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

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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.

List of Business Analytics Projects [Based on Levels]

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

Business Analytics Project Ideas : 

• Sales Data Analysis
• Customer Review Sentiment Analysis
• Market Basket Analysis
• Price Optimization
• Stock Market Data Analysis
• Customer Segmentation
• Fraud Detection
• Equity Research
• Social Media Reputation Monitoring
• Real-Time Pollution Analysis

Business Analytics Project Ideas for Beginners: 

• Employee Attrition and Performance
• Prediction of Sales in Tourism for the Next Five Years
• Prediction of the Success of an Upcoming Movie
• Prediction of the Fate of a Loan Application

Business Analytics Projects for Intermediates: 

• Creating Product Bundles
• Life Expectancy Analysis
• Building a BI app

Business Analytics Projects Topics for MBA Students

• Predicting Customer Churn Rate
• Prediction of Selling Prices for Different Products
• Store Sales Prediction

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  

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.

• Social media reputation monitoring

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

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.

Here is the 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.

Here is the Business Intelligence Analysis Source Code  

Key Tools for Business Analytics Project

Here is a list of top tools that are required business analytics projects: 

• Data Visualization Tools (e.g., Microsoft Power BI, Looker) 
• ETL/ELT Tools
• Data Warehousing (e.g., Amazon Redshift, Google BigQuery, etc.)
• Data Analysis and Manipulation Tools
• Data Mining and Machine Learning Tools (e.g., Scikit-learn, RapidMiner)
• Data Quality Management Tools
• Data Integration Tools
• BI Suites 
• Data Catalog Tools (e.g., Collibra)

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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10 Great Business Analyst Projects for Your Portfolio (2024)

If you're serious about launching a career as a business analyst, you'll need more than just certificates and an impressive resume. To stand out in a competitive job market, you need to showcase your skills through a portfolio of relevant business analyst projects. Building a project portfolio is essential for two key reasons:

• Practice makes perfect : Completing end-to-end business analyst projects allows you to apply your skills to solve real-world challenges. While exercises and case studies are helpful, hands-on projects provide the depth of experience that employers value. Project-based learning offers an effective approach for aspiring business analysts to gain practical, job-ready skills by bridging the gap between theory and practice.
• Prove your capabilities : When you're applying for business analyst roles , a strong portfolio is your secret weapon. It demonstrates your ability to tackle complex problems, collaborate with stakeholders, and deliver impactful solutions. A portfolio of hands-on projects is essential for aspiring business analysts to land a job by demonstrating their ability to apply skills to real-world problems, particularly when they have no experience .

What to expect : Before we get into any specific project details, we'll first take a look at how you should approach selecting the right projects for you portfolio. Next, we'll go over the skills and knowledge you'll need to get started. Finally, we'll list 10 business analyst projects that we know will give your portfolio a real boost.

Choosing the right business analyst projects for your portfolio

With so many potential projects to choose from, it can be challenging to know where to start. To help you navigate this process, let's take a look at some key factors to consider when selecting business analyst projects for your portfolio.

• Skill level and learning goals : If you're just starting out, focus on projects that allow you to develop foundational skills such as data cleaning, data analysis, and documenting insights. As you gain more experience, you can gradually take on more complex projects that challenge you to apply advanced techniques and methodologies.
• Personal interests and passions : Selecting projects that align with your personal interests and passions can help you stay motivated and engaged throughout the learning process. For example, if you're passionate about sustainability, you might choose projects that focus on improving environmental performance or reducing waste in business processes. By working on projects that excite you, you'll be more likely to invest the time and effort needed to produce high-quality deliverables.
• In-demand skills and industry trends : To maximize the impact of your portfolio, it's important to choose projects that showcase in-demand skills and align with current industry trends. Research job postings and industry reports to identify the skills and competencies that employers are looking for in business analysts. For instance, with the growing importance of data-driven decision making, projects that demonstrate your ability to analyze and visualize data using tools like Tableau or Power BI can help you stand out to potential employers .

Step-by-step guide

Now that we've explored some key factors to consider, let's walk through the process of selecting the right business analyst projects for your portfolio:

• Assess your current skill level and identify any areas for improvement
• Set clear learning goals and objectives for your project-based learning journey
• Select project ideas that align with your interests, passions, and learning goals
• Research industry trends and in-demand skills to refine your selection
• Evaluate potential projects based on factors such as scope, complexity, and required resources
• Select projects that offer the best opportunities for skill development and portfolio impact
• Plan your projects carefully, setting clear milestones and deliverables
• Execute your projects with a focus on quality, collaboration, and continuous improvement
• Seek feedback from mentors, peers, and industry professionals to refine your work
• Showcase your completed projects in your portfolio, highlighting the skills and value you bring to the table

Remember, the most effective way to learn is by doing, so don't be afraid to dive in and start tackling real-world business challenges today!

Getting started with business analyst projects

Ready to get your feet wet with business analysis projects? You'll need a mix of technical skills, business know-how, and people skills. But don't worry, you can build up these abilities through hands-on practice.

To get the ball rolling on a project, focus on nailing these key deliverables:

• Process flows
• Requirements documents

These will help you pin down the project scope and get everyone on the same page. Crafting solid versions of these docs is a core skill for business analysts.

Additional key tools:

• Data visualization : Power BI , Tableau
• Project management : Jira , Asana
• Process modeling : Visio
• Data analysis : SQL , Excel , Python *

* - While Python is more commonly associated with data analysts and data scientists due to its powerful data manipulation and analysis capabilities, business analysts are increasingly using Python for more advanced data analysis tasks. Python's ability to handle large datasets, automate repetitive tasks, and perform complex analyses makes it a valuable skill for business analysts looking to enhance their technical capabilities. Having Python skills is great way to set yourself apart from others applying for the same business analyst role.

When you're ready to tackle a project, knock out these first steps:

• Whip up a business requirements document (BRD) and project vision to get the team aligned.
• Get your tech environment set up with the right software for the job.
• Deploy the application for testing and production.

Remember, you don't have to take on the world right away. Start small and gradually level up your skills. Focus on simpler tasks at first, then work your way up to more complex challenges. This lets you grow your abilities while getting real project experience under your belt. This article has more tips on setting up your project environment. With some practice and persistence, you'll be tackling ambitious projects like a pro in no time.

Real learner, real results

Take it from Aleksey Korshuk , who leveraged Dataquest's project-based curriculum to gain practical data science skills and build an impressive portfolio of projects:

The general knowledge that Dataquest provides is easily implemented into your projects and used in practice.

Through hands-on projects, Aleksey gained real-world experience solving complex problems and applying his knowledge effectively. He encourages other learners to stay persistent and make time for consistent learning:

I suggest that everyone set a goal, find friends in communities who share your interests, and work together on cool projects. Don't give up halfway!

Aleksey's journey showcases the power of a project-based approach for anyone looking to build their data skills. By building practical projects and collaborating with others, you can develop in-demand skills and accomplish your goals, just like Aleksey did with Dataquest.

10 Business Analysis Project Ideas

The following ten project ideas provide an excellent introduction to essential business analysis techniques for beginners. You'll get to:

• Apply key concepts like data analysis, visualization, and business intelligence to real-world scenarios
• Develop a strong foundation and portfolio in the field
• Profitable App Profiles for the App Store and Google Play Markets
• Exploring Hacker News Posts
• Clean and Analyze Employee Exit Surveys
• Visualization of Life Expectancy and GDP Variation Over Time
• Building a BI App
• Business Intelligence Plots
• Data Presentation
• Creating An Efficient Data Analysis Workflow
• Identifying Customers Likely to Churn for a Telecommunications Provider
• Analyzing Startup Fundraising Deals from Crunchbase

The following sections walk through each project in detail, showing you how to apply your skills to real business challenges and drive smarter decisions. Let's go!

1. Profitable App Profiles for the App Store and Google Play Markets

In this guided project , you'll take on the role of a junior business analyst at a company that builds ad-supported mobile apps. Your job is to analyze historical data from the Apple App Store and Google Play Store to figure out what kinds of apps attract the most users and generate the most revenue for the company. Using Python and Jupyter Notebook, you'll clean up the data, analyze it to find the most common app categories and characteristics, and provide recommendations to the business on what types of apps they should focus on building to maximize downloads and profits.

Tools and Technologies

• Jupyter Notebook
• Business analysis

Prerequisites

This is a beginner-level project, but you should be comfortable working with Python Functions and Jupyter Notebook Course :

• Writing functions with arguments, return statements, and control flow
• Debugging functions to ensure proper execution
• Using conditional logic and loops within functions
• Working with Jupyter Notebook to write and run code

Step-by-Step Instructions

• Open and explore the App Store and Google Play datasets
• Clean the datasets by removing non-English apps and duplicate entries
• Isolate the free apps for further analysis
• Determine the most common app genres and their characteristics using frequency tables
• Make recommendations on the ideal app profiles to maximize users and revenue

Expected Outcomes

By completing this project, you'll gain practical experience and valuable skills, including:

• Cleaning real-world data to prepare it for analysis
• Analyzing app market data to identify trends and success factors
• Applying data analysis techniques like frequency tables and calculating averages
• Using data insights to inform business strategy and decision-making
• Communicating your findings and recommendations to stakeholders

Relevant Links and Resources

• Python Functions and Jupyter Notebook Course
• How to Use Jupyter Notebook: A Beginner's Tutorial
• Data Analysis for Business in Python Course

Additional Resources

• Basic Data Science Portfolio Project Tutorial
• Dataquest community where you can view and share this project with others
• Example Solution Code

2. Exploring Hacker News Posts

In this project , you'll get hands-on experience analyzing a real-world dataset from Hacker News, a popular website in the tech community. You'll learn how to use Python, a powerful programming language, to uncover trends and insights that can help drive business decisions. Even if you're new to data analysis, this project will walk you through key skills step-by-step, including cleaning messy data, calculating key metrics, and identifying factors that impact user engagement. By the end, you'll have a strong foundation in data analysis and be ready to apply your skills to your own business data.

• Data cleaning
• Object-oriented programming

To get the most out of this project, you should have some foundational Python and data cleaning skills , such as:

• Employing loops in Python to explore CSV data
• Utilizing string methods in Python to clean data for analysis
• Processing dates from strings using the datetime library
• Formatting dates and times for analysis using strftime
• Remove headers from a list of lists
• Extract 'Ask HN' and 'Show HN' posts
• Calculate the average number of comments for 'Ask HN' and 'Show HN' posts
• Find the number of 'Ask HN' posts and average comments by hour created
• Sort and print values from a list of lists

After completing this project, you'll have gained practical experience and skills, including:

• Applying Python string manipulation, OOP, and date handling to real-world data
• Analyzing trends and patterns in user submissions on Hacker News
• Identifying factors that contribute to post popularity and engagement
• Communicating insights derived from data analysis
• Introduction to Python Programming Course
• Original Hacker News Posts dataset on Kaggle

3. Clean and Analyze Employee Exit Surveys

In this guided project , you'll get hands-on experience analyzing real employee data to understand why employees leave their jobs. You'll work with exit surveys from two government education departments in Australia. Using Python and Jupyter Notebook, you'll combine messy data from different sources, clean it up to get it ready for analysis, and then look for insights into the most common reasons employees resign. Finally, you'll share your key findings and recommendations with stakeholders. Doing this project will give you practice with the data cleaning, analysis, and communication skills you need as a business analyst to help organizations make data-driven decisions.

Before starting this project, you should be familiar with:

• Exploring and analyzing data using pandas
• Aggregating data with pandas groupby operations
• Combining datasets using pandas concat and merge functions
• Manipulating strings and handling missing data in pandas
• Load and explore the DETE and TAFE exit survey data
• Identify missing values and drop unnecessary columns
• Clean and standardize column names across both datasets
• Filter the data to only include resignation reasons
• Verify data quality and create new columns for analysis
• Combine the cleaned datasets into one for further analysis
• Analyze the cleaned data to identify trends and insights

By completing this project, you will:

• Clean real-world, messy HR data to prepare it for analysis
• Apply core data cleaning techniques in Python and pandas
• Combine multiple datasets and conduct exploratory analysis
• Analyze employee exit surveys to understand key drivers of resignations
• Summarize your findings and share data-driven recommendations
• Pandas and NumPy Fundamentals Course
• Community Feedback on Guided Project

4. Visualization of Life Expectancy and GDP Variation Over Time

In this project , you'll get to be a business analyst exploring how life expectancy and GDP have changed in different parts of the world over time. You'll use Power BI to create interactive charts and graphs from a dataset called Gapminder. This will let you uncover trends and differences between regions that can provide valuable insights to help make business decisions. You'll go through the full process of importing and preparing the data, making visualizations, and sharing your findings in an engaging dashboard. This is great practice with core business analyst skills in Power BI that you can showcase in your portfolio.

To complete this project, you should be able to visualize data in Power BI , such as:

• Creating basic Power BI visuals
• Designing accessible report layouts
• Customizing report themes and visual markers
• Publishing Power BI reports and dashboards
• Import the life expectancy and GDP data into Power BI
• Clean and transform the data for analysis
• Create interactive scatter plots and stacked column charts
• Design an accessible report layout in Power BI
• Customize visual markers and themes to enhance insights
• Applying data cleaning, transformation, and visualization techniques in Power BI
• Creating interactive scatter plots and stacked column charts to uncover data insights
• Developing an engaging dashboard to showcase your data visualization skills
• Practicing the full life-cycle of Power BI report and dashboard development
• Introduction to Power BI
• Official Power BI Support
• Official Power BI documentation
• Why Business Analysts Need to Learn Power BI
• Business Analyst with Power BI career path

5. Building a BI App

In this hands-on project , you'll get to be a business analyst at Dataquest, an online learning company. You'll use Power BI to look at data about how many students finish each course and how happy they are with the courses. You'll make charts and graphs to find patterns and figure out which courses need improvement. This will help Dataquest's leaders make smart choices about how to make their courses better for students.

To successfully complete this project, you should have some foundational skills in Power BI, such as how to manage workspaces and datasets in Power BI :

• Creating and managing workspaces
• Importing and updating assets within a workspace
• Developing dynamic reports using parameters
• Implementing static and dynamic row-level security
• Import and explore the course completion and NPS data, looking for data quality issues
• Create a data model relating the fact and dimension tables
• Write calculations for key metrics like completion rate and NPS, and validate the results
• Design and build visualizations to analyze course performance trends and comparisons

Upon completing this project, you'll have gained valuable skills and experience:

• Importing, modeling, and analyzing data in Power BI to drive decisions
• Creating calculated columns and measures to quantify key metrics
• Designing and building insightful data visualizations to convey trends and comparisons
• Developing impactful reports and dashboards to summarize findings
• Sharing data stories and recommending actions via Power BI apps
• Introduction to Data Analysis in Microsoft Power BI Course
• What’s the best way to learn Microsoft Power BI?
• Sample datasets from Power BI

6. Business Intelligence Plots

In this beginner-friendly project , you'll get hands-on experience using Tableau to analyze sales data and provide valuable business insights. You'll compare Adventure Works' online and in-store sales, identify top-selling products, and build interactive dashboards to effectively communicate your findings. Along the way, you'll learn key Tableau skills like creating calculated fields, filtering data, and designing dual-axis charts. By the end, you'll have a professional set of visualizations to showcase to leadership and guide data-driven decision making.

To successfully complete this project, you should have a solid grasp of data visualization fundamentals in Tableau :

• Navigating the Tableau interface and distinguishing between dimensions and measures
• Constructing various foundational chart types in Tableau
• Developing and interpreting calculated fields to enhance analysis
• Employing filters to improve visualization interactivity
• Compare online vs offline orders using visualizations
• Analyze products across channels with scatter plots
• Embed visualizations in tooltips for added insight
• Summarize findings and identify next steps
• Practical experience building interactive business intelligence dashboards in Tableau
• Ability to create calculated fields to conduct tailored analysis
• Understanding of how to use filters and tooltips to enable data exploration
• Skill in developing visualizations that surface actionable insights for stakeholders
• Business Analyst with Tableau Career Path
• Data Preparation in Tableau Course
• Data Visualization Fundamentals in Tableau Course
• Tableau Community Forums
• Tableau Documentation

7. Data Presentation

In this project , you'll take on the role of a business analyst exploring customer data for a company. Using Tableau, you'll create interactive dashboards to uncover insights about which marketing channels and customer types are driving the most sales. You'll apply data visualization best practices to build professional dashboards that allow users to filter and explore the data. By the end, you'll have a polished data presentation ready to share your findings with business stakeholders to help guide decision making.

To successfully complete this project, you should be comfortable sharing insights in Tableau , such as:

• Building basic charts like bar charts and line graphs in Tableau
• Employing color, size, trend lines and forecasting to emphasize insights
• Combining charts, tables, text and images into dashboards
• Creating interactive dashboards with filters and quick actions
• Import and clean the conversion funnel data in Tableau
• Build basic charts to visualize key metrics
• Create interactive dashboards with filters and actions
• Add annotations and highlights to emphasize key insights
• Compile a professional dashboard to present findings

Upon completing this project, you'll have gained practical experience and valuable skills, including:

• Analyzing conversion funnel data to surface actionable insights
• Visualizing trends and comparisons using Tableau charts and graphs
• Applying data visualization best practices to create impactful dashboards
• Adding interactivity to enable exploration of the data
• Communicating data-driven findings and recommendations to stakeholders
• Example Solution

8. Creating An Efficient Data Analysis Workflow

In this hands-on project , you'll take on the role of a business analyst at a company that sells programming books. Your goal is to analyze sales data and figure out which books are generating the most profit. You'll use key concepts in R like control flow, loops, and functions to develop a streamlined process for cleaning, transforming and analyzing the data. This project will give you valuable practice in preparing data, uncovering insights, and putting together a structured report with your findings and recommendations that will help drive business decisions.

To successfully complete this project, you should have the following foundational control flow, iteration, and functions in R skills:

• Implementing control flow using if-else statements
• Employing for loops and while loops for iteration
• Writing custom functions to modularize code
• Combining control flow, loops, and functions in R
• Get acquainted with the provided book sales dataset
• Transform and prepare the data for analysis
• Analyze the cleaned data to identify top performing titles
• Summarize your findings in a structured report
• Provide data-driven recommendations to stakeholders
• Applying R programming concepts to real-world business analysis
• Developing an efficient, reproducible business analysis workflow
• Cleaning and preparing messy data for analysis
• Analyzing sales data to derive actionable business insights
• Communicating findings and recommendations to stakeholders
• Getting Started with R and RStudio - Dataquest Blog
• Download the Book Reviews dataset - CSV file

9. Identifying Customers Likely to Churn for a Telecommunications Provider

In this beginner project , you'll take on the role of a business analyst at a telecommunications company. Your challenge is to analyze customer data in Excel to identify profiles of those likely to leave the company. Keeping customers is crucial for telecom providers, so your insights will help inform efforts to proactively retain them. You'll explore the data, calculate key metrics, use PivotTables to slice the data, and create charts to visualize your findings. This project provides hands-on experience with core Excel skills for making data-driven business decisions that will enhance your business analyst portfolio.

To complete this project, you should feel comfortable exploring data in Excel :

• Calculating descriptive statistics in Excel
• Analyzing data with descriptive statistics
• Creating PivotTables in Excel to explore and analyze data
• Visualizing data with histograms and boxplots in Excel
• Import the customer dataset into Excel
• Calculate descriptive statistics for key metrics
• Create PivotTables, histograms, and boxplots to explore data differences
• Analyze and identify profiles of likely churners
• Compile a report with your data visualizations
• Hands-on practice analyzing a real-world customer dataset in Excel
• Ability to calculate and interpret key statistics to profile churn risks
• Experience building PivotTables and charts to slice data and uncover insights
• Skill in translating business analysis findings into an actionable report for stakeholders
• Customer Churn Prediction 2020 dataset - Kaggle
• Introduction to Data Analysis with Excel Skill Path

10. Analyzing Startup Fundraising Deals from Crunchbase

In this beginner-level guided project , you'll take on the role of a business analyst to explore and derive insights from a dataset of startup investments from Crunchbase. By applying fundamental data analysis skills using Python and SQL, you'll work with a large real-world dataset to uncover trends in fundraising, identify successful startups, and find the most active investors. This project will introduce you to techniques for handling large datasets, selecting the right tools for analysis, and leveraging SQL databases. You'll build your skills in applying the data analysis process to real business scenarios and communicating insights to stakeholders.

Although this is a beginner-level SQL project, you'll need some solid skills in Python and data analysis before taking it on:

• Python fundamentals, including variables, data types, and basic syntax
• Familiarity with pandas for data manipulation and analysis
• Basics of data cleaning techniques to handle missing data and inconsistencies
• Exposure to SQL databases and querying data using SQLite
• Explore the structure and contents of the Crunchbase startup investments dataset
• Process the large dataset in chunks and load into an SQLite database
• Analyze fundraising rounds data to identify trends and derive insights
• Examine the most successful startup verticals based on total funding raised
• Identify the most active investors by number of deals and total amount invested

Upon completing this guided project, you'll gain practical skills and experience, including:

• Applying pandas and SQLite to analyze real-world startup investment data
• Handling large datasets effectively through chunking and efficient data types
• Integrating pandas DataFrames with SQL databases for scalable data analysis
• Deriving actionable insights from fundraising data to understand startup success
• Building a project for your portfolio showcasing pandas and SQLite skills
• Junior Data Analyst Career Path
• SQL Fundamentals Skill Path
• Dataquest Community Discussion on the Project
• SQL Commands: The Complete List (w/ Examples)

How to prepare for a career as a business analyst

Starting your path to becoming a business analyst means knowing what employers are looking for in terms of qualifications, knowledge, and skills. This section will help you navigate that path.

Check out job listings

Begin by browsing current job listings to see what qualifications, knowledge, and skills are in demand. Trusted sites to look for business analyst positions include:

Get ready for success

Here are some steps to help you succeed in your business analyst career:

• Learn the necessary skills : Gain essential technical skills with programs like Dataquest's Business Analyst with Power BI career path.
• Work on real projects : Get practical experience by applying your knowledge to business analyst projects, as detailed in this article.
• Polish your resume : Highlight your achievements and quantify the impact of your projects on your resume. We can help you to optimize your business analyst resume .
• Prepare for interviews : Practice common business analyst interview questions using our guide on 20 interview questions and answers for business analysts .

Showcase your work

Creating a GitHub portfolio of business analyst projects is a great way to show potential employers your problem-solving skills and project management abilities. Include a variety of projects that demonstrate different skills and levels of complexity.

When to start applying

Don’t wait to have every skill listed in job postings before you start applying. Aim for about 70-80% of the required skills, as many employers value potential and the ability to learn on the job. Project-based learning is a practical way to bridge the gap between theory and real-world skills.

By building the right skills, getting relevant certifications, and showcasing your work, you'll be in a great position to land a rewarding job in this field. Remember, every business analyst started somewhere – just keep pushing forward and you'll get there!

If you're aiming to stand out as a business analyst, getting hands-on with real projects is the way to go.

By doing the actual business analysis work, you’ll pick up crucial skills like gathering requirements, managing stakeholders, and visualizing data. Plus, you'll have a solid portfolio to show potential employers, proving you know how to go from theory to practice.

If you’re looking for a structured way to get there, consider our Business Analyst with Power BI and Business Analyst with Tableau career paths. They’ll give you the specific tools and skills you need to succeed.

But if you’re confident in charting your own path, the projects we've shared in this post will definitely help. Keep pushing yourself, take on more challenges, and share your work in the Dataquest community for feedback. The more you practice and apply your knowledge, the more you'll grow.

More learning resources

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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 .

Find your bootcamp match

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!"

Venus, Software Engineer at Rockbot

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. 

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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37 Data Analytics Project Ideas and Datasets (2024 UPDATE)

Introduction.

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.

13. Delivery Time Estimator

This take-home exercise - which requires 5-6 hours to complete - is a two-part task involving both machine learning model development and application engineering. You’re tasked with building a model to predict delivery times based on historical data, followed by writing an application to make predictions using this model. An exercise like this is excellent practice for the type of challenges that are typically given to machine learning engineers and data scientists.

As you work through this exercise, focus specifically on the evaluation criteria, which include:

• The performance of your model on the test data set.
• The feature engineering choices and data processing techniques you employ.
• The clarity and thoroughness of your explanations and write-up.
• Your ability to write modular, well-documented, and production-ready code for the prediction application.

14. Trucking in High Winds

This take-home exercise - which is intended to take 2-3 hours to complete - is focused on estimating the mean distance to failure for wind-induced rollover events on a specified route. You’re asked to analyze historical weather data to assess the frequency of high wind events and to use this information to estimate the risk of rollover incidents. A task like this is good practice for the type of data-driven safety analyses that are relevant to data science roles in the logistics and transportation industry.

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:

15. 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.

16. 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.

17. 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.

18. 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 .

19. 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.

20. 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.

21. 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.

22. 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?

23. 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 .

24. 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!

25. 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.

26. 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.

27. 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.

28. 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 .

29. 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.

30. 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.

31. 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.

32. 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.

33. 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.

34. 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.

35. 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.

36. 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.

37. 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 .

You can also check out our blog for more resources like:

How to Get a Data Science Internship

How Hard Is It to Get a Google Internship?

Highest Paying Data Science Jobs

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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Table of Contents

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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• Top 10 skills in demand Business Analysis As A Skill In 2020
• 14% Growth in Jobs Of Business Analysis Profile By 2028

Business Analyst

• Industry-recognized certifications from IBM and Simplilearn
• Masterclasses from IBM experts

Post Graduate Program in Business Analysis

• Get mentored and network with industry experts from Amazon, Microsoft, and Google
• Access Harvard Business Publishing case studies of Pearson, CarMax, EvCard, etc

Here's what learners are saying regarding our programs:

Assistant Consultant at Tata Consultancy Services , Tata Consultancy Services

My experience with Simplilearn has been great till now. They have good materials to start with, and a wide range of courses. I have signed up for two courses with Simplilearn over the past 6 months, Data Scientist and Agile and Scrum. My experience with both is good. One unique feature I liked about Simplilearn is that they give pre-requisites that you should complete, before a live class, so that you go there fully prepared. Secondly, there support staff is superb. I believe there are two teams, to cater to the Indian and US time zones. Simplilearn gives you the most methodical and easy way to up-skill yourself. Also, when you compare the data analytics courses across the market that offer web-based tutorials, Simplilearn, scores over the rest in my opinion. Great job, Simplilearn!

I was keenly looking for a change in my domain from business consultancy to IT(Business Analytics). This Post Graduate Program in Business Analysis course helped me achieve the same. I am proficient in business analysis now and am looking for job profiles that suit my skill set.

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.

Are you looking for career growth in Business Analysis? Sign-up for our exclusive Professional Certificate Program In Business Analysis course and solve complex business problems quickly!

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.

Our Business And Leadership Courses Duration And Fees

Business And Leadership Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Recommended Reads

Business Intelligence Career Guide: Your Complete Guide to Becoming a Business Analyst

Business Analyst Interview Questions

How to Become a Business Analyst

Data Analyst Resume Guide

Role of a Business Analyst

Top Business Analyst Skills

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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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10 Unique Business Intelligence Projects with Source Code 2024

Check out this list of unique business intelligence projects with source code ideas to get started with the exciting domain of business intelligence.

Chilly December is here! And we do want our curious readers to feel warm in their blankets and conserve their energy when searching for projects on business intelligence. Read this blog if you are interested in exploring business intelligence projects examples that highlight different strategies for increasing business growth.

Business Intelligence refers to the toolkit of techniques that leverage a firm’s data to understand the overall architecture of the business. This understanding is achieved by using data visualization , data mining , data analytics , data science, etc. methodologies. And, as data is one of the most prized possessions for any business in the twenty-first century, the demand for business intelligence (BI) experts is rising. In fact, as per a report by the Bureau of Labor Statistics, the jobs for BI analysts are expected to rise by 14% between the years 2020 and 2030. And one can easily comprehend the statistics if one considers the various industries (law enforcement, healthcare , education, finance, and technology) that can benefit from Business Intelligence tools.

Table of Contents

Business intelligence examples highlighting the value of a bi expert, business intelligence projects for students and beginners, business intelligence and analytics projects for intermediate professionals.

With so many industries showing interest in leveraging the skills of a BI expert, let us explore a few examples that illustrate how those skills are significant in improving business strategies.

Business Intelligence in Healthcare: It has become common to use patients’ data to better diagnose diseases. Along with that, deep learning algorithms and image processing methods are also used over medical reports to support a patient’s treatment better.

Business Intelligence in Education: Ed-tech firms often use various statistical tools and methods to help students match with the teacher who makes them understand various topics at their own pace.

Business Intelligence in Finance: Banks have started using data science to fasten their loan application process. Additionally, many facilities, like passbook filling that can be complete without human intervention, have been automated.

Now that you have a fair idea of how BI methods can assist a business in making quicker and better decisions, why not check out real-world applications of business intelligence described in the next section?

Build a predictive model for Otto Group Product Classification

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10 Unique Business Intelligence Projects with Source Code for 2024

For the convenience of our curious readers, we have divided the projects on business intelligence into three categories so that they can easily pick a project on the basis of their previous experience with BI techniques.

1) Predicting Sales of a Supermarket Chain

Imagine running 1,000 stores across a country like Germany! Sounds like a huge responsibility, doesn’t it? Well, it indeed is but if you know how to utilise various BI tools, the whole process of running so many stores can be easily simplified. Project Idea: Rossmann Stores is a supermarket chain in Germany and has about 1,115 stores spread across the country. One can use their dataset to understand how they work out the whole process of the supply chain of various products and their approach towards inventory management. An analysis of their dataset will also reveal how they use data science tools and techniques to estimate their daily sales and maximise their profit.

Industry: Food and Beverages

Source Code: Rossmann Store Sales Business Intelligence Project

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2) Predicting Land Prices

Most of us believe that investing in real estate firms involves high risks. That is primarily because so many external factors like robbery, natural disasters, nearby infrastructure, etc., influence the land prices. Fortunately, with the help of relevant data visualisation tools, one can have a fair idea of such risks.

Project Idea: Use the Zillow Zestimate dataset to understand what kind of features play a crucial role in determining the prices of different houses. Additionally, use different machine learning algorithms like linear regression, decision trees, random forests, etc. to estimate the costs. You may also use principal component analysis to determine which factors are more prominent in affecting house prices.

Industry: Real Estate

Source Code: House Price Prediction Business Intelligence Project

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3) Classification of Loan Applications

Have you ever applied for a loan? If not you, at least someone close to you must-have. Recall that whenever a person applies for a loan at a bank, the bank staff collects a lot of information about the applicant. This information helps them understand whether the applicant can repay the loan or not. And, gone are the days when banks make such decisions manually, and they now rely on BI experts for that.

Project Idea: Use the German Credit Dataset available on Kaggle and apply statistical analysis like univariate, bivariate and multivariate analysis over it to understand the distribution of different variables. After that, use classification machine learning algorithms like decision trees, random forests, and logistic regression to predict the probability that an applicant will successfully repay the loan.

Industry: Banking Source Code: Business Intelligence Project-Work on German Credit Dataset (projectpro.io)

4) Estimating Retail Prices

For community-driven businesses that rely on them for products, it becomes essential to regulate their prices. The best way for such companies is to figure out a method of recommending prices to the sellers to help them with deciding the appropriate product prices.

Project Idea: Work on the dataset of one of famous Japan’s online shopping marketplace for electronics and perform Data Preprocessing techniques over it. After that, perform Exploratory Data Analysis and use machine learning models to estimate the product prices based on the information provided by the user for the product.

Industry: Retail Source Code: Machine learning for Retail Price Recommendation with R (projectpro.io)

New Projects

For this section, we have presented those business intelligence projects with source code that will guide you through upgrading your business intelligence skills by making you work on challenging problems.

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5) Time Series Forecasting

Time Series is a type of data with the distribution of variables depending on time. Such data can be used to make predictions for the future. For example, predicting the sales for a retail store can help them plan their inventory efficiently.

Project Idea: Explore one of the most popular algorithms for making predictions using time series data, the Auto-Regressive Integrated Moving Average (ARIMA) model. Use the model over the dataset of a call centre to understand various features that affect the traffic prediction for calls. Additionally, you can use ARIMAX (Auto-Regressive Integrated Moving Average Exogenous) and SARIMAX (Seasonal Auto-Regressive Integrated Moving Average with Exogenous factors) models.

Industry: Telemarketing

Source Code: Business Intelligence Project-Building ARIMA Model in Python

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6) Evaluating Recommendations for Users

While shopping online, we often see a list of products recommended to us by the websites to smoothen our browsing experience. You must have noticed this for entertainment apps like Netflix too. For evaluating appropriate recommendations, they leverage users’ data to increase engagement and traffic on their platforms.

Project Idea: Analyse the customer behaviour dataset of Expedia (a hotel booking website) and perform various feature engineering methods over it to visualise the patterns in the dataset. After cleaning the dataset, use machine learning algorithms like Naive Bayes, Random Forests, KNN to cluster similar hotels together. This clustering will help recommend a user with hotels they are likely to show interest in.

Industry: Hospitality Source Code: Data Science Project - Build Recommender Systems (projectpro.io)

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7) Building a Face Recognition System

Now, this business intelligence project idea may sound a bit off-track for this list. But, if you consider applying a face recognition system in automating attendance systems at workplaces, you will find it easy for managers to track the attendance, work hours, etc., of all the employees. We have listed this project mainly to widen your knowledge about interesting applications of data science, which will hopefully help you create an efficient business intelligence project plan for your company.

Project Idea: Create a dataset for your own company by clicking images of all the employees from different angles. Apply machine learning and deep learning algorithms over the dataset to make the system learn the facial features of all the employees. Next, incorporate a few advanced features to build an entire attendance system.

Industry: Technology Source Code: Build a Face Recognition System in Python using FaceNet

Recommended Reading: 15+ Computer Vision Project Ideas for Beginners in 2021

8) Exploring MS Excel as a BI Tool

Microsoft’s Excel is a powerful business intelligence tool that most learners neglect initially. However, professionals understand its value and are often found using it to solve complicated business problems through this easy-to-use software. As an intermediate professional, we highly recommend that you work on solving a challenging problem using MS Excel .

Project Idea: Analyse the dataset of AssureNext, a company that is placing machines at specific book stores and restaurants, to help the customers pick the best insurance plan for them. Create data connections, scorecards, status lists, dashboards, status indicators, etc. in Excel to help the company understand their customers better and possible amendments that will help them increase their revenue.

Industry: Financial Services

Source Code: Business Intelligence Project on Insurance Domain using Excel

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9) Implementing the FEAST Operational System

FEAST is an operational data system for handling and providing machine learning features to models in production. It solves the following problems that are encountered by companies every day:

Continuous access to data in modelling

Implementing new features into production

Point-in-time correct data for models

Makes it easy to use the features across the project again and again.

Project Idea: Use the customer churn dataset to understand the FEAST architecture and benefit from its features. Analyse different features and perform Model training in FEAST. Additionally, explore the full interactive deployment through this interesting operational data system.

Source Code: FEAST Feature Store Example for Scaling Machine Learning

10) Product Classification

Adding more products to one’s catalogue is one way of attracting more customers. But, with the addition of new products comes the responsibility of categorising them well so the customers can find them easily. Project Idea: Work on the dataset of Otto Group and analyse the various product categories. Furthermore, label the 144,000 unclassified products with one of the possible product categories using multi-class classification algorithms.

Source Code: Otto Group Product Classification Business Intelligence Project

There are chances that the projects mentioned in this blog are a bit difficult for you, and you want to explore more basic projects. You don’t need to worry at all if that is the case. We have more Data Science projects , and Big Data projects in our library that you can check out.

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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 Anastasios Panagiotelis

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Professor  Daniel Oron (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Dmytro Matsypura

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr Wilson Chen

Dr  Bern Conlon

Dr  Nam Ho-Nguyen

Dr  Pablo Montero-Manso

Dr  Stephen Tierney

Dr  Chao Wang

Dr Qin Fang

Dr  Simon Loria

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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What is Business Analysis?

Business analysis is the practice of enabling change in an organizational context, by defining needs and recommending solutions that deliver value to stakeholders. the set of tasks and techniques that are used to perform business analysis are defined in a guide to the business analysis body of knowledge® (babok® guide) ..

Learn More About Business Analysis

What is a Business Analyst?

The Business Analyst is an agent of change. Business Analysis is a disciplined approach for introducing and managing change to organizations, whether they are for-profit businesses, governments, or non-profits. Job titles for business analysis practitioners include not only business analyst, but also business systems analyst, systems analyst, requirements engineer, process analyst, product manager, product owner, enterprise analyst, business architect, management consultant, business intelligence analyst, data scientist, and more. Many other jobs, such as management, project management, product management, software development, quality assurance and interaction design rely heavily on business analysis skills for success.

Business analysis is used to identify and articulate the need for change in how organizations work, and to facilitate that change. As business analysts, we identify and define the solutions that will maximize the value delivered by an organization to its stakeholders. Business analysts work across all levels of an organization and may be involved in everything from defining strategy, to creating the enterprise architecture, to taking a leadership role by defining the goals and requirements for programs and projects or supporting continuous improvement in its technology and processes.

We have the specialized knowledge to act as a guide and lead the business through unknown or unmapped territory, to get it to its desired destination. The value of business analysis is in realization of benefits, avoidance of cost, identification of new opportunities, understanding of required capabilities and modeling the organization. Through the effective use of business analysis, we can ensure an organization realizes these benefits, ultimately improving the way they do business.

Business Analysis Helps Businesses Do Business Better.

Global standards of the business analysis profession, business analysis standards, regulations & best practices.

A Guide to the Business Analysis Body of Knowledge (BABOK® Guide) is the standard for the practice of business analysis and is for professionals who perform business analysis tasks. Recognized globally as the standard of business analysis , it guides business professionals within the six core knowledge areas, describing the skills, deliverables, and techniques that business analysis professionals require to achieve better business outcomes.

IIBA Certifications: AAC    CBAP    CBDA    CCA    CCBA    CPOA    ECBA

Iiba core business analysis certifications.

Entry Certificate in Business Analysis™ (ECBA™)

ECBA™ recognizes individuals ready to develop their business analysis skills, knowledge and behaviours as practicing business analysis professionals.

Certification of Capability in Business Analysis™ (CCBA ® )

CCBA® recognizes skilled business analysis professionals who have two to three years of practical business analysis work experience.

Certified Business Analysis Professional (CBAP ® )

CBAP® recognizes seasoned business analysis professionals who have over five years of practical business analysis work experience.

IIBA Specialized Business Analysis Certifications

Agile Analysis Certification (IIBA ® -AAC)

IIBA®-AAC certification strengthens your skills and expertise, focusing on applying an agile perspective within a business analysis framework.

Business Data Analytics Certification (IIBA ® - CBDA)

The Certification in Business Data Analytics (IIBA®- CBDA) recognizes your ability to effectively execute data analysis related work in support of business analytics initiatives.

Cybersecurity Analysis Certification (IIBA ® - CCA)

IIBA® and IEEE Computer Society have partnered to offer a robust learning and certification program on Cybersecurity Analysis.

Product Ownership Analysis Certification (IIBA ® -CPOA)

IIBA's Product Ownership Analysis Certification Program recognizes the integration of Business Analysis and Product Ownership with an Agile mindset to maximizing value.

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Business Analytics

Key concepts, who will benefit, college students and recent graduates, those considering graduate school, mid-career professionals.

What You Earn

Certificate of Completion

Boost your resume with a Certificate of Completion from HBS Online

Earn by: completing this course

Credential of Readiness

Prove your mastery of business fundamentals

Earn by: completing the three-course CORe curriculum and passing the exam

Describing and Summarizing Data

• Visualizing Data
• Descriptive Statistics
• Relationship Between Two Variables

Featured Exercises

Sampling and estimation.

• Creating Representative and Unbiased Samples
• The Normal Distribution
• Confidence Intervals
• Amazon's Inventory Sampling

Hypothesis Testing

• Designing and Performing Hypothesis Tests
• Improving the Customer Experience

Single Variable Linear Regression

• The Regression Line
• Forecasting
• Interpreting the Regression Output
• Performing Regression Analyses
• Forecasting Home Video Units

Multiple Regression

• The Multiple Regression Equation
• Adapting Concepts from Single Regression
• Performing Multiple Regression Analyses
• New Concepts in Multiple Regression
• The Caesars Staffing Problem

Advance Your Career with Essential Business Skills

Our difference, about the professor.

Janice Hammond Business Analytics

Dates & eligibility.

No current course offerings for this selection.

All learners must be at least 18 years of age, proficient in English, and committed to learning and engaging with fellow participants throughout the course.

Learn about bringing this course to your organization .

Learner Stories

Business Analytics FAQs

How does the business analytics certificate program relate to the credential of readiness program.

In addition to being a standalone certificate program, Business Analytics is also one component course of the Credential of Readiness (CORe) program , which also includes Economics for Managers and Financial Accounting . Designed for those interested in learning business fundamentals more broadly, CORe program participants progress through the three courses in tandem, and the program concludes with a final exam.

What are the learning requirements in order to successfully complete Business Analytics, and how are grades assigned?

Participants are expected to fully complete all coursework in a thoughtful and timely manner. This will mean meeting each week’s deadline to complete a module of the course and fully answering questions posed therein, including satisfactory performance on the quizzes at the end of each module (earning an average score of 50% or greater). This helps ensure your cohort proceeds through the course at a similar pace and can take full advantage of social learning opportunities. A module is composed of a series of teaching elements (such as faculty videos, simulations, reflections, or quizzes) designed to impart the learnings of the course. In addition to module and assignment completion, we expect participation in the social learning elements of the course by offering feedback on others’ reflections and contributing to conversations on the platform. Participants who fail to complete the course requirements will not receive a certificate and will not be eligible to retake the course.

More detailed information on individual course requirements will be communicated at the start of the course. No grades are assigned for Business Analytics–participants will either be evaluated as complete or not complete.

For more information on grading, please refer to the Policies Page .

Are there grants for Business Analytics? How do I qualify?

Business Analytics participants may be eligible for financial aid based on demonstrated financial need. To receive financial aid, you will be asked to provide supporting documentation. Please refer to our Payment & Financial Aid page .

What materials will I have access to after completing Business Analytics?

You will have access to the materials in every prior module as you progress through the program. Access to course materials and the course platform ends 60 days after the final deadline in the program. At the end of each course module, you will be able to download a PDF summary of the module’s key takeaways. At the end of the program, you will receive a PDF compilation of all of the module summary documents.

How should I list my certificate on my resume?

Harvard Business School Online Certificate in Business Analytics [Cohort Start Month and Year]

List your certificate on your LinkedIn profile under "Education" with the language from the Credential Verification page:

School: Harvard Business School Online Dates Attended: [The year you participated in the program] Degree: Other; Certificate in Business Analytics Field of Study: Leave blank Grade: Complete Activities and Societies: Leave blank

For the program description on LinkedIn, please use the following:

Business Analytics is an 8-week, 40-hour online certificate program from Harvard Business School. Business Analytics introduces quantitative methods used to analyze data and make better management decisions. Participants hone their understanding of key concepts, managerial judgment, and ability to apply course concepts to real business problems. Business Analytics was developed by leading Harvard Business School faculty and is delivered in an active learning environment based on the HBS signature case-based learning method.

How does HBS Online Business Analytics relate to the Harvard Business Analytics Program?

HBS Online Business Analytics and the Harvard Business Analytics Program are completely separate programs.

HBS Online Business Analytics consists of approximately 40 hours of material delivered entirely online through the HBS Online course platform over an eight-week period.

The Harvard Business Analytics Program is an online certificate program offered through a collaboration between Harvard Business School, the John A. Paulson School of Engineering and Applied Sciences, and the Faculty of Arts and Sciences. The program consists of six core courses, two seminars, and two in-person immersions, and can be completed in as little as nine months.

How does Business Analytics differ from Data Science Principles and Data Science for Business?

These three courses cover different topics related to data and analytics and do so in different ways.

Business Analytics teaches participants to apply basic statistics to real business problems and includes hands-on practice implementing analyses in Excel. The course covers descriptive statistics, sampling and estimation, hypothesis testing, and regression analysis. The course is intended for individuals at all stages of their careers who would like to strengthen their analytical skills, including college students and recent graduates without a background in statistics, those considering an MBA or other graduate program, or professionals seeking data literacy.

Data Science Principles introduces key concepts in data science—such as prediction, causality, visualization, data wrangling, privacy, and ethics—but does so without coding or mathematical application. The course is intended for organizational leaders and managers to be prepared to act on data analysis and to decide whether data science applications are appropriate tools for their businesses or organizations. The course is also well suited for business operations specialists to understand the building blocks of basic data visualization.

Data Science for Business moves beyond the spreadsheet and provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when using data for decision-making, this course will help you know when to trust your data and how to interpret the results. By the end of the course, you should understand how to create a data-driven framework for your organization or yourself; develop hypotheses and insights from visualization; identify data mistakes or missing components; and, speak the language of data science across themes such as forecasting, linear regressions, and machine learning to better lead your team to long-term success. You will learn how to create a compelling story that uses proven, collected data to make core business decisions, and explore coding environments such as R and visualization software.

Can I take CORe if I've taken Business Analytics?

By enrolling in the Business Analytics certificate program, participants will be ineligible to enroll in the CORe program. By enrolling in the CORe program, participants will be ineligible to enroll in Financial Accounting.

Related Programs

Credential of Readiness (CORe)

Designed to help you achieve fluency in the language of business, CORe is a business fundamentals program that combines Business Analytics, Economics for Managers, and Financial Accounting with a final exam.

Financial Accounting

In this accounting fundamentals course, discover what's behind the numbers in financial statements, such as balance sheets and income statements.

Economics for Managers

See the world through the lens of economics and gain the knowledge and skills to craft successful business strategy.

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The Secrets to Managing Business Analytics Projects

Business analytics projects are often characterized by uncertain or changing requirements — and a high implementation risk. so it takes a special breed of project manager to execute and deliver them..

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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.

About the Authors

Stijn Viaene is a professor at Katholieke Universiteit Leuven, in Leuven, Belgium, and the Deloitte Research Chair of “Bringing IT to Board Level” at Vlerick Leuven Gent Management School in Belgium. Annabel Van den Bunder is a research associate at Vlerick Leuven Gent Management School.

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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Tim constantine, stijn.viaene, jewellbruce.

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