality

Armed with your shortlisted selection of data sources, you will now need to drill down into each informational pocket and start your cleaning process. There will be droves of insights in every one of your sources that won’t offer any direct value to your business intelligence project report. There will also be information that could significantly throw your efforts off course.

Proceed with caution and look at every insight with a fine tooth comb. Much like you did with your data sources selection efforts, be as objective and ruthless as possible, trimming the informational fat to get to the heart of the issue.

Yes, the task of cleansing your data and omitting anything you consider inaccurate, misleading or redundant will give you a clear path toward business analytics project success. Why? Well, because once you’ve consolidated your best data from the most reliable sources, you will have all of the informational tools you need to make powerful strategic decisions for your organization. This will form solid foundations for all of your BI projects

Working with the best quality insights will improve your decision-making, make you more adaptable, and ensure your BI projects result in sustainable growth.

9. Implement a data governance plan

The next in our rundown of business analytics project ideas is creating a water-tight data governance plan.

Essentially, a data governance plan is a framework of policies, processes, and strategies that outline how you handle your various data assets, including how you collect, curate, store, view, and present your insights.

The reason that a governance plan is so beneficial to your business intelligence initiatives is that it will enable you to squeeze every last drop of value from your most relevant data. Having a water-tight plan will also ensure that everyone in the organization is on the same page, significantly improving communication in the process. Implementing the right plan will also help you remain on the right side of compliance, which is essential in the Age of Information.

In addition to having clear-cut policies and strategies, working with the right BI tools and dashboards will also prove essential to the success of your plan, and in turn, your project. The best BI tools will empower you to consolidate your most precious insights into one central space while presenting them in a way that is accessible, engaging, and actionable across the board.

10. Concentrate on technicalities

At this stage, you have developed your plan, set the time frame, identified, and communicated with relevant stakeholders, gathered your data, and now you need to choose the right dashboard tool . Since every project initiative is different, it would be wise to establish a framework for what you need from a tool. What kind of database you’re currently working with and do you need various data connectors to unite all your flat files, databases, marketing analytics, social media, etc. Working with the right partner that can deliver all your requirements is an invaluable choice and you should be able to choose based on your budget and the scope of the project. Keep in mind that BI and analytics projects are business programs in their core. You will need the help of the IT department, but the “business first” perspective will make your life easier and focus on the big picture.

11. Implement your BI solution and measure success

Our next tip to develop a proper BI and analytics project focuses on the implementation and measuring the success of your initiative. It is often hard to evaluate and quantify the level of success of utilizing a BI solution, but a simple calculator as shown below can provide you with an idea of how much you can save each year:

To see the full scope of the calculation, you can visit our business reporting page.

12. Work on a support and training system

As we will see in our business analytics project examples below in the article, training is necessary to develop relevant training and support systems in order to familiarize each member of the team with new enterprise applications and technologies, if you happen to develop an enterprise BI project. As we mentioned, a BI implementation project plan needs to be well-researched and correctly analyzed, but support and training are equally important. This stage needs to be carefully addressed because, even if you happen to have the best team and managers on board, if they don't have proper training knowledge and technical support, the project won't be utilized fully and you could misinterpret the right amount of capacity needed that will lead to poor decision-making processes.

In this case, no matter if you utilize a healthcare BI software or compile a selection of data science tools for your data team, the important notion to keep in mind is that each person must know what they're doing and what kind of goals they are trying to reach. That way, you have a better chance that your business intelligence project implementation plan works seamlessly, efficiently, and with relevant support systems in place.  

13. Finally, communicate regularly

We have mentioned several times how it's important to interview relevant stakeholders, gather the best possible team, and plan your project on business analytics thoroughly. Between these steps, it's important to communicate regularly in order to diminish any doubts or miscommunication risks that could jeopardize your plan and implementation process. Asking for regular feedback at the beginning, in between, and at the end is important as much as choosing the right tool for the job. Regular meetings, answering questions, or simply asking for help when developing digital dashboards , are essential in order to achieve success.

Besides, empowering users by letting them express their opinions in the planning and development of the project will ensure a healthy communication exchange, critical in succeeding in both business intelligence mini-projects as well as bigger ones.

To summarize, your business intelligence and analytics projects need detailed planning, the best tools that correspond to your business (and project) scope, and clear strategic and operational communication within the team and with stakeholders.

We have answered the question of what is a BI project, provided a roadmap of tips you need to follow in order to successfully implement such initiatives, and now we will focus on real-life business intelligence projects examples and templates that made companies’ processes more productive, saved costs, and increased efficiency.

Real-Life BI Projects Examples And Templates

Here are shining demonstrations of real-life business scenarios in which a BI and analytics project is used to improve efficiency, and productivity, and enable smarter decision-making processes in their operational and strategic efforts.

1. US-based financial services provider

Requirements:

• Real-time access to vast amounts of data
• Fast implementation
• Availability to all managers
• Maximum security and data privacy
• Reducing the reporting time

Challenges:

• Reducing IT involvement
• Decentralizing the decision-making processes from one person to 10

Facing the challenges of poor data quality, dispersed through a number of spreadsheets and databases, this financial company was unable to track financial data in real time and generate valuable insights needed to ensure their vendor payment, managed by the accounts payable department, is accurate and fast. They have already experienced a few business disputes and wanted to avoid such scenarios in the future. Additionally, they wanted more control over their working capital and the cash conversion cycle data in order to increase management productivity and operations.

After deciding to implement a business analytics project with the help of a data dashboard , their efficiency skyrocketed. We can also see below a visual business intelligence project template that can be used in any finance department or company:

**click to enlarge**

The final result was reducing the time of comprehensive financial reporting processes, automating calculations, and gaining access to data in a single, central location. A testament to the supremacy of using a financial dashboard to enhance internal performance.

2. Human resource department in a corporate setting

• Improving recruitment methods
• Self-service access to information
• Budget-friendly
• Multidimensional analysis
• Automating processes
• A comprehensive view of the entire recruitment process
• The performance of the team should be tracked on a weekly basis
• Providing a foundation for weekly meetings

This is one of our business intelligence projects samples that expound on the HR level in a corporate setting in the US. The company struggled with its recruitment funnel and didn’t have up-to-date information on the costs, turnover rates, and top-performing agents that can share their knowledge and educate the rest of the team. The final BI project template looked similar to this visual:

The manager gained a clear, birds-eye view of the department’s performance and crucial HR KPIs that provide instant insights through the employment of a powerful BI solution. Their reporting process was time-consuming and employees were facing challenges with weekly meetings when they needed to provide accurate data and deliver fast responses. By utilizing a comprehensive HR dashboard, every stakeholder had an interactive visual that they could access at any time, from any device, and decreased the time needed to generate HR reports. The automation of the reporting process enabled more efficient time management which employees could use to perform other relevant HR tasks.

Another testament to the power of using HR analytics tools .

3. Sales department distributed over multiple continents

Requirements :

• Consolidating data across 3 continents
• Real-time access to information
• Scalable infrastructure based on the company's growth
• Easy and fast integration with Salesforce
• Data is spread across multiple sources
• Combining data to develop a live dashboard
• Overview of multiple sales touchpoints and entire sales funnel

Another business analytics project example comes from a disparate sales department that needed a centralized point of access for their sales opportunity management as well as the possibility to drill down into each sales chart when questions arise. Before creating a BI project plan template, the team used traditional means of managing massive volumes of data such as static spreadsheets and PowerPoint presentations. Issues arose when the company grew and the team became disparate - manual work cost them countless hours and affected the quality of managing the most promising leads in the funnel.

The sales dashboard was developed with all the requirements in mind, and more. The regional and country managers have gained the possibility to drill into details of important metrics such as the number of current opportunities, purchase value, lost opportunities, or churn reasons. The details of the latest opportunities enable managers to examine the status of opportunities by sales reps, country, and company, and see if any comments were made by the team.

This is one of the BI project examples that was implemented fast and efficiently although the provider had to consolidate data from multiple continents. Finally, the possibility to connect a Salesforce dashboard and manage data from this popular CRM solution enabled the team to create a more productive working environment and analyze data no matter the hour or location.

As you can see, managing business intelligence software projects doesn't have to be complicated or demanding. With the right preparation, tools, and team, companies can now create BI project templates and adjust each departmental requirement separately while staying on budget and using the knowledge for future projects. Our business intelligence project plan samples show exactly how and you can use it as a roadmap for building your own BI success.

4. Corporate IT department with security concerns

• To gain a firm grip on the frequency of cyber security threats
• To create an enhanced phishing detection strategy
• To drive down cyber threat detection times

Challenges :

• Understanding key sources of cyber threats or attacks
• Gaining a clear and unified view of the business’s cyber security prevention abilities

Businesses across sectors lose millions of dollars a year at the hands of cybercrime. Without a clear-cut strategy and a means of accurately monitoring your cybersecurity status, you are essentially leaving your virtual door wide open to potentially devastating attacks. In this particular insurance, a corporate team was looking to fortify its cybersecurity strategy while gaining a clear, consolidated view of any related activity. 

With information coming from various sources, the team discovered their efforts were completely fragmented, preventing them from working towards a unified strategy. By leveraging the power of this security-focused business analytics project example, the team swiftly got to grips with its strategy.

Armed with highly-visual IT KPIs based on intrusion attempts across malware types, phishing test success rates, and detection as well as resolution times, the team was able to pull in the right direction.

Drilling down into patterns and trends over specific timeframes, the team was able to use IT analytics techniques to find viable ways of driving down detection times while becoming more responsive to any potential threat at the moment. As a result, the team’s efforts became largely preventative rather than having to tackle attacks that were already in action. As a result, the organization drove down the threat of cybercrime significantly while maintaining a scalable security strategy.

5. Multi-channel customer service team looking to improve their service quality

• To drive down customer service response and resolution times
• To improve communication across the department
• To provide a swifter, more personable level of service across channels
• Data fragmentation slowing down progress
• Communication silos

Customer service is one of the biggest drivers of success in our hyper-connected digital age. Now, the consumer is well and truly in the driving seat and as such, will remain loyal to brands that can meet their needs across channels. In one of our most vital business analytics projects examples, a scaling customer service team realized the necessity of improving their service efforts across every customer-facing channel.

To break down informational silos and improve multi-channel communication, the team embarked on a BI project, consolidating their efforts into a customer service dashboard . Working with its most valuable service data, the team gained a panoramic view of its activities.

Visual KPIs including abandonment rates across live chat and phone as well as a breakdown of costs and resolution success rates according to channel empowered everyone within the department to work as a unified unit.

As a result, the department was able to distribute its agents as well as its resources to handle queries, complaints, and requests across every customer-facing channel with ease. Service rates increased while unnecessary costs dwindled.

It’s Your Turn!

To summarize, here are the top tips for creating a successful BI project:

• Create a solid BI project plan
• Define goals and objectives
• Consult with key stakeholders
• Consider team and budget
• Define a clear timeframe
• Step back from the computer
• Define your data sources
• Clean your data and assess the quality
• Implement a data governance plan
• Concentrate on technicalities
• Implement your BI solution and measure success
• Work on a support and training system
• Communicate regularly

There’s no doubt about it: prioritizing BI will offer you a host of business-boosting rewards. Looking at our business intelligence projects examples alone, it’s clear to see that taking the right measures and working with interactive visual tools will consistently help you meet or even exceed your goals.

Rather than merely mulling over data and cherry-picking the odd piece of useful information, developing a clearcut project will give your efforts steer while bringing your data to life in a way that results in genuine progress.

Now that we have provided BI projects examples and templates that professionals and managers can use for their own purposes. Creating business analytics projects through self service BI and by following our tips and leveraging these samples to your advantage can create a much more stable business and better performance level. To see it in practice, you can start creating your own projects with our BI tool, for a 14-day trial , completely free!

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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• Business Management

Top 20 Business Analytics Project in 2024 [With Source Code]

Home Blog Business Management Top 20 Business Analytics Project in 2024 [With Source Code]

As a beginner in business management, one of the most crucial skills is gathering and analyzing data to make informed decisions. Business analytics uses data and statistical methods to extract insights and make data-driven decisions. The good news is that there are countless business analytics project ideas that you can start working on to improve your skills and help your business thrive. This blog will explore the top 10 business analytics projects you can do online as a beginner or an experienced professional. So, let’s dive in and discover how you can use business analytics projects to gain a competitive advantage in today’s fast-paced business world.

Why are Business Analytics Projects Important?

Business analytics is an amalgamation of business management and data analytics. High-value projects aimed at business development add value to the profile or resume of candidates who opt for a business analytics career. Business analytics projects are important because they enable data-driven decision-making, helping businesses uncover valuable insights from their data. These projects optimize operations, identify growth opportunities, and enhance overall efficiency, leading to improved profitability and competitiveness. Moreover, they provide a foundation for predictive and prescriptive analytics, enabling organizations to proactively address challenges and capitalize on emerging trends.

Top 10 Business Analytics Project Ideas

Here are the top 10 projects in business analytics, each offering unique insights and opportunities for data-driven decision-making in various industries. 

1. Sales Data Analysis 

It involves the analysis of data on every aspect of a company’s sales. It determines the total number of sales, average monthly sales, demographics of customers, and patterns of selling periods. It allows the company to make informed decisions to prioritize the production of specific products and scale them. To analyze the sales data, students can use different tools and languages. Students can use SQL to extract data from the database. Excel or Google Sheets can clean and analyze data for charts and graphs. For advanced visualizations and dashboards, Tableau or Power BI can be used. Python or R is good for advanced data analysis and statistical modeling, like looking for trends or making predictions.

Sales Analysis Source Code  

Dataset  

2. Customer Review Sentiment Analysis

It is the process of determining the emotional state of customers after they purchase or use the products. It allows the company to realize the possible reasons for customer complaints and measures to improve the features and quality. Students can use Python or R for data analysis. Tools like TextBlob and NLTK for sentiment analysis.

Reviews Sentiment Source Code

3. Market Basket Analysis 

It involves the analysis of the correlation between the ales of different products when combined. It helps improve the business by identifying the best combinations and increasing the preferences of customers for the products. For this project, students can analyze data using the Apriori algorithm. They can use either Python or R programming languages.

Market Basket Analysis Source Code

4. Price Optimization 

It involves investigating historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc. Programming Languages like Python or R are suitable for this project. Regression analysis and demand forecasting models are used to analyze the data.

Tensor House Source Code

5. Stock Market Data Analysis

The project involves determining the frequency of rise and fall in price, the general trend of average monthly closing prices over the year, and trading volumes. Candidates can select a specific dataset and explore the company’s stock performance history. To analyze the data for this project, Python and R is used. Tools like Pandas and Numpy are used for manipulating the data.

Stock Market and Analysis Source Code

6. Customer Segmentation

It refers to categorizing a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. It helps optimize marketing campaigns and maximize the profits from each client. The K-means and Hierarchical clustering algorithms are generally used for this project.

Customer Segmentation Source Code

7. Fraud Detection

Credit card fraud, identity theft, and cyber-attack are common fraudster challenges faced across various industries. Projects on fraud detection involve choosing a dataset and running statistical analyses to identify fraudulent operations. Machine learning algorithms such as decision trees and logistic regression are used for fraud detection.

Fraud Detection Source Code  

8. Equity Research

Equity is the value of the returns received by a company’s shareholders after liquidating all the assets and clearance debts incurred by the company. Equity research plays a crucial role in the successful run of both shareholders and companies. Students can use Excel and Python to analyze the financial datasets for this project. Tools such as ratio analysis and financial statement analysis are in equity research.

Equity Research Source Code

9. Social Media Reputation Monitoring

It is the process of gauging the presence and influence of a brand on customers through social media. Using analytical tools and techniques, the project audits, monitors, and interprets social media users’ opinions about the products. It helps revise social media marketing strategies to promote the business. Social media monitoring tools such as Hootsuite and Sprout Social are used to analyze the data.

10. Real-Time Pollution Analysis

It is a typical data visualization project, allowing the candidates to learn univariate and multivariate data analysis. The methodology can be reproducible to business aspects. Students can use either Python or R to build the project. Matplotlib or Plotly are used for creating visualizations.

Air Pollution Tracker Source Code

List of Business Analytics Projects [Based on Levels]

Here is a list of business analytics projects based on levels of experience:

Business Analytics Projects for Beginners

Graduates from several fields, including engineering, with an inclination for business, choose management as their career path. Business Management for beginners , augmented with business analytics projects, provide potential platforms to lay a strong foundation to build their career. The following are the most-edifying sample business analytics projects for students.

1. Employee Attrition and Performance

These projects are ideal for acquiring the qualitative analysis skills of employee attrition to find answers for the event’s who, when, and why. They also predict quantitative aspects of human resource dynamics for the organization’s next 5 to 10 years. The balance between attrition and retention is the secret to optimal human resources and talent utilization. To do this, students can use Excel to clean the data. SQL is used for data extraction. Python or R for data analysis.

Employee Attrition Performance Source Code  

2. Prediction of Sales in Tourism for the Next Five Years

This project helps business analysts to improve their skills in applying data mining to determine patterns and correlations among tourism packages and their preferences. It has two approaches: qualitative and quantitative. Both approaches help beginners to hone their analytical and judgmental skills. To predict sales, statistical analysis tools like R or Python are used. Excel and SQL are used for cleaning and extracting data, respectively.

3. Prediction of the Success of an Upcoming Movie 

Business management professionals have a good scope in the film industry as numerous films enter the screen. These projects involve forecasting success based on the analysis of variables, including genre, language, directors, actors, actresses, budget, locations, etc. The prediction depends on the model devised based on the data of predetermined variables associated with previously released movies against their success. Like the other projects, students can use Python or R to predict the success of the upcoming movie.

4. Prediction of the Fate of a Loan Application 

These projects expose beginners to several machine-learning tools and techniques, and datasets. They also introduce the candidates to various parameters and help them gain the ability to recognize variables under eccentric circumstances. The top 3 machine-learning solution approaches for loan prediction are as follows.

• Support vector machine 
• Random forest

Pandas are the most straightforward and powerful Python libraries for beginners used for the prediction of the fate of loan applications.

Business Analytics Project Ideas for MBA Students

ECBA certificate training is among the best options to improve the profile of business analytics aspirants. A merit of this program is the opportunities for business analytics projects for MBA students. Three top business analytics project ideas are as follows.

1. Predicting Customer Churn Rate

It involves predicting the decline of customer rates. It has scope for stakeholders to identify setbacks in the business. It helps learn several statistical tools, such as SHAP (Shapley Additive exPlanations), RandomSearch, and GridSearch, for univariate and multivariate analysis on a retrieved dataset.

Customer Churn Analysis Source Code

2. Prediction of Selling Prices for Different Products

It refers to the determination of the price of a product that attracts customers with an optimal profit margin. Further, it also helps companies to determine the offers to improve business. These projects help acquire skills to employ machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks that use different metrics to test each of their performances.

3. Store Sales Prediction

These projects involve working with numeric and categorical feature variables and performing univariate & bivariate analysis to find the redundancy in variables associated with the store chain of a company. They help the candidates learn machine learning models such as the ARIMA time series model. 

Store Item Demand Forecasting Source Code

Business Analytics Project Topics for Intermediate

Business analytics project ideas for experienced professionals should involve a complex combination of statistical parameters and real-world scenarios to enhance their skills significantly. Following are the business analytics project examples suitable for the intermediate levels.

1. Creating Product Bundles

It is a method that combines different products from the same company and sells them as a single unit. Under these projects, candidates learn market basket analysis and time series clustering methods to identify product bundles using sales data.

Product Bundle Source Code  

2. Life Expectancy Analysis

These projects aim to determine the monetary value of the potential consumer of the products and services of a company. Traditionally, government organizations utilize life expectancy analysis to determine the correlation between life expectancy and a nation’s GDP.

Life Expectancy Analysis Source Code  

3. Building a BI app 

Business intelligence apps or tools play a critical role in finding urgent solutions to issues that are high for the business. Low to no-code custom apps for decision-making and long-term strategies are invaluable for an organization.

Business Intelligence Analysis Source Code  

Are Business Analytics Projects Difficult to Complete?

Business analytic projects face several challenges that hamper their successful implementation. Technological advancement expands the options for tools and techniques. Still, they create a grey zone wherein the new tools emerge with overlapping functionalities interfering with decision-making. Other reasons for the failure of business analytics projects are: 

• Lack of well-defined and explicit goals 
• Poor data integration 
• Lack of conversion of insights and outcomes into actions.
• Poor adaptations to the ongoing development

Final Thoughts

Business analytics is blooming parallel to technological advancements, and every business is leveraging analytical tools and techniques to optimize its actions. Whether experienced or fresher, diverse business analyst projects for resume help you upgrade your profile. KnowledgeHut Business Management for beginners is highly recommendable for a firm foundation before undertaking business analytics projects, as it provides top-quality augmentation to your aptitude for the discipline.

Frequently Asked Questions (FAQs)

The common challenges faced in business analytics projects are: 

• Changing requirements or business needs 
• Conflicts with stakeholders 
• Poorly documented processes 
• Unrealistic timelines. 

Predictive analytics is a branch of analytics that predicts future outcomes using models based on historical data. Businesses use customer data and transaction information to predict the performance of the products and make strategies to optimize profits. 

Popular business analytics tools are SAS business analytics, Sisense, Microstrategy, KNIMETIBCO Spotfire, Tableau big data analytics, Power BI, and Excel.

Mansoor Mohammed

Mansoor Mohammed is a dynamic and energetic Enterprise Agile Coach, P3M & PMO Consultant, Trainer, Mentor, and Practitioner with over 20 years of experience in Strategy Execution and Business Agility. With a background in Avionics, Financial Services, Banking, Telecommunications, Retail, and Digital, Mansoor has led global infrastructure and software development teams, launched innovative products, and enabled Organizational Change Management. As a results-driven leader, he excels in collaborating, adapting, and driving partnerships with stakeholders at all levels. With expertise in Change Management, Transformation, Lean, Agile, and Organizational Design, Mansoor is passionate about aligning strategic goals and delivering creative solutions for successful business outcomes. Connect with him to explore change, Agile Governance, implementation delivery, and the future of work.

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The Secrets to Managing Business Analytics Projects

Smart use of information technology can allow for frequent and faster iterations between the design and operating environments, improving experimentation efficiency.

Image courtesy of Flickr user BotheredByBees .

Managers have used business analytics to inform their decision making for years. Numerous studies have pointed to its growing importance, not only in analyzing past performance but also in identifying opportunities to improve future performance. 1 As business environments become more complex and competitive, managers need to be able to detect or, even better, predict trends and respond to them early. 2 Companies are giving business analytics increasingly high priority in hopes of gaining an edge on their competitors. Few companies would yet qualify as being what management innovation and strategy expert Thomas H. Davenport has dubbed “analytic competitors,” but more and more businesses are moving in that direction. 3

Against this backdrop, we set out to examine what characterizes the most experienced project managers involved in business analytics projects. Which best practices do they employ, and how would they advise their less experienced peers? Our goal was to fill in gaps in management’s understanding of how project managers involved in analytics projects can contribute to the new intelligent enterprise. (See “About the Research.”) We found that project managers’ most important qualities can be sorted into five areas: (1) having a delivery orientation and a bias toward execution, (2) seeing value in use and value of learning, (3) working to gain commitment, (4) relying on intelligent experimentation and (5) promoting smart use of information technology.

About the Research

This paper assesses issues that were top of mind for experienced project managers involved in business analytics projects, which best practices they used and what advice they had for less experienced peers. We set out to find common denominators and to describe trends relevant to experienced project managers.

We use the term “project management” broadly to refer to a disciplined way of improving a result, product or service, subject to constraints of time and cost. The idea was to highlight the discipline labeled “best practice” by experienced project managers. For “business analytics” we rely on the definition of Thomas H. Davenport and Jeanne G. Harris in “Competing on Analytics: The New Science of Winning” (Harvard Business Press, 2007). They define it as the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive decisions and actions.

The findings presented here are based on data gathered in 32 in-depth interviews during 2010 and 2011, conducted in person and lasting one to two hours. The interviews were conducted on the basis of a semistructured questionnaire with open-ended questions. Additional information was obtained by probing the initial responses. All interviews were taped with permission of the interviewees and fully transcribed for further qualitative analysis.

We restricted our sample of interviewees to experienced project managers who had (1) a minimum work experience of 10 years and (2) at least five years of experience in managing analytics projects. We recruited interviewees through the method of snowball sampling: An initial set of eligible project managers referred us to other project managers whom they considered highly competent. The average work experience of our sample was 16.6 years (median = 15, min. = 5, max. = 35), with an average experience of 11.1 years on analytics projects (median = 10, min. = 1.5, max. = 20).

Among the interviewees were both internal and consulting project managers. They were active in a wide range of sectors, including financial services, insurance, manufacturing, transportation, leisure, retail, media, telecommunications and government. One in three project managers interviewed had been, or was still, active as a business analyst.

Beyond the interviews, the paper draws on the findings of six years of dedicated research in the field of business intelligence, undertaken by the Business Intelligence Research Centre at Vlerick Leuven Gent Management School, sponsored since 2008 by SAS Institute and Enqio. The complete report is titled “Business Analytics Project Managers: What Defines Them?”

1. Having a Delivery Orientation and a Bias Toward Execution

As a starting point, it’s important to understand what makes experienced business analytics project managers tick. The vast majority of our interviewees do not consider themselves different from other project managers. Like other focused project managers, they want to deliver their projects on time and on budget, and they have a strong delivery orientation.

But unlike many traditional project managers, they do not have a plan bias. Instead, they have a strong bias toward execution. (See “Learning From Experience.”) Although our interviewees don’t question the importance of initial planning, their focus is on project execution and delivery as opposed to adherence to the plan. In fact, they start with the assumption that the initial plan will have to change as the project progresses. This is what we mean by “a bias toward execution.”

Learning from Experience

Certain practices can contribute to success. Starting project managers may want to take the following recommendations to heart.

Plan, but plan for change. Start with the premise that the initial plan will have to change as the project progresses, and focus on project execution and the delivery of value, rather than on adhering to the plan.

Adapt project execution to the nature of the project. Iterative development encourages and enables a culture of learning and will help to optimize value in use.

Gain commitment. Engage stakeholders as early as possible and throughout the project, set the right expectations from the start and manage them throughout. Avoid implementing “black boxes.”

Experiment whenever possible. Experimentation is fundamental to the learning process. When experimenting, adopt a pragmatic approach — that is, apply sufficient scientific rigor to each step without losing focus on practical relevance, usability and usefulness.

Work with the situation, not against it. Remember that in most organizations, business analytics is still relatively new. Instead of forcing an approach and cutting out the learning curve, try coaching the stakeholders into cultural change.

Work in partnerships. Business analytics projects are business projects, but look for ways to involve IT. Projects conducted as partnerships yield better results.

Why do analytics project managers have this execution bias? Many say it is because of the inherent complexity of the projects themselves, and they cite three reasons. First, analytics projects are typically characterized by uncertain or changing requirements. Project sponsors and users will often have a vision of what they seek to accomplish with analytics — for example, to improve direct marketing response, reduce inventory or increase service quality and customer satisfaction while controlling costs. But how they will achieve those goals is often unclear and involves further exploration.

The Leading Question

How do experienced business analytics project managers approach their projects?

• They start with the assumption that the initial plan will have to change as the project progresses.
• They enable a process of engaging stakeholders, explaining and managing expectations.
• They rely on intelligent experimentation.

Second, the technology or models for meeting the uncertain requirements are often not known; they may be new to the team, or they may not even exist. This adds to the exploratory nature of analytics projects. Third, users of business analytics applications expect responsiveness, so the applications, by nature, should be highly responsive to user interaction. The challenge, then, is to find a balance between responsiveness and robustness.

Traditional project management methods tend to focus primarily on planning or a priori risk management (as opposed to managing and mitigating risk during execution). However, the uncertainty associated with analytics projects calls for a different approach. 4 A growing body of literature on project management emphasizes the importance of adapting management and processes to the project characteristics. So while there may be a set of general-purpose tools for managing projects, different projects call for different managerial approaches. On the one hand, production-oriented and specifications-based approaches emphasize detailed early planning and requirements specification with minimal ongoing change and exploration. On the other, experimentation-based approaches emphasize less-specific early planning, good-enough requirements, and experimental and evolutionary design with significant ongoing learning and change. 5 The latter, more adaptive approach, interviewees say, is better suited to analytics projects.

2. Seeing Value in Use and Value of Learning

There is increasing awareness in the project management community that sticking to the original plan does not necessarily provide value. Instead, value comes from a focus on execution and delivery. Experienced analytics project managers say they approach return on investment as a process rather than as a control metric. By focusing on execution, they seek to add value throughout the project’s life cycle, not just at the end of the project.

Our interviewees are guided by the concept of “value in use,” which measures value in terms of how a given asset provides benefits to a specific owner under a specific use. 6 The idea is that the assets themselves have no inherent value; they generate value only when they offer specific benefits to their owners or users (for example, by allowing them to do their work differently). Consequently, only when an analytical model or application is actually used can its real benefits (and costs) be identified.

We found that project managers involved in analytics projects usually want to assess the value of the project quickly and accurately. Interviewees explained how they try to capture value both early in a project and throughout (for example, by using iterative feature-based delivery or rapid prototyping). Indeed, capturing value early and often can significantly improve a project’s ROI. For the assessment of value to be accurate, it needs to be carried out with a certain degree of rigor — which, as we have noted and will discuss later, is what our interviewees do.

Many project managers have learned through experience that they can’t expect to be right the first time. A bias toward execution is essential, interviewees report, because it is better to attempt to execute good ideas quickly than to attempt to impose the “perfect” plan. This implies that the focus is not on explaining discrepancies between the plan and actual results but on learning something new in the course of implementation that might justify altering the plan. Similarly, the iterative, incremental delivery described by interviewees assumes that each iteration provides learning inputs.

As a result, project benefits can be expressed in terms of “value in use” and “value of learning” that accrue during the project. Many analytics project managers have adopted project management approaches that tie in with the project management methods that are being developed to support highly complex projects. Adaptive methods assume there is a need to gather information and learn as you go along. These methods typically emphasize rapid delivery of prototypes and require that those involved be allowed to experiment during the project. 7

The success of an analytics project is a function of the user’s acceptance of the model or the application. Our data make a convincing case for the value of continuous exposure to user feedback. As a project manager at a European financial services group explained, “Ideally, analysts and users are physically in the same room, or in close proximity.” The design environment and the operating environment should be closely linked, with the analytics project managers facilitating continuous interaction between them.

3. Working to Gain Commitment

Experienced project managers are unequivocal about the importance of engaging business users and other stakeholders as much as possible, as opposed to merely informing them after the fact. As an enterprise-business-intelligence architect at an international transport solutions provider put it: “We don’t want to develop a model just like that. If the business processes aren’t aligned with the model, or if the business doesn’t understand the definitions used in the model, then it simply won’t be used.”

The importance of explaining or clarifying the thinking behind a decision — or, in this case, the analytical model or application — cannot be overestimated. Indeed, interviewees say that one of the major risks of analytics projects is that the decision makers won’t be savvy enough to understand the analysis or the model’s underlying assumptions, and they will try to apply it where it isn’t applicable. Explanation is also crucial in gaining trust, as one project manager at a financial institution notes: “Gut feeling and intuition still take precedence over analytics. No matter how transparent analytical models are, they are inevitably statistically complex. That’s why users find it difficult to put their faith in quantitative data and methods.” And this is why analytics project managers should be pedagogical experts and help open up the black box of analytic models.

Ultimately, our interviewees agree that expectation management should not be overlooked. Setting the right expectations at the beginning (for example, regarding the quality of the data and the applicability of the models) and managing them as the project progresses increases both acceptance and the chances that the project will be successful.

The process described above bears a strong resemblance to what W. Chan Kim and Renée Mauborgne, recognized thought-leaders and authorities on business strategy, innovation and wealth creation, have described as “fair process.” 8 Process fairness has proven its worth in diverse management contexts as a way to gain stakeholder commitment to decisions and change.

4. Relying on Intelligent Experimentation

A key element that emerges from the interview data is the importance of experimentation. Many of the analytics project managers we spoke to consider experimentation fundamental to the learning process. This is consistent with leading research by Harvard University professor and innovation management authority Stefan H. Thomke, who defines experimentation as a fundamental innovation-process activity, consisting of iterative trial and error and directed by insight. 9 The execution of an experiment, then, follows a four-step cycle: design, build, run and analyze.

The quality of the experimentation process has a strong bearing on the extent to which the project succeeds. Interviewees tend to be strong advocates of “good experimentation,” which is consistent with the scientific method. Well-designed experiments need clear goals and objectives, which is why the first steps of the scientific method devote significant time and effort to observation, to specifying the questions the experiment is intended to answer and to background research. Project managers need to invest time upfront examining the analytics project and setting the objectives. Good experiments need measurable hypotheses about the expected outcomes and controlled testing of these hypotheses. Interviewees reported spending significant amounts of time setting up the experiments and analyzing the results. What they learn forms the basis for improvements and for the next batch of experiments.

However, most interviewees recognize that a laboratory-style scientific approach is neither appropriate nor practical. They take a more pragmatic 10 approach to experimentation. Still, their process has enough rigor to allow a sufficiently accurate assessment of a project’s benefits. Interviewees acknowledge that business analytics is still a relatively new area and that some companies are still learning to incorporate analytics into their business. As a business analytics coordinator at an international food retailer put it: “Our policy is clear: Not using a control group is no option. But the reality is sometimes different. Many of our people are still trying to get to grips with analytics. Conflicts won’t help our case.” Successful, experienced project managers will try to advance the learning curve and coach the stakeholders into cultural change.

5. Promoting Smart Use of Information Technology

So far, information technology has been conspicuously absent from this discussion. Yet intelligent use of IT can allow for frequent and faster iterations between the design and operating environments, and this can improve experimentation efficiency. MIT professor and global IT expert Erik Brynjolfsson, who coined the phrase “IT productivity paradox,” 11 has noted that leading companies leverage IT to revolutionize the way in which they innovate by playing on four dimensions simultaneously: measurement, experimentation, sharing and replication. 12 The big advantage of IT-based experimentation, he argues, is that it can trace causality in a way that would be impossible with pure measurement and observation.

IT capabilities are key to helping companies to explore as well as exploit their full potential in turbulent markets. But while business has embraced IT capabilities , it is often far less positive about the IT department . Many of the analytics project managers we interviewed identify with the business side of their organizations even if they report to the IT department. They often trace their frustration with IT to negative experiences. As one explained: “Whenever IT is involved, business analytics projects cost more and take more time than planned. They are hideously inflexible. It’s virtually impossible to go ahead with anything at short notice. And not only that, they just speak another language. They really lack the sense of urgency and pragmatism you find on the business side.”

IT departments that ignore complaints from the business side risk being circumvented. In fact, some of the analytics teams we encountered built valuable models and applications independently from the IT department. Still, bypassing the IT department altogether can be counterproductive, especially when the focus is on delivering enterprise value rather than locally optimized solutions and functional value. Certainly with enterprise value, the most appropriate modus operandi would be to approach analytics projects as partnerships between the business side and IT. 13

Research suggests that best-in-class CIOs have realized that IT and business need to find better ways to work together. 14 By proposing pragmatic solutions and pointing out the consequences of infrastructural decisions, CIOs can become constructive partners, enabling their businesses to make smarter choices. This means that IT should, where possible, pursue opportunities to deliver faster implementation cycles, maintaining just enough process and architectural hygiene to ensure quality and professional support.

But what is just enough process and infrastructure? Enterprise infrastructure remains a long-term investment. The big challenge is to develop a process that provides for flexible infrastructure even as the process itself — the way applications and infrastructure are built and modified — remains stable. This will require infrastructure architects with mind-sets much like those of the analytics project managers we interviewed. Indeed, these architects will need to have a strong bias toward execution, so that IT solutions and infrastructure are rooted in the present without mortgaging the future.

Two things are certain. First, the boundaries between functional domains are blurring within organizations, requiring cross-functional collaboration. Second, it will take experience-based negotiation, not theoretical design, to create just enough process and infrastructure. This is a vital area where experienced analytics project managers can put their interpersonal skills to good use.

1. S. LaValle, E. Lesser, R. Shockley, M.S. Hopkins and N. Kruschwitz, “ Big Data, Analytics and the Path From Insights to Value ,” MIT Sloan Management Review 52, no. 2 (2011): 21-32.

2. See, for example, G. Schreyögg and M. Kliesch-Eberl, “How Dynamic Can Organizational Capabilities Be? Towards a Dual-Process Model of Capability Dynamization,” Strategic Management Journal 28, no. 9 (2007): 913-933; and O.A. El Sawy and P.A. Pavlou, “IT-Enabled Business Capabilities for Turbulent Environments,” MIS Quarterly Executive 7, no. 3 (2008): 139-150.

3. T.H. Davenport and J.G. Harris, “Competing on Analytics: The New Science of Winning” (Boston: Harvard Business Press, 2007); and T.H. Davenport, J.G. Harris and R. Morison, “Analytics at Work: Smarter Decisions, Better Results” (Boston: Harvard Business Press, 2010).

4. See, for example, D. Howell, C. Windahl and R. Seidel, “A Project Contingency Framework Based on Uncertainty and Its Consequences,” International Journal of Project Management 28, no. 3 (2010): 256-264; and A. Gemino, B.H. Reich and C. Sauer, “A Temporal Model of Information Technology Project Performance,” Journal of Management Information Systems 24, no. 3 (2008): 9-44.

5. J. Highsmith, “Agile Project Management: Creating Innovative Products,” 2nd ed. (Boston: Addison-Wesley Professional, 2009).

6. The notion of “value in use” was introduced by Adam Smith in 1776. See, for example, D. Walters, “Operations Strategy: A Value Chain Approach” (Basingstoke, United Kingdom: Palgrave Macmillan, 2002).

7. See, for example, Highsmith, “Agile Project Management”; L.M. Applegate, R.D. Austin and D.L. Soule, “Corporate Information Strategy and Management,” 8th ed. (New York: McGraw-Hill Professional, 2008), 592-596; and R. Austin and L. Devin, “Artful Making: What Managers Need to Know About How Artists Work” (Upper Saddle River, New Jersey: FT Press, 2003).

8. See, for example, W.C. Kim and R. Mauborgne, “Fair Process: Managing in the Knowledge Economy,” Harvard Business Review 81, no. 1 (2003): 127-136.

9. S.H. Thomke, “Managing Experimentation in the Design of New Products,” Management Science 44, no. 6 (1998): 743-762; and S.H. Thomke, “Experimentation Matters: Unlocking the Potential of New Technologies for Innovation” (Boston: Harvard Business Press, 2003).

10. “Pragmatic” should not be confused with “unprofessional.” We use the term “pragmatic” to describe an approach that is guided by experience and observation rather than by dogma.

11. The “IT productivity paradox” implies that despite massive investment and resourcing by companies and organizations worldwide, when it comes to the value of IT there seems to be little payoff. See E. Brynjolfsson, “The Productivity Paradox of Information Technology: Review and Assessment,” Communications of the ACM 36, no. 12 (1993): 67-77; and E. Brynjolfsson and L. Hitt, “Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending,” Management Science 42, no. 4 (1996): 541-558.

12. M.S. Hopkins, “ The Four Ways IT Is Revolutionizing Innovation ,” MIT Sloan Management Review 51, no. 3 (2010): 51-56.

13. See, for example, S. Viaene, “Linking Business Intelligence Into Your Business,” IT Professional 10, no. 6 (November/December 2008): 28-34; and S. Viaene, S. De Hertogh and L. Lutin, “Shadow or Not? A Business Intelligence Tale at KBC Bank,” Case Folio (January 2009): 19-29.

14. S. Viaene, S. De Hertogh and O. Jolyon, “Engaging in Turbulent Times: Direction Setting for Business and IT Alignment,” International Journal of IT/Business Alignment and Governance 2, no. 1 (2011): 1-15.

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses , and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

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Why are business analysis projects important, key tools required for business analysis projects, top challenges in business analysis  projects, 10 business analysis project ideas, conclusion , top business analysis projects for 2024.

Business Analytics is a highly promising field crucial for planning and decision-making in large organizations. Given its significant impact on the market, there is a high demand for professionals, resulting in numerous job opportunities. To secure these positions, having a compelling resume is essential. An effective way to enhance your resume is to showcase the business analytics projects you have undertaken. 

In this article, we'll discuss why business analytics projects matter, check out the tools you need, discuss the challenges, and share the 10 best projects to supercharge your resume.

Business analytics projects are important for two key reasons. 

• They provide a practical way to apply various skills to real-world challenges from start to finish. While different exercises are helpful, working on complete projects allows for comprehensively applying various skills. 
• A portfolio featuring these projects is crucial for landing a business analyst job. Beyond an impressive resume and list of qualifications, employers want to see your skills in action. A portfolio filled with completed projects is the best way to demonstrate what you can do and increase your chances of securing interviews effectively.

Here are the key tools required for successful business analytics projects, each serving a specific purpose in the analytical process:

Data Analysis Tools

Tools like Excel, Power BI , Tableau , SQL , and Python are crucial for collecting, organizing, and interpreting data from various sources in business analytics projects. They help identify patterns, trends, and opportunities in data, allowing for hypothesis testing and assumption validation.

Process Modeling Tools

In business analytics projects, tools like Visio, Lucidchart, Bizagi, BPMN, and UML assist in documenting and optimizing analytical processes. These tools use graphical notations and diagrams to capture the current and desired states of processes, facilitating effective communication with stakeholders.

Requirements Management Tools

Jira, Trello, Confluence, Rational RequisitePro, and Caliber are examples of tools that help manage requirements for business analytics projects. They ensure clarity, completeness, and consistency in aligning requirements with project goals and stakeholder needs.

Collaboration Tools

Collaboration tools such as Slack, Zoom, Teams, Google Workspace, and SharePoint are instrumental in promoting communication and teamwork among analysts and stakeholders, especially when working across different locations and time zones.

Testing Tools

Testing tools, including Selenium , TestRail, Postman, SoapUI, and JMeter, play a crucial role in verifying and validating the quality and functionality of solutions or products in business analytics projects. They assist in designing, executing, and reporting test cases while identifying and resolving defects and issues.

Change Management Tools

Change management tools like Prosci ADKAR, Kotter's 8-Step Model, Lewin's Change Model, and Change Compass help plan, implement, and evaluate changes within the context of business analytics projects. These tools assist in assessing the impact and readiness of change and managing resistance and associated risks.

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Getting business analytics projects right involves recognizing and addressing key challenges. Let's delve into two major aspects:

Technological Complexity

Navigating the ever-evolving technological landscape poses a significant challenge for business analytics projects. The abundance of tools and techniques brings opportunities, but the risk of overlapping functionalities can complicate decision-making processes.

Project Execution and Adaptation

The success of business analytics projects hinges on overcoming challenges related to setting clear goals, integrating data seamlessly, and translating insights into actionable strategies. Furthermore, adapting to ongoing development processes is crucial for ensuring the project aligns with evolving needs and objectives.

Now, let's take a closer look at the 10 best business analytics projects that you can undertake to delve into the intricacies of this dynamic field.

1. Market Basket Analysis

Explore the fascinating patterns in customer shopping behaviors. Understand which products are commonly purchased together and learn how to enhance recommendation systems and optimize store layouts. This project is a gateway to understanding customer preferences and contributing to a company's sales strategy.

2. Customer Review Sentiment Analysis

Gain a comprehensive understanding of customer sentiments through the analysis of product reviews. Acquire the skills to interpret customer emotions, utilizing this insight to enhance product features, resolve issues, and cultivate an outstanding customer experience. Engage in this practical project to demonstrate your capacity to establish a personal connection with customers.

3. Price Optimization

Acquire expertise in the strategic task of setting optimal prices for products. Analyze historical data, market conditions, and customer profiles to make informed decisions. This project equips you with the skills to navigate the dynamic pricing world, a crucial aspect for any aspiring business analyst entering a competitive market landscape.

4. Sales Data Analysis

Immerse yourself in the core of business success by exploring sales data. Learn how to understand customer behavior, what they purchase, and when. This analysis equips you with the skills to predict future sales trends, a valuable asset for any aspiring business analyst looking to contribute to a company's growth.

5. Customer Churn Rate Prediction

Sharpen your ability to understand and predict customer loyalty by learning how to foresee and minimize churn rates. Churn rates reveal the percentage of customers who stop using a product or service. Mastering this project allows you to proactively address concerns and enhance customer satisfaction, a vital skill for any budding business analyst.

6. Stock Market Data Analysis

Delve into the intricacies of the stock market to make well-informed investment decisions. Analyze daily price changes, trading volumes, and historical patterns to understand market behavior. This project empowers you to make strategic choices and positions you as a data-savvy business analyst who can navigate the dynamic landscape of financial markets.

7. Customer Segmentation

Hone your marketing skills by mastering the art of customer segmentation. Categorize customers based on behavior, interests, and loyalty. This practical guide enables you to direct marketing efforts effectively, save resources, and maximize profits by tailoring strategies to specific target groups. It is a foundational skill for any aspiring business analyst looking to contribute to impactful and targeted marketing campaigns.

8. Fraud Detection

Arm yourself with advanced skills in fraud detection, a critical area in the realm of business analytics. Learn to analyze intricate patterns in data to identify anomalies, from credit card fraud to cyber attacks . Mastery in fraud detection not only safeguards the financial integrity of a business but also positions you as a vigilant business analyst capable of addressing evolving challenges in the digital landscape.

9. Life Expectancy Analysis

Embark on a comprehensive exploration of the factors influencing life expectancy in a region. Analyze correlations between economic indicators, environmental conditions, political landscapes, and social trends. This project provides invaluable insights for those aspiring to contribute to public health initiatives and societal well-being, establishing you as a business analyst with a holistic understanding of the factors shaping community health.

10. Building a BI App

Familiarize yourself with Business Intelligence (BI) applications like Microsoft Power BI, going beyond surface-level understanding. This hands-on project allows you to showcase your data visualization skills fully. Building a BI app enhances your proficiency and positions you as a forward-thinking business analyst capable of transforming complex data into meaningful insights for informed decision-making in diverse organizational settings.

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In conclusion, business analytics projects open the door to a world of possibilities, allowing you to hone your analytical prowess and make a tangible impact on business strategies. Whether predicting sales trends, enhancing customer experiences, or safeguarding against fraud, these projects offer a valuable learning experience.

For those seeking to sharpen their business analytics skills further and dive deeper into strategic decision-making, consider exploring Simplilearn’s Post Graduate Program in Business Analysis . This extensive program is crafted to equip you with the necessary knowledge and tools to adeptly navigate the intricacies of strategic decision-making using proficient business analytics.

1. What qualifications are needed for a business analyst? 

Certain positions may give preference to or require a master's degree, particularly for senior roles. A bachelor's degree in business, finance, economics, information technology, or a closely related field is generally necessary. 

2. How do business analysis techniques vary across industries? 

IT and Software Development is about translating business needs into software requirements. In Finance, the focus is on financial concepts, modeling, and risk management, tailoring the approach to the sector's demands.

3. What are the common pitfalls in business analysis projects? 

Common pitfalls include insufficient stakeholder involvement, unclear requirements, communication gaps, and misalignment with business goals. Proactive communication and adaptability are key to navigating these challenges.

4. How can technology enhance business analysis?

Technology streamlines processes automates data analysis, and fosters stakeholder collaboration. Tools like data analytics software and project management platforms contribute to efficient and accurate business analysis, enabling deeper insights and informed decision-making in a data-driven landscape.

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36 Data Analytics Project Ideas and Datasets (2024 UPDATE)

Data analytics projects help you to build a portfolio and land interviews. It is not enough to just do a novel analytics project however, you will also have to market your project to ensure it gets found.

The first step for any data analytics project is to come up with a compelling problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the strongest categories for data analytics project ideas include:

• Beginner Analytics Projects  - For early-career data analysts, beginner projects help you practice new skills.
• Python Analytics Projects - Python allows you to scrape relevant data and perform analysis with pandas dataframes and SciPy libraries.
• Rental and Housing Data Analytics Projects - Housing data is readily available from public sources, or can be simple enough to create your own dataset. Housing is related to many other societal forces, and because we all need some form of it, the topic will always be of interest to many people.
• Sports and NBA Analytics Projects - Sports data can be easily scraped, and by using player and game stats you can analyze strategies and performance.
• Data Visualization Projects - Visualizations allow you to create graphs and charts to tell a story about the data.
• Music Analytics Projects - Contains datasets for music-related data and identifying music trends.
• Economics and Current Trends - From exploring GDPs of respective countries to the spread of the COVID-19 virus, these datasets will allow you to explore a wide variety of time-relevant data.
• Advanced Analytics Projects - For data analysts looking for a stack-filled project.

A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?

Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:

• Learn new tools and techniques.
• Work with complex datasets.
• Practice packaging your work and results.
• Prep for a case study and take-home interviews.
• Give you inbound interviews from hiring managers that have read your blog post!

Beginner Data Analytics Projects

Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.

1. Relax Predicting User Adoption Take-Home

This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.

Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.

How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.

2. Salary Analysis

Are you in some sort of slump, or do you find the other projects a tad too challenging? Here’s something that’s really easy; this is a salary dataset from Kaggle that is easy to read and clean, and yet still has many dimensions to interpret.

This salary dataset is a good candidate for descriptive analysis , and we can identify which demographics experience reduced or increased salaries. For example, we could explore the salary variations by gender, age, industry, and even years of prior work.

How you can do it: The first step is to grab the dataset from Kaggle. You can either use it as-is and use spreadsheet tools such as Excel to analyze the data, or you can load it into a local SQL server and design a database around the available data. You can then use visualization tools such as Tableau to visualize the data; either through Tableau MySQL Connector, or Tableau’s CSV import feature.

3. Skilledup Messy Product Data Analysis Take-Home

This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.

Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:

• Is there redundancy in the categorization?
• How can redundancy be identified and removed?
• Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?

How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.

4. Marketing Analytics Exploratory Data Analysis

This  marketing analytics dataset  on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:

• What factors are significantly related to the number of store purchases?
• Is there a significant relationship between the region the campaign is run in and that campaign’s success?
• How does the U.S. compare to the rest of the world in terms of total purchases?

How you can do it:  This  Kaggle Notebook from user Jennifer Crockett  is a good place to start, and includes quite a few visualizations and analyses.

If you want to take it a step further, there is quite a bit of statistical analysis you can perform as well.

5. UFO Sightings Data Analysis

The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a robust source for a beginner EDA project, and you can create insights into where sightings are reported most frequently sightings in the U.S. vs the rest of the world, and more.

How you can do it:  Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an  interactive map with sighting data .

6. Data Cleaning Practice

This  Kaggle Challenge asks you to clean data as well as perform a variety of data cleaning tasks. This is a perfect beginner data analytics project, which will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.

How you can do it:  You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.

Python Data Analytics Projects

Python is a powerful tool for data analysis projects. Whether you are web scraping data - on sites like the New York Times and Craigslist - or you’re conducting EDA on Uber trips, here are three Python data analytics project ideas to try:

7. Enigma Transforming CSV file Take-Home

This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.

As you work through this practice challenge, focus specifically on the grading criteria, which include:

• How well you solve the problems.
• The logic and approach you take to solving them.
• Your ability to produce, document, and comment on code.
• Ultimately, the ability to write clear and clean scripts for data preparation.

8. Wedding Crunchers

Todd W. Schneider’s  Wedding Crunchers  is a prime example of a data analysis project using Python. Todd  scraped wedding announcements  from the New York Times, performed analysis on the data, and found intriguing tidbits like:

• Distribution of common phrases.
• Average age trends of brides and grooms.
• Demographic trends.

Using the data and his analysis Schneider created a lot of cool visuals, like this one on Ivy League representation in the wedding announcements:

How you can do it:  Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.

9. Scraping Craigslist

Craigslist is a classic data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.

Riley Predum created a handy tutorial  that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some interesting analysis of pricing when segmented by neighborhood and price distributions. When graphed, his analysis looked like this:

How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.

10. Uber Trip Analysis

Here’s a cool project from Aman Kharwal: An  analysis of Uber trip data from NYC.  The project used this  Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.

Here’s a data visualization on pickup times by hour of the day from Aman:

How you can do it:  This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and  Uber datasets on data.world,  including one for Austin, TX.

11. Twitter Sentiment Analysis

Twitter (now X) is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.

One option would be to measure sentiment in your dataset over time like this:

How you can do it:  This tutorial from Natassha Selvaraj  provides step-by-step instructions to do sentiment analysis on Twitter data. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets.

12. Home Pricing Predictions

This project has been featured in our list of  Python data science projects . With this project, you can take the classic  California Census dataset , and use it to predict home prices by region, zip code, or details about the house.

Python can be used to produce some stunning visualizations, like this heat map of price by location.

How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python. Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.

Rental and Housing Data Analytics Project Ideas

There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.

If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:

13. Airbnb Data Analytics Take-Home Assignment

• Overview:  Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
• Time Required:  6 hours
• Skills Tested:  Analytics, EDA, growth marketing, data visualization
• Deliverable:  Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).

This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.

This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:

• Analytical approach and clarity of visualizations.
• Your data sense and decision-making, as well as the reproducibility of the analysis.
• Strength of your recommendations
• Your ability to communicate insights in your presentation.
• Your ability to follow directions.

14. Zillow Housing Prices

Check out  Zillow’s free datasets.  The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.

Here’s an  analytics project based in R  that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:

How you can do it:  There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.

15. Inside Airbnb

On  Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. There is data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.

Agratama Arfiano has extensively examined Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really striking visualizations for this project, including the following:

How you can do it:  Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.

16. Car Rentals

Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:

How you can do it: Using the dataset, you could analyze rental cars by make and model, a particular location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets:  Cash for Clunkers cars ,  Carvana sales data or used cars on eBay .

17. Analyzing NYC Property Sales

This  real estate dataset  shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.

A beginner analytics project you can try with this data would be a missing values analysis project like:

How you can do it: There are a ton of  helpful Kaggle notebooks  you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.

Sports and NBA Data Analytics Projects

Sports data analytics projects are fun if you’re a fan, and also, because there are quite a few free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull a wide range of statistics and build your own unique dataset to investigate a problem.

18. NBA Data Analytics Project

Check out this  NBA data analytics project  from Jay at Interview Query. Jay analyzed data from  Basketball Reference  to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to an end-of-quarter strategy where a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the  source code on GitHub).

The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:

How you can do it: Read this tutorial on  scraping Basketball Reference data . You can analyze in-game statistics, career statistics, playoff performance, and much more. An idea could be to analyze a player’s high school ranking  vs. their success in the NBA. Or you could visualize a player’s career.

19. Olympic Medals Analysis

This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there is plenty of data to analyze, and it’s useful for identifying performance trends by country and sport. Here’s a visualization from Didem Erkan :

How you can do it: Check out the  Olympics medals dataset . Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g., how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.

20. Soccer Power Rankings

FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of  working with the SPI data .

How you can do it:  Check out the  SPI data . Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?

21. Home Field Advantage Analysis

Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from  Pro-Football-Reference.com . Then you can perform a simple linear regression model to measure the impact.

There are a ton of projects you can do with NFL data. One would be to  determine WR rankings, based on season performance .

How you can do it:  See this Github repository on performing a  linear regression to quantify home field advantage .

22. Daily Fantasy Sports

Creating a model to perform in daily fantasy sports requires you to:

• Predict which players will perform best based on matchups, locations, and other indicators.
• Build a roster based on a “salary cap” budget.
• Determine which players will have the top ROI during the given week.

If you’re interested in fantasy football, basketball, or baseball, this would be a strong project.

How you can do it: Check out the  Daily Fantasy Data Science course , if you want a step-by-step look.

Data Visualization Projects

All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!

23. Supercell Data Scientist Pre-Test

This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.

You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.

24. Visualizing Pollution

This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:

How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.

26. Visualizing Top Movies

There are a ton of movie and media datasets on Kaggle:  The Movie Database 5000 ,  Netflix Movies and TV Shows ,  Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for fantastic visualizations.

Take a look at this  visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:

How you can do it: Take a Kaggle movie dataset, and create a visualization that shows one of the following: gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.

27. Gender Pay Gap Analysis

Salary is a subject everyone is interested in, and it makes it a relevant subject for visualization. One idea: Take this dataset from the  U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.

You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:

How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or  data science salaries by the company .

27. Visualize Your Favorite Book

Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an  Italo Calvo book into cool visualizations . The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:

How you can do it: This  Shakespeare dataset , which features all of the lines from his plays, would be ripe for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script.

Music Analytics Projects

If you’re a music fan, music analytics projects are a good way to jumpstart your portfolio. Of course, analyzing music through digital signal processing is out of our scope, so the best way to go around music-related projects is through exploring trends and charts. Here are some resources that you may use.

28. Popular Music Analysis

Here’s one way to analyze music features without explicit feature extraction. This dataset from Kaggle contains a list of popular music from the 1960s. A feature of this dataset is that it is currently being maintained. Here are a few approaches you can use.

How you can do it: You can grab this dataset from Kaggle. This dataset has classifications for popularity, release date, album name, and even genre. You can also use pre-extracted features such as time signature, liveness, valence, acoustic-ness, and even tempo.

Load this dataset into a Pandas DataFrame and do your appropriate processes there. You can analyze how the features move over time (i.e., did songs over time get a bit more mellow, livelier, or louder), or you can even explore the rise and fall of artists over time.

29. KPOP Melon Music Charts Analysis

If you’re interested in creating a KPOP-related analytics project, here’s one for you. While this is not a dataset, what we have here is a data source that scrapes data from the Melon charts and shows you the top 100 songs in the weekly, daily, rising, monthly, and LIVE charts.

How you can do it: The problem with this data source is that it is scraped, so gathering previous data might be a bit problematic. In order to do historical analysis, you will need to compile and store the data yourself.

So for this approach, we will prefer a locally hosted infrastructure. Knowing how to use cloud services to automate and store data might introduce additional layers of complexity for you to show off to a recruiter. Here’s a local approach to conducting this project.

The first step is to decide which database solution to use. We recommend XAMPP’s toolkit with MySQL Server and PHPMyAdmin as it provides an easy-to-use frontend while also providing a query builder that allows you to construct table schemas, so learning DDL (Data Definition Language) is not as much of a necessity.

The second step is to create a Python script that scrapes data from Melon’s music charts. Thankfully, we have a module that scrapes data from the charts. First, install the melonapi module. Then, you can gather the data and store it in your database. Here’s a step-by-step guide to loading the data from the site.

Of course, running this script over a period of time manually opens the door to human forgetfulness or boredom. To avoid this, you can use an automation service to automate your processes. For Windows systems, you can use the built-in Windows Task Scheduler. If you’re using Mac, you can use Automator.

When you have the appropriate data, you can then perform analytics, such as examining how songs move over time, classifying songs by album, and so on.

Economic and Current Trends Analytics Projects

One of the most valuable analytics projects is those that delve into economic and current trends. These projects, which make use of data from financial market trends, public demographic data, and social media behavior, are powerful tools not only for businesses and policymakers but also for individuals who aim to better understand the world around them.

When discussing current trends, COVID-19 is a significant phenomenon that continues to profoundly impact the status quo. An in-depth analysis of COVID-19 datasets can provide valuable insights into public health, global economies, and societal behavior.

How you can do it: These datasets, readily available for download, focus on different geographical areas. Here are a few:

• EU COVID-19 Dataset - dataset from the European Centre for Disease Prevention and Control, contains COVID-19 data for EU territories.
• US COVID-19 Dataset - US COVID-19 data provided by the New York Times. However, data might be outdated.
• Mexico COVID-19 Dataset - A COVID-19 dataset provided by the Mexican government.

These datasets provide opportunities to develop predictive algorithms and to create visualizations depicting the virus’s spread over time. Despite COVID-19 being less deadly today, it has become more contagious , and insights derived from these datasets can be crucial for understanding and combating future pandemics. For instance, a time-series analysis could identify key periods of infection rates’ acceleration and slow-down, highlighting effective and ineffective public health measures.

31. News Media Dataset

The News Media Dataset provides valuable information about the top 43 English media channels on YouTube, including each of their top 50 videos. This dataset, although limited in its scope, can offer intriguing insights into viewer preferences and trends in news consumption.

How you can do it: Grab the dataset from Kaggle and use the dataset which contains the top 50 viewed videos per channel. There are a lot of insights you can gain here, such as using a basic sentiment analysis tool to determine whether the top-performing headlines were positive or negative.

For sentiment analysis, you don’t necessarily need to train a model. You can load the CSV file and loop through all the tags. Use the TextBlob module to conduct sentiment analysis. Here’s how you can go about doing it:

Then, by using the subjectivity and polarity metrics, you can create visualizations that reflect your findings.

32. The Big Mac Index Analytics

The Big Mac Index offers an intriguing approach to comparing purchasing power parity (PPP) between different countries. The index shows how the U.S. dollar compares to other currencies, through a standardized, identical product, the McDonald’s Big Mac. The dataset, provided by Andrii Samoshyn, contains a lot of missing data, offering a real-world exercise in data cleaning. The data goes back to April 2000 up until January 2020.

How you can do it: You can download the dataset from Kaggle here . One common strategy for handling missing data is by using measures of central tendency like mean or median to fill in gaps. More advanced techniques, such as regression imputation, could also be applicable depending on the nature of the missing data.

Using this cleaned dataset, you can compare values over time or between regions. Introducing a “geographical proximity” column could provide additional layers of analysis, allowing comparisons between neighboring countries. Machine Learning techniques like clustering or classification could reveal novel groupings or patterns within the data, providing a richer interpretation of global economic trends.

When conducting these analyses, it’s important to keep in mind methods for evaluating the effectiveness of your work. This might involve statistical tests for significance, accuracy measures for predictive models, or even visual inspection of plotted data to ensure trends and patterns have been accurately captured. Remember, any analytics project is incomplete without a robust method of evaluation.

33. Global Country Information Dataset

This dataset offers a wealth of information about various countries, encompassing factors such as population density, birth rate, land area, agricultural land, Consumer Price Index (CPI), Gross Domestic Product (GDP), and much more. This data provides ample opportunity for comprehensive analysis and correlation studies among different aspects of countries.

How you can do it : Download this dataset from Kaggle. This dataset includes diverse attributes, ranging from economic to geographic factors, creating an array of opportunities for analysis. Here are some project ideas:

• Correlation Analysis: Investigate the correlations between different attributes, such as GDP and education enrollment, population density and CO2 emissions, birth rate, and life expectancy. You can use libraries like pandas and seaborn in Python for these tasks.
• Geospatial Analysis: With latitude and longitude data available, you could visualize data on a world map to understand global patterns better. Libraries such as geopandas and folium can be helpful here.
• Predictive Modeling: Try to predict an attribute based on others. For instance, could you predict a country’s GDP based on factors like population, education enrollment, and CO2 emissions?
• Cluster Analysis: Group countries based on various features to identify patterns. Are there groups of countries with similar characteristics, and if so, why?

Remember to perform EDA before diving into modeling or advanced analysis, as this will help you understand your data better and could reveal insights or trends to explore further.

34. College Rankings and Tuition Costs Dataset

This dataset offers valuable information regarding various universities, including their rankings and tuition fees. It allows for a comprehensive analysis of the relationship between a university’s prestige, represented by its ranking, and its cost.

How you can do it: First, download the dataset from Kaggle . You can then use Python’s pandas for data handling, and matplotlib or seaborn for visualization.

Possible analyses include exploring the correlation between college rankings and tuition costs, comparing tuition costs of private versus public universities, and studying trends in tuition costs over time. For a more advanced task, try predicting college rankings based on tuition and other variables.

Advanced Data Analytics Project

Ready to take your data skills to the next level? Advanced projects are a way to do just that. They’re all about handling larger datasets, really digging into data cleaning and preprocessing, and getting your hands dirty with a range of tech stacks. It’s a two-in-one deal – you’ll dip your toes inside the roles of both a data engineer and a data scientist. Here are some project ideas to consider.

35. Analyzing Google Trends Data

Google Trends, a free service provided by Google, can serve as a treasure trove for data analysts, offering insights into popular trends worldwide. But there’s a hitch. Google Trends does not support any official API, making direct data acquisition a bit challenging. However, there’s a workaround — web scraping. This guide will walk you through the process of using a Python module for scraping Google Trends data.

How you can do it: Of course, we would not want to implement a web scraper ourselves. Simply put, it’s too much work. For this project, we will utilize a Python module that will help us scrape the data. Let’s view an example:

This code should print out the data in the following format:

You should use an automation service to automate scraping at least once per hour (see: KPOP Melon Music Charts Analysis) . Then, you should store the results in a CSV file that you can query later. There are many points of analysis, such as keyword rankings, website rankings for articles, and more.

Taking it a step further:

If you want to make an even more robust project that’s bound to wow your recruiters, here are some ideas to make the scraping process easier to maintain, albeit with a higher difficulty in setting up.

The first problem in our previous approach is the hardware issue. Simply put, the automation service we used earlier is moot if our device is off or if it was not instantiated during device startup. To solve this, we can utilize the cloud.

Using a function service (i.e., GCP Cloud Functions, AWS Lambda), you can execute Python scripts. Now, you will need to orchestrate this service, and you can use a Pub/Sub service such as GCP Pub/Sub and AWS SNS. These will alert your cloud functions to run, and you can modify the Pub/Sub service to run at a specified time gap.

Then, when your script successfully scrapes the data, you will need a SQL server instance. The flavor of SQL does not really matter, but you can use the available databases provided by your cloud provider. For example, AWS offers RDS, while GCP offers Cloud SQL.

Once your data is pulled together, you can then start analyzing your data and employing analysis techniques to visualize and interpret data.

36. New York Times (NYT) Movie Reviews Sentiment Analysis

Sentiment Analysis is a critical tool in gauging public opinion and emotional responses towards various subjects, and in this case, movies. With a substantial number of movie reviews published daily in well-circulated publications like the NYT, proper sentiment analysis can provide valuable insights into the perceived quality of films and their reception among critics.

How you can do it: As a data source, NYT has an API service that allows you to query their databases. Create an account at this link and enable the ‘Movie Reviews’ service. Then, using your API key, you can start querying using the following script:

The query looks up the titles and returns movie reviews matching those in the query. You can then use the review summaries to do sentiment analysis.

Other NY Times APIs you can explore include the Most Popular API , and the Top Stories API .

More Analytics Project Resources

If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.

You should also read our guide on the data analyst career path , how to become a data analyst without a degree , how to build a data science project from scratch and list of 30 data science project ideas .

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Home » 500+ Business Research Topics

500+ Business Research Topics

Business research is an essential component of any successful organization, as it allows companies to make informed decisions based on data-driven insights. Whether it’s market research to identify new opportunities, or analyzing internal processes to improve efficiency, there are a vast array of business research topics that companies can explore. With the constantly evolving business landscape, it’s critical for organizations to stay up-to-date with the latest research trends and best practices to remain competitive. In this post, we’ll explore some of the most compelling business research topics that are currently being studied, providing insights and actionable recommendations for businesses of all sizes.

Business Research Topics

Business Research Topics are as follows:

• The impact of social media on consumer behavior
• Strategies for enhancing customer satisfaction in the service industry
• The effectiveness of mobile marketing campaigns
• Exploring the factors influencing employee turnover
• The role of leadership in organizational culture
• Investigating the relationship between corporate social responsibility and financial performance
• Assessing the impact of employee engagement on organizational performance
• The challenges and opportunities of global supply chain management
• Analyzing the effectiveness of e-commerce platforms
• Investigating the impact of organizational culture on employee motivation
• The role of corporate governance in ensuring ethical business practices
• Examining the impact of digital marketing on brand equity
• Strategies for managing diversity and inclusion in the workplace
• Exploring the effects of employee empowerment on job satisfaction
• The role of innovation in business growth
• Analyzing the impact of mergers and acquisitions on company performance
• Investigating the impact of workplace design on employee productivity
• The challenges and opportunities of international business expansion
• Strategies for managing talent in the knowledge economy
• The role of artificial intelligence in transforming business operations
• Examining the impact of customer loyalty programs on retention and revenue
• Investigating the relationship between corporate social responsibility and brand reputation
• The role of emotional intelligence in effective leadership
• The impact of digital transformation on small and medium-sized enterprises
• Analyzing the effectiveness of green marketing strategies
• The role of entrepreneurship in economic development
• Investigating the impact of employee training and development on organizational performance
• The challenges and opportunities of omnichannel retailing
• Examining the impact of organizational change on employee morale and productivity
• The role of corporate social responsibility in attracting and retaining millennial talent
• Analyzing the impact of employee motivation on organizational culture
• Investigating the impact of workplace diversity on team performance
• The challenges and opportunities of blockchain technology in business operations
• Strategies for managing cross-functional teams
• The role of big data analytics in business decision-making
• Examining the impact of corporate social responsibility on customer loyalty
• Investigating the relationship between corporate social responsibility and employee engagement
• The impact of social media marketing on customer engagement and brand loyalty.
• The effectiveness of AI in improving customer service and satisfaction.
• The role of entrepreneurship in economic development and job creation.
• The impact of the gig economy on the labor market.
• The effects of corporate social responsibility on company profitability.
• The role of data analytics in predicting consumer behavior and market trends.
• The effects of globalization on the competitiveness of small businesses.
• The impact of e-commerce on traditional brick-and-mortar retail.
• The role of emotional intelligence in leadership and team management.
• The effects of workplace diversity on employee productivity and satisfaction.
• The role of corporate culture in employee retention and satisfaction.
• The impact of employee training and development on company performance.
• The effectiveness of performance-based pay structures on employee motivation.
• The impact of sustainability practices on company reputation and profitability.
• The effects of artificial intelligence on job displacement and the future of work.
• The role of innovation in the growth and success of small businesses.
• The impact of government regulations on business operations and profitability.
• The effects of organizational structure on company performance and efficiency.
• The role of emotional labor in service industries.
• The impact of employee empowerment on job satisfaction and retention.
• The effects of workplace flexibility on employee productivity and well-being.
• The role of emotional intelligence in negotiation and conflict resolution.
• The impact of branding on consumer behavior and purchase decisions.
• The effects of customer experience on brand loyalty and advocacy.
• The role of storytelling in marketing and advertising.
• The impact of consumer psychology on pricing strategies and sales.
• The effects of influencer marketing on consumer behavior and brand loyalty.
• The role of trust in online transactions and e-commerce.
• The impact of product design on consumer perception and purchasing decisions.
• The effects of customer satisfaction on company profitability and growth.
• The role of social entrepreneurship in addressing societal problems and creating value.
• The impact of corporate governance on company performance and stakeholder relations.
• The effects of workplace harassment on employee well-being and company culture.
• The role of strategic planning in the success of small businesses.
• The impact of technology on supply chain management and logistics.
• The effects of customer segmentation on marketing strategies and sales.
• The role of corporate philanthropy in building brand reputation and loyalty.
• The impact of intellectual property protection on innovation and creativity.
• The effects of trade policies on international business operations and profitability.
• The role of strategic partnerships in business growth and expansion.
• The impact of digital transformation on organizational structure and operations.
• The effects of leadership styles on employee motivation and performance.
• The role of corporate social activism in shaping public opinion and brand reputation.
• The impact of mergers and acquisitions on company performance and stakeholder value.
• The effects of workplace automation on job displacement and re-skilling.
• The role of cross-cultural communication in international business operations.
• The impact of workplace stress on employee health and productivity.
• The effects of customer reviews and ratings on online sales and reputation.
• The role of competitive intelligence in market research and strategy development.
• The impact of brand identity on consumer trust and loyalty.
• The impact of organizational structure on innovation and creativity
• Analyzing the effectiveness of virtual teams in global organizations
• The role of corporate social responsibility in crisis management
• The challenges and opportunities of online marketplaces
• Strategies for managing cultural diversity in multinational corporations
• The impact of employer branding on employee retention
• Investigating the impact of corporate social responsibility on investor behavior
• The role of technology in enhancing customer experience
• Analyzing the impact of social responsibility initiatives on customer satisfaction
• Investigating the impact of supply chain disruptions on business performance
• The role of business ethics in organizational decision-making
• The challenges and opportunities of artificial intelligence in customer service
• Strategies for managing employee burnout and stress in the workplace.
• Impact of social media on consumer behavior and its implications for businesses.
• The impact of corporate social responsibility on company performance.
• An analysis of the relationship between employee satisfaction and customer loyalty.
• The effect of advertising on consumer behavior.
• A study on the effectiveness of social media marketing in building brand image.
• The impact of technological innovations on business strategy and operations.
• The relationship between leadership style and employee motivation.
• A study of the effects of corporate culture on employee engagement.
• An analysis of the factors influencing consumer buying behavior.
• The effectiveness of training and development programs in enhancing employee performance.
• The impact of global economic factors on business decision-making.
• The role of organizational communication in achieving business goals.
• The relationship between customer satisfaction and business success.
• A study of the challenges and opportunities in international business.
• The effectiveness of supply chain management in improving business performance.
• An analysis of the factors influencing customer loyalty in the hospitality industry.
• The impact of employee turnover on organizational performance.
• A study of the impact of corporate governance on company performance.
• The role of innovation in business growth and success.
• An analysis of the relationship between marketing and sales performance.
• The effect of organizational structure on employee behavior.
• A study of the impact of cultural differences on business negotiations.
• The effectiveness of pricing strategies in increasing sales revenue.
• The impact of customer service on customer loyalty.
• A study of the role of human resource management in business success.
• The impact of e-commerce on traditional brick-and-mortar businesses.
• An analysis of the relationship between employee empowerment and job satisfaction.
• The effectiveness of customer relationship management in building brand loyalty.
• The role of business ethics in corporate decision-making.
• A study of the impact of digital marketing on consumer behavior.
• The effect of organizational culture on employee turnover.
• An analysis of the factors influencing employee engagement in the workplace.
• The impact of social media on business communication and marketing.
• A study of the relationship between customer service and customer loyalty in the airline industry.
• The role of diversity and inclusion in business success.
• The effectiveness of performance management systems in improving employee performance.
• The impact of corporate social responsibility on employee engagement.
• A study of the factors influencing business expansion into new markets.
• The role of brand identity in customer loyalty and retention.
• The effectiveness of change management strategies in organizational change.
• The impact of organizational structure on organizational performance.
• A study of the impact of technology on the future of work.
• The relationship between innovation and competitive advantage in the marketplace.
• The effect of employee training on organizational performance.
• An analysis of the impact of online reviews on consumer behavior.
• The role of leadership in shaping organizational culture.
• The effectiveness of talent management strategies in retaining top talent.
• The impact of globalization on small and medium-sized enterprises.
• A study of the relationship between corporate social responsibility and brand reputation.
• The effectiveness of employee retention strategies in reducing turnover rates.
• The role of emotional intelligence in leadership and employee engagement.
• The impact of digital marketing on customer behavior
• The role of organizational culture in employee engagement and retention
• The effects of employee training and development on organizational performance
• The relationship between corporate social responsibility and financial performance
• The impact of globalization on business strategy
• The importance of supply chain management in achieving competitive advantage
• The role of innovation in business growth and sustainability
• The impact of e-commerce on traditional retail businesses
• The role of leadership in managing change in organizations
• The effects of workplace diversity on organizational performance
• The impact of social media on brand image and reputation
• The relationship between employee motivation and productivity
• The role of organizational structure in promoting innovation
• The effects of customer service on customer loyalty
• The impact of globalization on small businesses
• The role of corporate governance in preventing unethical behavior
• The effects of technology on job design and work organization
• The relationship between employee satisfaction and turnover
• The impact of mergers and acquisitions on organizational culture
• The effects of employee benefits on job satisfaction
• The impact of cultural differences on international business negotiations
• The role of strategic planning in organizational success
• The effects of organizational change on employee stress and burnout
• The impact of business ethics on customer trust and loyalty
• The role of human resource management in achieving competitive advantage
• The effects of outsourcing on organizational performance
• The impact of diversity and inclusion on team performance
• The role of corporate social responsibility in brand differentiation
• The effects of leadership style on organizational culture
• The Impact of Digital Marketing on Brand Equity: A Study of E-commerce Businesses
• Investigating the Relationship between Employee Engagement and Organizational Performance
• Analyzing the Effects of Corporate Social Responsibility on Customer Loyalty and Firm Performance
• An Empirical Study of the Factors Affecting Entrepreneurial Success in the Technology Sector
• The Influence of Organizational Culture on Employee Motivation and Job Satisfaction: A Case Study of a Service Industry
• Investigating the Impact of Organizational Change on Employee Resistance: A Comparative Study of Two Organizations
• An Exploration of the Impact of Artificial Intelligence on Supply Chain Management
• Examining the Relationship between Leadership Styles and Employee Creativity in Innovative Organizations
• Investigating the Effectiveness of Performance Appraisal Systems in Improving Employee Performance
• Analyzing the Role of Emotional Intelligence in Leadership Effectiveness: A Study of Senior Managers
• The Impact of Transformational Leadership on Employee Motivation and Job Satisfaction in the Healthcare Sector
• Evaluating the Effectiveness of Talent Management Strategies in Enhancing Organizational Performance
• A Study of the Effects of Customer Relationship Management on Customer Retention and Loyalty
• Investigating the Impact of Corporate Governance on Firm Performance: Evidence from Emerging Markets
• The Relationship between Intellectual Capital and Firm Performance: A Case Study of Technology Firms
• Analyzing the Effectiveness of Diversity Management in Improving Organizational Performance
• The Impact of Internationalization on the Performance of Small and Medium-sized Enterprises: A Comparative Study of Developed and Developing Countries
• Examining the Relationship between Corporate Social Responsibility and Financial Performance: A Study of Listed Firms
• Investigating the Influence of Entrepreneurial Orientation on Firm Performance in Emerging Markets
• Analyzing the Impact of E-commerce on Traditional Retail Business Models: A Study of Brick-and-Mortar Stores
• The Effect of Corporate Reputation on Customer Loyalty and Firm Performance: A Study of the Banking Sector
• Investigating the Factors Affecting Consumer Adoption of Mobile Payment Systems
• The Role of Corporate Social Responsibility in Attracting and Retaining Millennial Employees
• Analyzing the Impact of Social Media Marketing on Brand Awareness and Consumer Purchase Intentions
• A Study of the Effects of Employee Training and Development on Job Performance
• Investigating the Relationship between Corporate Culture and Employee Turnover: A Study of Multinational Companies
• The Impact of Business Process Reengineering on Organizational Performance: A Study of Service Industries
• An Empirical Study of the Factors Affecting Internationalization Strategies of Small and Medium-sized Enterprises
• The Effect of Strategic Human Resource Management on Firm Performance: A Study of Manufacturing Firms
• Investigating the Influence of Leadership on Organizational Culture: A Comparative Study of Two Organizations
• The Impact of Technology Adoption on Organizational Productivity: A Study of the Healthcare Sector
• Analyzing the Effects of Brand Personality on Consumer Purchase Intentions: A Study of Luxury Brands
• The Relationship between Corporate Social Responsibility and Customer Perceptions of Product Quality: A Study of the Food and Beverage Industry
• Investigating the Effectiveness of Performance Management Systems in Improving Employee Performance: A Study of a Public Sector Organization
• The Impact of Business Ethics on Firm Performance: A Study of the Banking Industry
• Examining the Relationship between Employee Engagement and Customer Satisfaction in the Service Industry
• Investigating the Influence of Entrepreneurial Networking on Firm Performance: A Study of Small and Medium-sized Enterprises
• The Effect of Corporate Social Responsibility on Employee Retention: A Study of High-tech Firms
• The impact of workplace communication on employee engagement
• The role of customer feedback in improving service quality
• The effects of employee empowerment on job satisfaction
• The impact of innovation on customer satisfaction
• The role of knowledge management in organizational learning
• The effects of product innovation on market share
• The impact of business location on customer behavior
• The role of financial management in business success
• The effects of corporate social responsibility on employee engagement
• The impact of cultural intelligence on cross-cultural communication
• The role of social media in crisis management
• The effects of corporate branding on customer loyalty
• The impact of globalization on consumer behavior
• The role of emotional intelligence in leadership effectiveness
• The effects of employee involvement in decision-making on job satisfaction
• The impact of business strategy on market share
• The role of corporate culture in promoting ethical behavior
• The effects of corporate social responsibility on investor behavior
• The impact of sustainability on brand image and reputation
• The role of corporate social responsibility in reducing carbon emissions.
• The effectiveness of loyalty programs on customer retention
• The benefits of remote work for employee productivity
• The impact of environmental sustainability on consumer purchasing decisions
• The role of brand identity in consumer loyalty
• The relationship between employee satisfaction and customer satisfaction
• The impact of e-commerce on traditional brick-and-mortar stores
• The effectiveness of online advertising on consumer behavior
• The impact of leadership styles on employee motivation
• The role of corporate social responsibility in brand perception
• The impact of workplace diversity on organizational performance
• The effectiveness of gamification in employee training programs
• The impact of pricing strategies on consumer behavior
• The effectiveness of mobile marketing on consumer engagement
• The impact of emotional intelligence on leadership effectiveness
• The role of customer service in consumer loyalty
• The impact of technology on supply chain management
• The effectiveness of employee training programs on job performance
• The impact of culture on consumer behavior
• The effectiveness of performance appraisal systems on employee motivation
• The impact of social responsibility on organizational performance
• The role of innovation in business success
• The impact of ethical leadership on organizational culture
• The effectiveness of cross-functional teams in project management
• The impact of government regulations on business operations
• The role of strategic planning in business growth
• The impact of emotional intelligence on team dynamics
• The effectiveness of supply chain management on customer satisfaction
• The impact of workplace culture on employee satisfaction
• The role of employee engagement in organizational success
• The impact of globalization on organizational culture
• The effectiveness of virtual teams in project management
• The impact of employee turnover on organizational performance
• The role of corporate social responsibility in talent acquisition
• The impact of technology on employee training and development
• The effectiveness of knowledge management on organizational learning
• The impact of organizational structure on employee motivation
• The role of innovation in organizational change
• The impact of cultural intelligence on global business operations
• The effectiveness of marketing strategies on brand perception
• The impact of change management on organizational culture
• The role of leadership in organizational transformation
• The impact of employee empowerment on job satisfaction
• The effectiveness of project management methodologies on project success
• The impact of workplace communication on team performance
• The role of emotional intelligence in conflict resolution
• The impact of employee motivation on job performance
• The effectiveness of diversity and inclusion initiatives on organizational performance.
• The impact of social media on consumer behavior and buying decisions
• The role of diversity and inclusion in corporate culture and its effects on employee retention and productivity
• The effectiveness of remote work policies on job satisfaction and work-life balance
• The impact of customer experience on brand loyalty and revenue growth
• The effects of environmental sustainability practices on corporate reputation and financial performance
• The role of corporate social responsibility in consumer purchasing decisions
• The effectiveness of leadership styles on team performance and productivity
• The effects of employee motivation on job performance and turnover
• The impact of technology on supply chain management and logistics efficiency
• The role of emotional intelligence in effective leadership and team dynamics
• The impact of artificial intelligence and automation on job displacement and workforce trends
• The effects of brand image on consumer perception and purchasing decisions
• The role of corporate culture in promoting innovation and creativity
• The impact of e-commerce on traditional brick-and-mortar retail businesses
• The effects of corporate governance on financial reporting and transparency
• The effectiveness of performance-based compensation on employee motivation and productivity
• The impact of online reviews and ratings on consumer trust and brand reputation
• The effects of workplace diversity on innovation and creativity
• The impact of mobile technology on marketing strategies and consumer behavior
• The role of emotional intelligence in customer service and satisfaction
• The effects of corporate reputation on financial performance and stakeholder trust
• The impact of artificial intelligence on customer service and support
• The role of organizational culture in promoting ethical behavior and decision-making
• The effects of corporate social responsibility on employee engagement and satisfaction
• The impact of employee turnover on organizational performance and profitability
• The role of customer satisfaction in promoting brand loyalty and advocacy
• The effects of workplace flexibility on employee morale and productivity
• The impact of employee wellness programs on absenteeism and healthcare costs
• The role of data analytics in business decision-making and strategy formulation
• The effects of brand personality on consumer behavior and perception
• The impact of social media marketing on brand awareness and customer engagement
• The role of organizational justice in promoting employee satisfaction and retention
• The effects of corporate branding on employee motivation and loyalty
• The impact of online advertising on consumer behavior and purchasing decisions
• The role of corporate entrepreneurship in promoting innovation and growth
• The effects of cultural intelligence on cross-cultural communication and business success
• The impact of workplace diversity on customer satisfaction and loyalty
• The role of ethical leadership in promoting employee trust and commitment
• The effects of job stress on employee health and well-being
• The impact of supply chain disruptions on business operations and financial performance
• The role of organizational learning in promoting continuous improvement and innovation
• The effects of employee engagement on customer satisfaction and loyalty
• The impact of brand extensions on brand equity and consumer behavior
• The role of strategic alliances in promoting business growth and competitiveness
• The effects of corporate transparency on stakeholder trust and loyalty
• The impact of digital transformation on business models and competitiveness
• The role of business ethics in promoting corporate social responsibility and sustainability
• The effects of employee empowerment on job satisfaction and organizational performance.
• The role of corporate governance in mitigating unethical behavior in multinational corporations.
• The effects of cultural diversity on team performance in multinational corporations.
• The impact of corporate social responsibility on consumer loyalty and brand reputation.
• The relationship between organizational culture and employee engagement in service industries.
• The impact of globalization on the competitiveness of small and medium enterprises (SMEs).
• The effectiveness of performance-based pay systems on employee motivation and productivity.
• The relationship between innovation and corporate performance in the pharmaceutical industry.
• The impact of digital marketing on the traditional marketing mix.
• The role of emotional intelligence in leadership effectiveness in cross-cultural teams.
• The relationship between corporate social responsibility and financial performance in the banking sector.
• The impact of diversity management on employee satisfaction and retention in multinational corporations.
• The relationship between leadership style and organizational culture in family-owned businesses.
• The impact of e-commerce on supply chain management.
• The effectiveness of training and development programs on employee performance in the retail sector.
• The impact of global economic trends on strategic decision-making in multinational corporations.
• The relationship between ethical leadership and employee job satisfaction in the healthcare industry.
• The impact of employee empowerment on organizational performance in the manufacturing sector.
• The relationship between corporate social responsibility and employee well-being in the hospitality industry.
• The impact of artificial intelligence on customer service in the banking industry.
• The relationship between emotional intelligence and employee creativity in the technology industry.
• The impact of big data analytics on customer relationship management in the telecommunications industry.
• The relationship between organizational culture and innovation in the automotive industry.
• The impact of internationalization on the performance of SMEs in emerging markets.
• The effectiveness of performance appraisal systems on employee motivation and retention in the public sector.
• The relationship between diversity management and innovation in the pharmaceutical industry.
• The impact of social entrepreneurship on economic development in developing countries.
• The relationship between transformational leadership and organizational change in the energy sector.
• The impact of online customer reviews on brand reputation in the hospitality industry.
• The effectiveness of leadership development programs on employee engagement in the finance industry.
• The relationship between corporate social responsibility and employee turnover in the retail sector.
• The impact of artificial intelligence on the recruitment and selection process in the technology industry.
• The relationship between organizational culture and employee creativity in the fashion industry.
• The impact of digital transformation on business models in the insurance industry.
• The relationship between employee engagement and customer satisfaction in the service industry.
• The impact of mergers and acquisitions on organizational culture and employee morale.
• The effectiveness of knowledge management systems on organizational performance in the consulting industry.
• The impact of social media marketing on brand loyalty in the food and beverage industry.
• The relationship between emotional intelligence and customer satisfaction in the airline industry.
• The impact of blockchain technology on supply chain management in the logistics industry.
• The relationship between corporate social responsibility and employee engagement in the technology industry.
• The impact of digitalization on talent management practices in the hospitality industry.
• The effectiveness of reward and recognition programs on employee motivation in the manufacturing industry.
• The impact of industry 4.0 on organizational structure and culture in the aerospace industry.
• The relationship between leadership style and team performance in the construction industry.
• The impact of artificial intelligence on financial forecasting and decision-making in the banking sector.
• The relationship between corporate social responsibility and customer loyalty in the automotive industry.
• The impact of virtual teams on employee communication and collaboration in the pharmaceutical industry.
• The impact of remote work on employee productivity and job satisfaction
• The effects of social media marketing on customer engagement and brand loyalty
• The role of artificial intelligence in streamlining supply chain management
• The effectiveness of employee training and development programs on organizational performance
• The impact of diversity and inclusion initiatives on organizational culture and employee satisfaction
• The role of corporate social responsibility in enhancing brand reputation and customer loyalty
• The effects of e-commerce on small businesses and local economies
• The impact of big data analytics on marketing strategies and customer insights
• The effects of employee empowerment on organizational innovation and performance
• The impact of globalization on the hospitality industry
• The effects of corporate governance on organizational performance and financial outcomes
• The role of customer satisfaction in driving business growth and profitability
• The impact of artificial intelligence on financial forecasting and risk management
• The effects of corporate culture on employee engagement and retention
• The role of green marketing in promoting environmental sustainability and brand reputation
• The impact of digital transformation on the retail industry
• The effects of employee motivation on job performance and organizational productivity
• The role of customer experience in enhancing brand loyalty and advocacy
• The impact of international trade agreements on global business practices
• The effects of artificial intelligence on customer service and support
• The role of organizational communication in facilitating teamwork and collaboration
• The impact of corporate social responsibility on employee motivation and retention
• The effects of global economic instability on business decision-making
• The role of leadership styles in organizational change management
• The impact of social media influencers on consumer behavior and purchasing decisions
• The effects of employee well-being on organizational productivity and profitability
• The role of innovation in driving business growth and competitive advantage
• The impact of digital marketing on consumer behavior and brand perception
• The role of strategic planning in organizational success and sustainability
• The impact of e-commerce on consumer privacy and data security
• The effects of corporate reputation on customer acquisition and retention
• The role of diversity and inclusion in organizational creativity and innovation
• The impact of artificial intelligence on customer relationship management
• The effects of customer feedback on product development and innovation
• The role of employee job satisfaction in reducing turnover and absenteeism
• The impact of global competition on business strategy and innovation
• The effects of corporate branding on customer loyalty and advocacy
• The role of digital transformation in enhancing organizational agility and responsiveness
• The effects of employee empowerment on customer satisfaction and loyalty
• The role of entrepreneurial leadership in driving business innovation and growth
• The impact of digital disruption on traditional business models
• The effects of organizational culture on innovation and creativity
• The role of marketing research in developing effective marketing strategies
• The impact of social media on customer relationship management
• The effects of employee engagement on organizational innovation and competitiveness
• The role of strategic partnerships in promoting business growth and expansion
• The impact of global trends on business innovation and entrepreneurship

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr  Nam Ho-Nguyen

Dr  Stephen Tierney

Dr  Chao Wang

Dr Wilson Chen

Dr  Bern Conlon

Dr Qin Fang

Dr  Simon Loria

Dr  Pablo Montero-Manso

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

• Date: 2 Jun 2017 at 11am
• Speaker: Professor Wray Buntine, Master of Data Science, Monash University

Optimisation and equilibrium problems in engineering

• Date: 26 May 2017 at 11am
• Speaker: Prof Steven Gabriel, Department of Mechanical Engineering, University of Maryland

Exact subsampling MCMC

• Date: 12 May 2017 at 11am
• Speaker: Dr Matias Quiroz, UNSW Business School, University of New South Wales

Effects of taxes and safety net pensions on life-cycle labor supply, savings and human capital: The case of Australia

• Date: 21 Apr 2017 at 11am
• Speaker: Dr Fedor Iskhakov, College of Business and Economics, Australian National University

Trial-offer markets with social influence: The impact of different ranking policies

• Date: 18 Apr 2017 at 11am
• Venue: Rm 5040, Abercrombie Building (H70)
• Speaker: Dr Gerardo Berbeglia, Melbourne Business School, University of Melbourne

Conditionally optimal weights and forward-looking approaches to combining forecasts

• Date: 7 Apr 2017 at 11am
• Speaker: Dr Andrey Vasnev, Discipline of Business Analytics, The University of Sydney

A flexible generalised hyberbolic option pricing model and its special cases

• Date: 31 Mar 2017 at 11am
• Speaker: Dr Simon Kwok, School of Economics, The University of Sydney

Scheduling with variable processing times: Complexity results and approximation algorithms

• Date: 24 Mar 2017 at 11:00am
• Speaker: Associate Professor Daniel Oron, Discipline of Business Analytics, The University of Sydney

Modelling insurance losses using contaminated generalised beta type-2 distribution

• Date: 17 Mar 2017 at 11am
• Speaker: Dr Boris Choy, Discipline of Business Analytics, The University of Sydney

How (not) to get what you ask for: Survey mode effects on self-reported substance use

• Date: 24 Feb 2017 at 11am
• Speaker: Dr Bin Peng, School of Mathematics, University of Technology Sydney

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Postgraduate research, business analytics working papers.

A list of all our research working papers, from 1975-present.

• University of Sydney eScholarship Repository

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