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Top 15 Big Data Projects (With Source Code)

Introduction, big data project ideas, projects for beginners, intermediate big data projects, advanced projects, big data projects: why are they so important, frequently asked questions, additional resources.

Almost 6,500 million linked gadgets communicate data via the Internet nowadays. This figure will climb to 20,000 million by 2025. This “sea of data” is analyzed by big data to translate it into the information that is reshaping our world. Big data refers to massive data volumes – both organized and unstructured – that bombard enterprises daily. But it’s not simply the type or quantity of data that matters; it’s also what businesses do with it. Big data may be evaluated for insights that help people make better decisions and feel more confident about making key business decisions. Big data refers to vast, diversified amounts of data that are growing at an exponential rate. The volume of data, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered (known as the “three v’s” of big data) are all factors to consider. Big data is frequently derived by data mining and is available in a variety of formats.

Unstructured and structured big data are two types of big data. For large data, the term structured data refers to data that has a set length and format. Numbers, dates, and strings, which are collections of words and numbers, are examples of organized data. Unstructured data is unorganized data that does not fit into a predetermined model or format. It includes information gleaned from social media sources that aid organizations in gathering information on customer demands.

Key Takeaway

Confused about your next job?

• Big data is a large amount of diversified information that is arriving in ever-increasing volumes and at ever-increasing speeds.
• Big data can be structured (typically numerical, readily formatted, to and saved) or unstructured (often non-numerical, difficult to format and store) (more free-form, less quantifiable).
• Big data analysis may benefit nearly every function in a company, but dealing with the clutter and noise can be difficult.
• Big data can be gathered willingly through personal devices and applications, through questionnaires, product purchases, and electronic check-ins, as well as publicly published remarks on social networks and websites.
• Big data is frequently kept in computer databases and examined with software intended to deal with huge, complicated data sets.

Just knowing the theory of big data isn’t going to get you very far. You’ll need to put what you’ve learned into practice. You may put your big data talents to the test by working on big data projects. Projects are an excellent opportunity to put your abilities to the test. They’re also great for your resume. In this article, we are going to discuss some great Big Data projects that you can work on to showcase your big data skills.

1. Traffic control using Big Data

Big Data initiatives that simulate and predict traffic in real-time have a wide range of applications and advantages. The field of real-time traffic simulation has been modeled successfully. However, anticipating route traffic has long been a challenge. This is because developing predictive models for real-time traffic prediction is a difficult endeavor that involves a lot of latency, large amounts of data, and ever-increasing expenses.

The following project is a Lambda Architecture application that monitors the traffic safety and congestion of each street in Chicago. It depicts current traffic collisions, red light, and speed camera infractions, as well as traffic patterns on 1,250 street segments within the city borders.

These datasets have been taken from the City of Chicago’s open data portal:

• Traffic Crashes shows each crash that occurred within city streets as reported in the electronic crash reporting system (E-Crash) at CPD. Citywide data are available starting September 2017.
• Red Light Camera Violations reflect the daily number of red light camera violations recorded by the City of Chicago Red Light Program for each camera since 2014.
• Speed Camera Violations reflect the daily number of speed camera violations recorded by each camera in Children’s Safety Zones since 2014.
• Historical Traffic Congestion Estimates estimates traffic congestion on Chicago’s arterial streets in real-time by monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses.
• Current Traffic Congestion Estimate shows current estimated speed for street segments covering 300 miles of arterial roads. Congestion estimates are produced every ten minutes.

The project implements the three layers of the Lambda Architecture:

• Batch layer – manages the master dataset (the source of truth), which is an immutable, append-only set of raw data. It pre-computes batch views from the master dataset.
• Serving layer – responds to ad-hoc queries by returning pre-computed views (from the batch layer) or building views from the processed data.
• Speed layer – deals with up-to-date data only to compensate for the high latency of the batch layer

Source Code – Traffic Control

2. Search Engine

To comprehend what people are looking for, search engines must deal with trillions of network objects and monitor the online behavior of billions of people. Website material is converted into quantifiable data by search engines. The given project is a full-featured search engine built on top of a 75-gigabyte In this project, we will use several datasets like stopwords.txt (A text file containing all the stop words in the current directory of the code) and wiki_dump.xml (The XML file containing the full data of Wikipedia). Wikipedia corpus with sub-second search latency. The results show wiki pages sorted by TF/IDF (stands for Term Frequency — Inverse Document Frequency) relevance based on the search term/s entered. This project addresses latency, indexing, and huge data concerns with an efficient code and the K-Way merge sort method.

Source Code – Search Engine

3. Medical Insurance Fraud Detection

A unique data science model that uses real-time analysis and classification algorithms to assist predict fraud in the medical insurance market. This instrument can be utilized by the government to benefit patients, pharmacies, and doctors, ultimately assisting in improving industry confidence, addressing rising healthcare expenses, and addressing the impact of fraud. Medical services deception is a major problem that costs Medicare/Medicaid and the insurance business a lot of money.

4 different Big Datasets have been joined in this project to get a single table for final data analysis. The datasets collected are:

• Part D prescriber services- data such as name of doctor, addres of doctor, disease, symptoms etc.
• List of Excluded Individuals and Entities (LEIE) database: This database contains a rundown of people and substances that are prohibited from taking an interest in governmentally financed social insurance programs (for example Medicare) because of past medicinal services extortion. 
• Payments Received by Physician from Pharmaceuticals
• CMS part D dataset- data by Center of Medicare and Medicaid Services

It has been developed by taking consideration of different key features with applying different Machine Learning Algorithms to see which one performs better. The ML algorithms used have been trained to detect any irregularities in the dataset so that the authorities can be alerted.

Source Code – Medical Insurance Fraud

4. Data Warehouse Design for an E-Commerce Site

A data warehouse is essentially a vast collection of data for a company that assists the company in making educated decisions based on data analysis. The data warehouse designed in this project is a central repository for an e-commerce site, containing unified data ranging from searches to purchases made by site visitors. The site can manage supply based on demand (inventory management), logistics, the price for maximum profitability, and advertisements based on searches and things purchased by establishing such a data warehouse. Recommendations can also be made based on tendencies in a certain area, as well as age groups, sex, and other shared interests. This is a data warehouse implementation for an e-commerce website “Infibeam” which sells digital and consumer electronics.

Source Code – Data Warehouse Design

5. Text Mining Project

You will be required to perform text analysis and visualization of the delivered documents as part of this project. For beginners, this is one of the best deep learning project ideas. Text mining is in high demand, and it can help you demonstrate your abilities as a data scientist . You can deploy Natural Language Process Techniques to gain some useful information from the link provided below. The link contains a collection of NLP tools and resources for various languages.

Source Code – Text Mining

6. Big Data Cybersecurity

The major goal of this Big Data project is to use complex multivariate time series data to exploit vulnerability disclosure trends in real-world cybersecurity concerns. This project consists of outlier and anomaly detection technologies based on Hadoop, Spark, and Storm are interwoven with the system’s machine learning and automation engine for real-time fraud detection and intrusion detection to forensics.

For independent Big Data Multi-Inspection / Forensics of high-level risks or volume datasets exceeding local resources, it uses the Ophidia Analytics Framework. Ophidia Analytics Framework is an open-source big data analytics framework that contains cluster-aware parallel operators for data analysis and mining (subsetting, reduction, metadata processing, and so on). The framework is completely connected with Ophidia Server: it takes commands from the server and responds with alerts, allowing processes to run smoothly.

Lumify, an open-source big data analysis, and visualization platform are also included in the Cyber Security System to provide big data analysis and visualization of each instance of fraud or intrusion events into temporary, compartmentalized virtual machines, which creates a full snapshot of the network infrastructure and infected device, allowing for in-depth analytics, forensic review, and providing a transportable threat analysis for Executive level next-steps.

Lumify, a big data analysis and visualization tool developed by Cyberitis is launched using both local and cloud resources (customizable per environment and user). Only the backend servers (Hadoop, Accumulo, Elasticsearch, RabbitMQ, Zookeeper) are included in the Open Source Lumify Dev Virtual Machine. This VM allows developers to get up and running quickly without having to install the entire stack on their development workstations.

Source Code – Big Data Cybersecurity

7. Crime Detection

The following project is a Multi-class classification model for predicting the types of crimes in Toronto city. The developer of the project, using big data ( The dataset collected includes every major crime committed from 2014-2017* in the city of Toronto, with detailed information about the location and time of the offense), has constructed a multi-class classification model using a Random Forest classifier to predict the type of major crime committed based on time of day, neighborhood, division, year, month, etc. using data sourced from Toronto Police.

The use of big data analytics here is to discover crime tendencies automatically. If analysts are given automated, data-driven tools to discover crime patterns, these tools can help police better comprehend crime patterns, allowing for more precise estimates of past crimes and increasing suspicion of suspects.

Source Code – Crime Detection

8. Disease Prediction Based on Symptoms

With the rapid advancement of technology and data, the healthcare domain is one of the most significant study fields in the contemporary era. The enormous amount of patient data is tough to manage. Big Data Analytics makes it easier to manage this information (Electronic Health Records are one of the biggest examples of the application of big data in healthcare). Knowledge derived from big data analysis gives healthcare specialists insights that were not available before. In healthcare, big data is used at every stage of the process, from medical research to patient experience and outcomes. There are numerous ways of treating various ailments throughout the world. Machine Learning and Big Data are new approaches that aid in disease prediction and diagnosis. This research explored how machine learning algorithms can be used to forecast diseases based on symptoms. The following algorithms have been explored in code:

• Naive Bayes
• Decision Tree
• Random Forest
• Gradient Boosting

Source Code – Disease Prediction

9. Yelp Review Analysis

Yelp is a forum for users to submit reviews and rate businesses with a star rating. According to studies, an increase of one star resulted in a 59 percent rise in income for independent businesses. As a result, we believe the Yelp dataset has a lot of potential as a powerful insight source. Customer reviews of Yelp is a gold mine waiting to be discovered.

This project’s main goal is to conduct in-depth analyses of seven different cuisine types of restaurants: Korean, Japanese, Chinese, Vietnamese, Thai, French, and Italian, to determine what makes a good restaurant and what concerns customers, and then make recommendations for future improvement and profit growth. We will mostly evaluate customer evaluations to determine why customers like or dislike the business. We can turn the unstructured data (reviews)  into actionable insights using big data, allowing businesses to better understand how and why customers prefer their products or services and make business improvements as rapidly as feasible.

Source Code – Review Analysis

10. Recommendation System

Thousands, millions, or even billions of objects, such as merchandise, video clips, movies, music, news, articles, blog entries, advertising, and so on, are typically available through online services. The Google Play Store, for example, has millions of apps and YouTube has billions of videos. Netflix Recommendation Engine, their most effective algorithm, is made up of algorithms that select material based on each user profile. Big data provides plenty of user data such as past purchases, browsing history, and comments for Recommendation systems to deliver relevant and effective recommendations. In a nutshell, without massive data, even the most advanced Recommenders will be ineffective. Big data is the driving force behind our mini-movie recommendation system. Over 3,000 titles are filtered at a time by the engine, which uses 1,300 suggestion clusters depending on user preferences. It’s so accurate that customized recommendations from the engine drive 80 percent of Netflix viewer activity. The goal of this project is to compare the performance of various recommendation models on the Hadoop Framework.

Source Code – Recommendation System

11. Anomaly Detection in Cloud Servers

Anomaly detection is a useful tool for cloud platform managers who want to keep track of and analyze cloud behavior in order to improve cloud reliability. It assists cloud platform managers in detecting unexpected system activity so that preventative actions can be taken before a system crash or service failure occurs.

This project provides a reference implementation of a Cloud Dataflow streaming pipeline that integrates with BigQuery ML, Cloud AI Platform to perform anomaly detection. A key component of the implementation leverages Dataflow for feature extraction & real-time outlier identification which has been tested to analyze over 20TB of data.

Source Code – Anomaly Detection

12. Smart Cities Using Big Data

A smart city is a technologically advanced metropolitan region that collects data using various electronic technologies, voice activation methods, and sensors. The information gleaned from the data is utilized to efficiently manage assets, resources, and services; in turn, the data is used to improve operations throughout the city. Data is collected from citizens, devices, buildings, and assets, which is then processed and analyzed to monitor and manage traffic and transportation systems, power plants, utilities, water supply networks, waste, crime detection, information systems, schools, libraries, hospitals, and other community services. Big data obtains this information and with the help of advanced algorithms, smart network infrastructures and various analytics platforms can implement the sophisticated features of a smart city.  This smart city reference pipeline shows how to integrate various media building blocks, with analytics powered by the OpenVINO Toolkit, for traffic or stadium sensing, analytics, and management tasks.

Source Code – Smart Cities

13. Tourist Behavior Analysis

This is one of the most innovative big data project concepts. This Big Data project aims to study visitor behavior to discover travelers’ preferences and most frequented destinations, as well as forecast future tourism demand. 

What is the role of big data in the project? Because visitors utilize the internet and other technologies while on vacation, they leave digital traces that Big Data can readily collect and distribute – the majority of the data comes from external sources such as social media sites. The sheer volume of data is simply too much for a standard database to handle, necessitating the use of big data analytics.  All the information from these sources can be used to help firms in the aviation, hotel, and tourist industries find new customers and advertise their services. It can also assist tourism organizations in visualizing and forecasting current and future trends.

Source Code – Tourist Behavior Analysis

14. Web Server Log Analysis

A web server log keeps track of page requests as well as the actions it has taken. To further examine the data, web servers can be used to store, analyze, and mine the data. Page advertising can be determined and SEO (search engine optimization) can be performed in this manner. Web-server log analysis can be used to get a sense of the overall user experience. This type of processing is advantageous to any company that relies largely on its website for revenue generation or client communication. This interesting big data project demonstrates parsing (including incorrectly formatted strings) and analysis of web server log data.

Source Code – Web Server Log Analysis

15. Image Caption Generator

Because of the rise of social media and the importance of digital marketing, businesses must now upload engaging content. Visuals that are appealing to the eye are essential, but subtitles that describe the images are also required. The usage of hashtags and attention-getting subtitles might help you reach out to the right people even more. Large datasets with correlated photos and captions must be managed. Image processing and deep learning are used to comprehend the image, and artificial intelligence is used to provide captions that are both relevant and appealing. Big Data source code can be written in Python. The creation of image captions isn’t a beginner-level Big Data project proposal and is indeed challenging. The project given below uses a neural network to generate captions for an image using CNN (Convolution Neural Network) and RNN (Recurrent Neural Network) with BEAM Search (Beam search is a heuristic search algorithm that examines a graph by extending the most promising node in a small collection. 

There are currently rich and colorful datasets in the image description generating work, such as MSCOCO, Flickr8k, Flickr30k, PASCAL 1K, AI Challenger Dataset, and STAIR Captions, which are progressively becoming a trend of discussion. The given project utilizes state-of-the-art ML and big data algorithms to build an effective image caption generator.

Source Code – Image Caption Generator

Big Data is a fascinating topic. It helps in the discovery of patterns and outcomes that might otherwise go unnoticed. Big Data is being used by businesses to learn what their customers want, who their best customers are, and why people choose different products. The more information a business has about its customers, the more competitive it is.

It can be combined with Machine Learning to create market strategies based on customer predictions. Companies that use big data become more customer-centric.

This expertise is in high demand and learning it will help you progress your career swiftly. As a result, if you’re new to big data, the greatest thing you can do is brainstorm some big data project ideas. 

We’ve examined some of the best big data project ideas in this article. We began with some simple projects that you can complete quickly. After you’ve completed these beginner tasks, I recommend going back to understand a few additional principles before moving on to the intermediate projects. After you’ve gained confidence, you can go on to more advanced projects.

What are the 3 types of big data? Big data is classified into three main types:

• Unstructured
• Semi-structured

What can big data be used for? Some important use cases of big data are:

• Improving Science and research
• Improving governance
• Smart cities
• Understanding and targeting customers
• Understanding and Optimizing Business Processes
• Improving Healthcare and Public Health
• Financial Trading
• Optimizing Machine and Device Performance

What industries use big data? Big data finds its application in various domains. Some fields where big data can be used efficiently are:

• Travel and tourism
• Financial and banking sector
• Telecommunication and media
• Banking Sector
• Government and Military
• Social Media
• Big Data Tools
• Big Data Engineer
• Applications of Big Data
• Big Data Interview Questions
• Big Data Projects

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• Big Data Life Cycle

Activities in the Big Data life cycle

At the Planning stage, because of potential data volume and growth, the selection of data for preservation must be discussed.  Keeping all raw data may be required as  some experiments are too costly to reproduce.  In some cases, the volume and velocity of data preclude the preservation of raw data. In other cases, it is cheaper and easier to run a simulation or a sequencer again to obtain the raw data than to preserve it.

The Acquire activity reflects how data is produced, generated, and ingested in the research process.  Data acquisition may be the result of using remote sensors, computational simulations, and downloads from external sources such as a Disciplinary Repository or the Twitter API (Application Programmer’s Interface). 

Preparing datasets and making them ready for analysis is a time-consuming step with Big Data and its complexity is often overlooked.  It's often called data wrangling when these steps involve reformatting, cleaning, and integrating data sets

Examples of Big Data Life Cycle

This figure presents the Big Data life cycle from the point of view of a project.  Researchers understand the research life cycle, but often confuse it with the data curation life cycle.  This diagram looks at research from the point of view of data curation.  The Describe and Assure activities are presented outside the cycle to emphasize that they should be present at every step of the Big Data life cycle.

Describing the data and processes used in the analysis at every step – capturing the provenance trace - is crucial for Big Data.  The earlier curation-related tasks are being planned in the data management life cycle, the easier they may be to execute.

Documenting data sources, experimental conditions, instruments and sensors, simulation scripts, processing of datasets, analysis parameters and thresholds ensures not only much needed transparency of the research, but also data discovery and future use in science.  This documentation also provides a basis and a justification for decision-making.

The Analysis activity is the domain of the scientists performing research.  Statistical methods and machine learning, in particular, feature prominently with Big Data.  However, recording and preserving the parameters of experiments, including simulation scripts, and the entire computational environment are needed for the reproducibility of results.

The preservation activity includes the creation of pipelines or workflows that track dependencies between data and processes and allow linking raw data to results in a publication.  Preservation activities should aim to capture data transformations in order to address the challenges of Big Data.

The Discover activity refers to the set of procedures that ensures that datasets relevant to a particular analysis or collection can be found.  At this stage, one must decide which data will be made discoverable.  Integrating the results of different search methods – keyword-based, geo-spatial searches, metadata-based, semantic searches helps providing direct answers to user questions, rather than links to documents containing the information.

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Use This Framework to Predict the Success of Your Big Data Project

• Carsten Lund Pedersen
• Thomas Ritter

Data projects often fail because executives can’t assess risks at the outset.

Up to 85% of big data projects fail, often because executives cannot accurately assess project risks at the outset. But a new research project offers some guidelines and questions to ask yourself before launching a new big data initiative to help predict its success. Access to data is obviously a precondition for any initiative focused on data-driven growth. However, not all available data is useful, nor is it unique and exclusive. Moreover, not all data is available. The question executives need to ask is “ Can we access data that is valuable and rare? ”. If the answer is yes, you then need to ask: “ Can employees use data to create solutions on their own? ” and “ Can our technology deliver the solution? ” And finally, “ Is our solution compliant with laws and ethics? ” Little value can be created if your solution breaks the law. Moreover, if users think of the solution as “creepy,” you might face a media backlash. Try this structured approach to predict the success of your next big data project.

Big data projects that revolve around exploiting data for business optimization and business development are top of mind for most executives. However, up to 85% of big data projects fail, often because executives cannot accurately assess project risks at the outset. We argue that the success of data projects is largely determined by four important components — data, autonomy, technology, and accountability — or, simply put, by the four D.A.T.A. questions. These questions originate from our four-year research project on big data commercialization .

• CP Carsten Lund Pedersen is an associate professor in digital transformation at the IT University of Copenhagen in Denmark, where he researches digital transformation, digital business development and digital market responsiveness.
• TR Thomas Ritter is a professor of market strategy and business development at the Department of Strategy and Innovation at Copenhagen Business School in Denmark, where he researches business model innovation, market strategies, and market management.

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25+ Solved End-to-End Big Data Projects with Source Code

Solved End-to-End Real World Mini Big Data Projects Ideas with Source Code For Beginners and Students to master big data tools like Hadoop and Spark.

Ace your big data analytics interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data analytics projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. You will find several big data projects depending on your level of expertise- big data projects for students, big data projects for beginners, etc.

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Have you ever looked for sneakers on Amazon and seen advertisements for similar sneakers while searching the internet for the perfect cake recipe? Maybe you started using Instagram to search for some fitness videos, and now, Instagram keeps recommending videos from fitness influencers to you. And even if you’re not very active on social media, I’m sure you now and then check your phone before leaving the house to see what the traffic is like on your route to know how long it could take you to reach your destination. None of this would have been possible without the application of big data analysis process on by the modern data driven companies. We bring the top big data projects for 2023 that are specially curated for students, beginners, and anybody looking to get started with mastering data skills.

Table of Contents

What is a big data project, how do you create a good big data project, 25+ big data project ideas to help boost your resume , big data project ideas for beginners, intermediate projects on data analytics, advanced level examples of big data projects, real-time big data projects with source code, sample big data project ideas for final year students, big data project ideas using hadoop , big data projects using spark, gcp and aws big data projects, best big data project ideas for masters students, fun big data project ideas, top 5 apache big data projects, top big data projects on github with source code, level-up your big data expertise with projectpro's big data projects, faqs on big data projects.

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on structured and unstructured data for several purposes, including predictive modeling and other advanced analytics applications. Before actually working on any big data projects, data engineers must acquire proficient knowledge in the relevant areas, such as deep learning, machine learning, data visualization , data analytics, data science, etc. 

Many platforms, like GitHub and ProjectPro, offer various big data projects for professionals at all skill levels- beginner, intermediate, and advanced. However, before moving on to a list of big data project ideas worth exploring and adding to your portfolio, let us first get a clear picture of what big data is and why everyone is interested in it.

Kicking off a big data analytics project is always the most challenging part. You always encounter questions like what are the project goals, how can you become familiar with the dataset, what challenges are you trying to address,  what are the necessary skills for this project, what metrics will you use to evaluate your model, etc.

Well! The first crucial step to launching your project initiative is to have a solid project plan. To build a big data project, you should always adhere to a clearly defined workflow. Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation.

Understand the Business Goals of the Big Data Project

The first step of any good big data analytics project is understanding the business or industry that you are working on. Go out and speak with the individuals whose processes you aim to transform with data before you even consider analyzing the data. Establish a timeline and specific key performance indicators afterward. Although planning and procedures can appear tedious, they are a crucial step to launching your data initiative! A definite purpose of what you want to do with data must be identified, such as a specific question to be answered, a data product to be built, etc., to provide motivation, direction, and purpose.

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Collect Data for the Big Data Project

The next step in a big data project is looking for data once you've established your goal. To create a successful data project, collect and integrate data from as many different sources as possible. 

Here are some options for collecting data that you can utilize:

Connect to an existing database that is already public or access your private database.

Consider the APIs for all the tools your organization has been utilizing and the data they have gathered. You must put in some effort to set up those APIs so that you can use the email open and click statistics, the support request someone sent, etc.

There are plenty of datasets on the Internet that can provide more information than what you already have. There are open data platforms in several regions (like data.gov in the U.S.). These open data sets are a fantastic resource if you're working on a personal project for fun.

Data Preparation and Cleaning

The data preparation step, which may consume up to 80% of the time allocated to any big data or data engineering project, comes next. Once you have the data, it's time to start using it. Start exploring what you have and how you can combine everything to meet the primary goal. To understand the relevance of all your data, start making notes on your initial analyses and ask significant questions to businesspeople, the IT team, or other groups. Data Cleaning is the next step. To ensure that data is consistent and accurate, you must review each column and check for errors, missing data values, etc.

Making sure that your project and your data are compatible with data privacy standards is a key aspect of data preparation that should not be overlooked. Personal data privacy and protection are becoming increasingly crucial, and you should prioritize them immediately as you embark on your big data journey. You must consolidate all your data initiatives, sources, and datasets into one location or platform to facilitate governance and carry out privacy-compliant projects. 

New Projects

Data Transformation and Manipulation

Now that the data is clean, it's time to modify it so you can extract useful information. Starting with combining all of your various sources and group logs will help you focus your data on the most significant aspects. You can do this, for instance, by adding time-based attributes to your data, like:

Acquiring date-related elements (month, hour, day of the week, week of the year, etc.)

Calculating the variations between date-column values, etc.

Joining datasets is another way to improve data, which entails extracting columns from one dataset or tab and adding them to a reference dataset. This is a crucial component of any analysis, but it can become a challenge when you have many data sources.

 Visualize Your Data

Now that you have a decent dataset (or perhaps several), it would be wise to begin analyzing it by creating beautiful dashboards, charts, or graphs. The next stage of any data analytics project should focus on visualization because it is the most excellent approach to analyzing and showcasing insights when working with massive amounts of data.

Another method for enhancing your dataset and creating more intriguing features is to use graphs. For instance, by plotting your data points on a map, you can discover that some geographic regions are more informative than some other nations or cities.

Build Predictive Models Using Machine Learning Algorithms

Machine learning algorithms can help you take your big data project to the next level by providing you with more details and making predictions about future trends. You can create models to find trends in the data that were not visible in graphs by working with clustering techniques (also known as unsupervised learning). These organize relevant outcomes into clusters and more or less explicitly state the characteristic that determines these outcomes.

Advanced data scientists can use supervised algorithms to predict future trends. They discover features that have influenced previous data patterns by reviewing historical data and can then generate predictions using these features. 

Lastly, your predictive model needs to be operationalized for the project to be truly valuable. Deploying a machine learning model for adoption by all individuals within an organization is referred to as operationalization.

Repeat The Process

This is the last step in completing your big data project, and it's crucial to the whole data life cycle. One of the biggest mistakes individuals make when it comes to machine learning is assuming that once a model is created and implemented, it will always function normally. On the contrary, if models aren't updated with the latest data and regularly modified, their quality will deteriorate with time.

You need to accept that your model will never indeed be "complete" to accomplish your first data project effectively. You need to continually reevaluate, retrain it, and create new features for it to stay accurate and valuable. 

If you are a newbie to Big Data, keep in mind that it is not an easy field, but at the same time, remember that nothing good in life comes easy; you have to work for it. The most helpful way of learning a skill is with some hands-on experience. Below is a list of Big Data analytics project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data. 

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In this section, you will find a list of good big data project ideas for masters students.

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Explore features of Spark SQL in practice on Spark 2.0

MovieLens Dataset Exploratory Analysis

Bitcoin Data Mining on AWS

Create A Data Pipeline Based On Messaging Using PySpark And Hive - Covid-19 Analysis

Spark Project-Analysis and Visualization on Yelp Dataset

Project Ideas on Big Data Analytics

Let us now begin with a more detailed list of good big data project ideas that you can easily implement.

This section will introduce you to a list of project ideas on big data that use Hadoop along with descriptions of how to implement them.

1. Visualizing Wikipedia Trends

Human brains tend to process visual data better than data in any other format. 90% of the information transmitted to the brain is visual, and the human brain can process an image in just 13 milliseconds. Wikipedia is a page that is accessed by people all around the world for research purposes, general information, and just to satisfy their occasional curiosity. 

Raw page data counts from Wikipedia can be collected and processed via Hadoop. The processed data can then be visualized using Zeppelin notebooks to analyze trends that can be supported based on demographics or parameters. This is a good pick for someone looking to understand how big data analysis and visualization can be achieved through Big Data and also an excellent pick for an Apache Big Data project idea. 

Visualizing Wikipedia Trends Big Data Project with Source Code .

2. Visualizing Website Clickstream Data

Clickstream data analysis refers to collecting, processing, and understanding all the web pages a particular user visits. This analysis benefits web page marketing, product management, and targeted advertisement. Since users tend to visit sites based on their requirements and interests, clickstream analysis can help to get an idea of what a user is looking for. 

Visualization of the same helps in identifying these trends. In such a manner, advertisements can be generated specific to individuals. Ads on webpages provide a source of income for the webpage, and help the business publishing the ad reach the customer and at the same time, other internet users. This can be classified as a Big Data Apache project by using Hadoop to build it.

Big Data Analytics Projects Solution for Visualization of Clickstream Data on a Website

3. Web Server Log Processing

A web server log maintains a list of page requests and activities it has performed. Storing, processing, and mining the data on web servers can be done to analyze the data further. In this manner, webpage ads can be determined, and SEO (Search engine optimization) can also be done. A general overall user experience can be achieved through web-server log analysis. This kind of processing benefits any business that heavily relies on its website for revenue generation or to reach out to its customers. The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.

Big Data Project using Hadoop with Source Code for Web Server Log Processing 

This section will provide you with a list of projects that utilize Apache Spark for their implementation.

4. Analysis of Twitter Sentiments Using Spark Streaming

Sentimental analysis is another interesting big data project topic that deals with the process of determining whether a given opinion is positive, negative, or neutral. For a business, knowing the sentiments or the reaction of a group of people to a new product launch or a new event can help determine the profitability of the product and can help the business to have a more extensive reach by getting an idea of the feel of the customers. From a political standpoint, the sentiments of the crowd toward a candidate or some decision taken by a party can help determine what keeps a specific group of people happy and satisfied. You can use Twitter sentiments to predict election results as well. 

Sentiment analysis has to be done for a large dataset since there are over 180 million monetizable daily active users ( https://www.businessofapps.com/data/twitter-statistics/) on Twitter. The analysis also has to be done in real-time. Spark Streaming can be used to gather data from Twitter in real time. NLP (Natural Language Processing) models will have to be used for sentimental analysis, and the models will have to be trained with some prior datasets. Sentiment analysis is one of the more advanced projects that showcase the use of Big Data due to its involvement in NLP.

Access Big Data Project Solution to Twitter Sentiment Analysis

5. Real-time Analysis of Log-entries from Applications Using Streaming Architectures

If you are looking to practice and get your hands dirty with a real-time big data project, then this big data project title must be on your list. Where web server log processing would require data to be processed in batches, applications that stream data will have log files that would have to be processed in real-time for better analysis. Real-time streaming behavior analysis gives more insight into customer behavior and can help find more content to keep the users engaged. Real-time analysis can also help to detect a security breach and take necessary action immediately. Many social media networks work using the concept of real-time analysis of the content streamed by users on their applications. Spark has a Streaming tool that can process real-time streaming data.

Access Big Data Spark Project Solution to Real-time Analysis of log-entries from applications using Streaming Architecture

6. Analysis of Crime Datasets

Analysis of crimes such as shootings, robberies, and murders can result in finding trends that can be used to keep the police alert for the likelihood of crimes that can happen in a given area. These trends can help to come up with a more strategized and optimal planning approach to selecting police stations and stationing personnel. 

With access to CCTV surveillance in real-time, behavior detection can help identify suspicious activities. Similarly, facial recognition software can play a bigger role in identifying criminals. A basic analysis of a crime dataset is one of the ideal Big Data projects for students. However, it can be made more complex by adding in the prediction of crime and facial recognition in places where it is required.

Big Data Analytics Projects for Students on Chicago Crime Data Analysis with Source Code

Explore Categories

In this section, you will find big data projects that rely on cloud service providers such as AWS and GCP.

7. Build a Scalable Event-Based GCP Data Pipeline using DataFlow

Suppose you are running an eCommerce website, and a customer places an order. In that case, you must inform the warehouse team to check the stock availability and commit to fulfilling the order. After that, the parcel has to be assigned to a delivery firm so it can be shipped to the customer. For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration.

This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow .

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes:

people_positive_cases_count

county_name

data_source

Language Used: Python 3.7

Services: Cloud Composer , Google Cloud Storage (GCS), Pub-Sub , Cloud Functions, BigQuery, BigTable

Big Data Project with Source Code: Build a Scalable Event-Based GCP Data Pipeline using DataFlow  

8. Topic Modeling

The future is AI! You must have come across similar quotes about artificial intelligence (AI). Initially, most people found it difficult to believe that could be true. Still, we are witnessing top multinational companies drift towards automating tasks using machine learning tools. 

Understand the reason behind this drift by working on one of our repository's most practical data engineering project examples .

Project Objective: Understand the end-to-end implementation of Machine learning operations (MLOps) by using cloud computing .

Learnings from the Project: This project will introduce you to various applications of AWS services . You will learn how to convert an ML application to a Flask Application and its deployment using Gunicord webserver. You will be implementing this project solution in Code Build. This project will help you understand ECS Cluster Task Definition.

Tech Stack:

Language: Python

Libraries: Flask, gunicorn, scipy , nltk , tqdm, numpy, joblib, pandas, scikit_learn, boto3

Services: Flask, Docker, AWS, Gunicorn

Source Code: MLOps AWS Project on Topic Modeling using Gunicorn Flask

9. MLOps on GCP Project for Autoregression using uWSGI Flask

Here is a project that combines Machine Learning Operations (MLOps) and Google Cloud Platform (GCP). As companies are switching to automation using machine learning algorithms, they have realized hardware plays a crucial role. Thus, many cloud service providers have come up to help such companies overcome their hardware limitations. Therefore, we have added this project to our repository to assist you with the end-to-end deployment of a machine learning project .

Project Objective: Deploying the moving average time-series machine-learning model on the cloud using GCP and Flask.

Learnings from the Project: You will work with Flask and uWSGI model files in this project. You will learn about creating Docker Images and Kubernetes architecture. You will also get to explore different components of GCP and their significance. You will understand how to clone the git repository with the source repository. Flask and Kubernetes deployment will also be discussed in this project.

Tech Stack: Language - Python

Services - GCP, uWSGI, Flask, Kubernetes, Docker

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This section has good big data project ideas for graduate students who have enrolled in a master course.

10. Real-time Traffic Analysis

Traffic is an issue in many major cities, especially during some busier hours of the day. If traffic is monitored in real-time over popular and alternate routes, steps could be taken to reduce congestion on some roads. Real-time traffic analysis can also program traffic lights at junctions – stay green for a longer time on higher movement roads and less time for roads showing less vehicular movement at a given time. Real-time traffic analysis can help businesses manage their logistics and plan their commute accordingly for working-class individuals. Concepts of deep learning can be used to analyze this dataset properly.

11. Health Status Prediction

“Health is wealth” is a prevalent saying. And rightly so, there cannot be wealth unless one is healthy enough to enjoy worldly pleasures. Many diseases have risk factors that can be genetic, environmental, dietary, and more common for a specific age group or sex and more commonly seen in some races or areas. By gathering datasets of this information relevant for particular diseases, e.g., breast cancer, Parkinson’s disease, and diabetes, the presence of more risk factors can be used to measure the probability of the onset of one of these issues. 

In cases where the risk factors are not already known, analysis of the datasets can be used to identify patterns of risk factors and hence predict the likelihood of onset accordingly. The level of complexity could vary depending on the type of analysis that has to be done for different diseases. Nevertheless, since prediction tools have to be applied, this is not a beginner-level big data project idea.

12. Analysis of Tourist Behavior

Tourism is a large sector that provides a livelihood for several people and can adversely impact a country's economy.. Not all tourists behave similarly simply because individuals have different preferences. Analyzing this behavior based on decision-making, perception, choice of destination, and level of satisfaction can be used to help travelers and locals have a more wholesome experience. Behavior analysis, like sentiment analysis, is one of the more advanced project ideas in the Big Data field.

13. Detection of Fake News on Social Media

With the popularity of social media, a major concern is the spread of fake news on various sites. Even worse, this misinformation tends to spread even faster than factual information. According to Wikipedia, fake news can be visual-based, which refers to images, videos, and even graphical representations of data, or linguistics-based, which refers to fake news in the form of text or a string of characters. Different cues are used based on the type of news to differentiate fake news from real. A site like Twitter has 330 million users , while Facebook has 2.8 billion users. A large amount of data will make rounds on these sites, which must be processed to determine the post's validity. Various data models based on machine learning techniques and computational methods based on NLP will have to be used to build an algorithm that can be used to detect fake news on social media.

Access Solution to Interesting Big Data Project on Detection of Fake News

14. Prediction of Calamities in a Given Area

Certain calamities, such as landslides and wildfires, occur more frequently during a particular season and in certain areas. Using certain geospatial technologies such as remote sensing and GIS (Geographic Information System) models makes it possible to monitor areas prone to these calamities and identify triggers that lead to such issues. 

If calamities can be predicted more accurately, steps can be taken to protect the residents from them, contain the disasters, and maybe even prevent them in the first place. Past data of landslides has to be analyzed, while at the same time, in-site ground monitoring of data has to be done using remote sensing. The sooner the calamity can be identified, the easier it is to contain the harm. The need for knowledge and application of GIS adds to the complexity of this Big Data project.

15. Generating Image Captions

With the emergence of social media and the importance of digital marketing, it has become essential for businesses to upload engaging content. Catchy images are a requirement, but captions for images have to be added to describe them. The additional use of hashtags and attention-drawing captions can help a little more to reach the correct target audience. Large datasets have to be handled which correlate images and captions. 

This involves image processing and deep learning to understand the image and artificial intelligence to generate relevant but appealing captions. Python can be used as the Big Data source code. Image caption generation cannot exactly be considered a beginner-level Big Data project idea. It is probably better to get some exposure to one of the projects before proceeding with this.

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16. Credit Card Fraud Detection

The goal is to identify fraudulent credit card transactions, so a customer is not billed for an item that the customer did not purchase. This can tend to be challenging since there are huge datasets, and detection has to be done as soon as possible so that the fraudsters do not continue to purchase more items. Another challenge here is the data availability since the data is supposed to be primarily private. Since this project involves machine learning, the results will be more accurate with a larger dataset. Data availability can pose a challenge in this manner. Credit card fraud detection is helpful for a business since customers are likely to trust companies with better fraud detection applications, as they will not be billed for purchases made by someone else. Fraud detection can be considered one of the most common Big Data project ideas for beginners and students.

If you are looking for big data project examples that are fun to implement then do not miss out on this section.

17. GIS Analytics for Better Waste Management

Due to urbanization and population growth, large amounts of waste are being generated globally. Improper waste management is a hazard not only to the environment but also to us. Waste management involves the process of handling, transporting, storing, collecting, recycling, and disposing of the waste generated. Optimal routing of solid waste collection trucks can be done using GIS modeling to ensure that waste is picked up, transferred to a transfer site, and reaches the landfills or recycling plants most efficiently. GIS modeling can also be used to select the best sites for landfills. The location and placement of garbage bins within city localities must also be analyzed. 

18. Customized Programs for Students

We all tend to have different strengths and paces of learning. There are different kinds of intelligence, and the curriculum only focuses on a few things. Data analytics can help modify academic programs to nurture students better. Programs can be designed based on a student’s attention span and can be modified according to an individual’s pace, which can be different for different subjects. E.g., one student may find it easier to grasp language subjects but struggle with mathematical concepts.

In contrast, another might find it easier to work with math but not be able to breeze through language subjects. Customized programs can boost students’ morale, which could also reduce the number of dropouts. Analysis of a student’s strong subjects, monitoring their attention span, and their responses to specific topics in a subject can help build the dataset to create these customized programs.

19. Real-time Tracking of Vehicles

Transportation plays a significant role in many activities. Every day, goods have to be shipped across cities and countries; kids commute to school, and employees have to get to work. Some of these modes might have to be closely monitored for safety and tracking purposes. I’m sure parents would love to know if their children’s school buses were delayed while coming back from school for some reason. 

Taxi applications have to keep track of their users to ensure the safety of the drivers and the users. Tracking has to be done in real-time, as the vehicles will be continuously on the move. Hence, there will be a continuous stream of data flowing in. This data has to be processed, so there is data available on how the vehicles move so that improvements in routes can be made if required but also just for information on the general whereabouts of the vehicle movement.

20. Analysis of Network Traffic and Call Data Records

There are large chunks of data-making rounds in the telecommunications industry. However, very little of this data is currently being used to improve the business. According to a MindCommerce study: “An average telecom operator generates billions of records per day, and data should be analyzed in real or near real-time to gain maximum benefit.” 

The main challenge here is that these large amounts of data must be processed in real-time. With big data analysis, telecom industries can make decisions that can improve the customer experience by monitoring the network traffic. Issues such as call drops and network interruptions must be closely monitored to be addressed accordingly. By evaluating the usage patterns of customers, better service plans can be designed to meet these required usage needs. The complexity and tools used could vary based on the usage requirements of this project.

This section contains project ideas in big data that are primarily open-source and have been developed by Apache.

Apache Hadoop is an open-source big data processing framework that allows distributed storage and processing of large datasets across clusters of commodity hardware. It provides a scalable, reliable, and cost-effective solution for processing and analyzing big data.

22. Apache Spark

Apache Spark is an open-source big data processing engine that provides high-speed data processing capabilities for large-scale data processing tasks. It offers a unified analytics platform for batch processing, real-time processing, machine learning, and graph processing.

23. Apache Nifi 

Apache NiFi is an open-source data integration tool that enables users to easily and securely transfer data between systems, databases, and applications. It provides a web-based user interface for creating, scheduling, and monitoring data flows, making it easy to manage and automate data integration tasks.

24. Apache Flink

Apache Flink is an open-source big data processing framework that provides scalable, high-throughput, and fault-tolerant data stream processing capabilities. It offers low-latency data processing and provides APIs for batch processing, stream processing, and graph processing.

25. Apache Storm

Apache Storm is an open-source distributed real-time processing system that provides scalable and fault-tolerant stream processing capabilities. It allows users to process large amounts of data in real-time and provides APIs for creating data pipelines and processing data streams.

Does Big Data sound difficult to work with? Work on end-to-end solved Big Data Projects using Spark , and you will know how easy it is!

This section has projects on big data along with links of their source code on GitHub.

26. Fruit Image Classification

This project aims to make a mobile application to enable users to take pictures of fruits and get details about them for fruit harvesting. The project develops a data processing chain in a big data environment using Amazon Web Services (AWS) cloud tools, including steps like dimensionality reduction and data preprocessing and implements a fruit image classification engine. 

The project involves generating PySpark scripts and utilizing the AWS cloud to benefit from a Big Data architecture (EC2, S3, IAM) built on an EC2 Linux server. This project also uses DataBricks since it is compatible with AWS.

Source Code: Fruit Image Classification

27. Airline Customer Service App

In this project, you will build a web application that uses machine learning and Azure data bricks to forecast travel delays using weather data and airline delay statistics. Planning a bulk data import operation is the first step in the project. Next comes preparation, which includes cleaning and preparing the data for testing and building your machine learning model. 

This project will teach you how to deploy the trained model to Docker containers for on-demand predictions after storing it in Azure Machine Learning Model Management. It transfers data using Azure Data Factory (ADF) and summarises data using Azure Databricks and Spark SQL . The project uses Power BI to visualize batch forecasts.

Source Code: Airline Customer Service App

28. Criminal Network Analysis

This fascinating big data project seeks to find patterns to predict and detect links in a dynamic criminal network. This project uses a stream processing technique to extract relevant information as soon as data is generated since the criminal network is a dynamic social graph. It also suggests three brand-new social network similarity metrics for criminal link discovery and prediction. The next step is to develop a flexible data stream processing application using the Apache Flink framework, which enables the deployment and evaluation of the newly proposed and existing metrics.

Source Code- Criminal Network Analysis

Trying out these big data project ideas mentioned above in this blog will help you get used to the popular tools in the industry. But these projects are not enough if you are planning to land a job in the big data industry. And if you are curious about what else will get you closer to landing your dream job, then we highly recommend you check out ProjectPro . ProjectPro hosts a repository of solved projects in Data Science and Big Data prepared by experts in the industry. It offers a subscription to that repository that contains solutions in the form of guided videos along with supporting documentation to help you understand the projects end-to-end. So, don’t wait more to get your hands dirty with ProjectPro projects and subscribe to the repository today!

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1. Why are big data projects important?

Big data projects are important as they will help you to master the necessary big data skills for any job role in the relevant field. These days, most businesses use big data to understand what their customers want, their best customers, and why individuals select specific items. This indicates a huge demand for big data experts in every industry, and you must add some good big data projects to your portfolio to stay ahead of your competitors.

2. What are some good big data projects?

Design a Network Crawler by Mining Github Social Profiles. In this big data project, you'll work on a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects.

Visualize Daily Wikipedia Trends using Hadoop - You'll build a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects. 

Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset - You will reconstruct the movielens dataset in a graph structure and use that structure to answer queries in various ways in this Neo4j big data project.

3. How long does it take to complete a big data project?

A big data project might take a few hours to hundreds of days to complete. It depends on various factors such as the type of data you are using, its size, where it's stored, whether it is easily accessible, whether you need to perform any considerable amount of ETL processing on the data, etc. 

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ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

• Which are the best tools and software for big data processing?
• Evaluate the security issues that face big data.
• An analysis of large-scale data for social networks globally.
• The influence of big data storage systems.
• The best platforms for big data computing.
• The relation between business intelligence and big data analytics.
• The importance of semantics and visualization of big data.
• Analysis of big data technologies for businesses.
• The common methods used for machine learning in big data.
• The difference between self-turning and symmetrical spectral clustering.
• The importance of information-based clustering.
• Evaluate the hierarchical clustering and density-based clustering application.
• How is data mining used to analyze transaction data?
• The major importance of dependency modeling.
• The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

• Discuss the privacy issues in big data.
• Evaluate the storage systems of scalable in big data.
• The best big data processing software and tools.
• Data mining tools and techniques are popularly used.
• Evaluate the scalable architectures for parallel data processing.
• The major natural language processing methods.
• Which are the best big data tools and deployment platforms?
• The best algorithms for data visualization.
• Analyze the anomaly detection in cloud servers
• The scrutiny normally done for the recruitment of big data job profiles.
• The malicious user detection in big data collection.
• Learning long-term dependencies via the Fourier recurrent units.
• Nomadic computing for big data analytics.
• The elementary estimators for graphical models.
• The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

• Evaluate the data mining process.
• The influence of the various dimension reduction methods and techniques.
• The best data classification methods.
• The simple linear regression modeling methods.
• Evaluate the logistic regression modeling.
• What are the commonly used theorems?
• The influence of cluster analysis methods in big data.
• The importance of smoothing methods analysis in big data.
• How is fraud detection done through AI?
• Analyze the use of GIS and spatial data.
• How important is artificial intelligence in the modern world?
• What is agile data science?
• Analyze the behavioral analytics process.
• Semantic analytics distribution.
• How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

• The difference between big data analytics and traditional data analytics methods.
• Why do you think the organization should think beyond the Hadoop hype?
• Does the size of the data matter more than how recent the data is?
• Is it true that bigger data are not always better?
• The debate of privacy and personalization in maintaining ethics in big data.
• The relation between data science and privacy.
• Do you think data science is a rebranding of statistics?
• Who delivers better results between data scientists and domain experts?
• According to your view, is data science dead?
• Do you think analytics teams need to be centralized or decentralized?
• The best methods to resource an analytics team.
• The best business case for investing in analytics.
• The societal implications of the use of predictive analytics within Education.
• Is there a need for greater control to prevent experimentation on social media users without their consent?
• How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

• The machine learning algorithms are used for fall recognition.
• The divergence and convergence of the internet of things.
• The reliable data movements using bandwidth provision strategies.
• How is big data analytics using artificial neural networks in cloud gaming?
• How is Twitter accounts classification done using network-based features?
• How is online anomaly detection done in the cloud collaborative environment?
• Evaluate the public transportation insights provided by big data.
• Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
• Discuss the current data lossless compression in the smart grid.
• How does online advertising traffic prediction helps in boosting businesses?
• How is the hyperspectral classification done using the multiple kernel learning paradigm?
• The analysis of large data sets downloaded from websites.
• How does social media data help advertising companies globally?
• Which are the systems recognizing and enforcing ownership of data records?
• The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

• The various errors and uncertainty in making data decisions.
• The application of big data on tourism.
• The automation innovation with big data or related technology
• The business models of big data ecosystems.
• Privacy awareness in the era of big data and machine learning.
• The data privacy for big automotive data.
• How is traffic managed in defined data center networks?
• Big data analytics for fault detection.
• The need for machine learning with big data.
• The innovative big data processing used in health care institutions.
• The money normalization and extraction from texts.
• How is text categorization done in AI?
• The opportunistic development of data-driven interactive applications.
• The use of data science and big data towards personalized medicine.
• The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

• The data-centric network of things.
• Big data management using artificial intelligence supply chain.
• The big data analytics for maintenance.
• The high confidence network predictions for big biological data.
• The performance optimization techniques and tools for data-intensive computation platforms.
• The predictive modeling in the legal context.
• Analysis of large data sets in life sciences.
• How to understand the mobility and transport modal disparities sing emerging data sources?
• How do you think data analytics can support asset management decisions?
• An analysis of travel patterns for cellular network data.
• The data-driven strategic planning for citywide building retrofitting.
• How is money normalization done in data analytics?
• Major techniques used in data mining.
• The big data adaptation and analytics of cloud computing.
• The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

• How is ultra-targeted marketing done?
• The transition of A/B testing from digital to offline.
• How can big data and A/B testing be done to win an election?
• Evaluate the use of A/B testing on big data
• Evaluate A/B testing as a randomized control experiment.
• How does A/B testing work?
• The mistakes to avoid while conducting the A/B testing.
• The most ideal time to use A/B testing.
• The best way to interpret results for an A/B test.
• The major principles of A/B tests.
• Evaluate the cluster randomization in big data
• The best way to analyze A/B test results and the statistical significance.
• How is A/B testing used in boosting businesses?
• The importance of data analysis in conversion research
• The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

• Assess the benefits and barriers of big data in the public sector.
• The best approach to smart city data ecosystems.
• The big analytics used for policymaking.
• Evaluate the smart technology and emergence algorithm bureaucracy.
• Evaluate the use of citizen scoring in public services.
• An analysis of the government administrative data globally.
• The public values are found in the era of big data.
• Public engagement on local government data use.
• Data analytics use in policymaking.
• How are algorithms used in public sector decision-making?
• The democratic governance in the big data era.
• The best business model innovation to be used in sustainable organizations.
• How does the government use the collected data from various sources?
• The role of big data for smart cities.
• How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

• Who uses big data analytics?
• Evaluate structure machine learning.
• Explain the whole deep learning process.
• Which are the best ways to manage platforms for enterprise analytics?
• Which are the new technologies used in data management?
• What is the importance of data retention?
• The best way to work with images is when doing research.
• The best way to promote research outreach is through data management.
• The best way to source and manage external data.
• Does machine learning improve the quality of data?
• Describe the security technologies that can be used in data protection.
• Evaluate token-based authentication and its importance.
• How can poor data security lead to the loss of information?
• How to determine secure data.
• What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

• Evaluate the 5G networks and IoT.
• Analyze the use of Artificial intelligence in the modern world.
• How do ultra-power IoT technologies work?
• Evaluate the adaptive systems and models at runtime.
• How have smart cities and smart environments improved the living space?
• The importance of the IoT-based supply chains.
• How does smart agriculture influence water management?
• The internet applications naming and identifiers.
• How does the smart grid influence energy management?
• Which are the best design principles for IoT application development?
• The best human-device interactions for the Internet of Things.
• The relation between urban dynamics and crowdsourcing services.
• The best wireless sensor network for IoT security.
• The best intrusion detection in IoT.
• The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

• The best cloud computing platforms for big data analytics.
• The parallel programming techniques for big data processing.
• The importance of big data models and algorithms in research.
• Evaluate the role of big data analytics for smart healthcare.
• How is big data analytics used in business intelligence?
• The best machine learning methods for big data.
• Evaluate the Hadoop programming in big data analytics.
• What is privacy-preserving to big data analytics?
• The best tools for massive big data processing
• IoT deployment in Governments and Internet service providers.
• How will IoT be used for future internet architectures?
• How does big data close the gap between research and implementation?
• What are the cross-layer attacks in IoT?
• The influence of big data and smart city planning in society.
• Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

• Which are the most used languages in big data?
• How is scala used in big data research?
• Is scala better than Java in big data?
• How is scala a concise programming language?
• How does the scala language stream process in real-time?
• Which are the various libraries for data science and data analysis?
• How does scala allow imperative programming in data collection?
• Evaluate how scala includes a useful REPL for interaction.
• Evaluate scala’s IDE support.
• The data catalog reference model.
• Evaluate the basics of data management and its influence on research.
• Discuss the behavioral analytics process.
• What can you term as the experience economy?
• The difference between agile data science and scala language.
• Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

• The biggest investment is in big data analysis.
• How are multi-cloud and hybrid settings deep roots?
• Why do you think machine learning will be in focus for a long while?
• Discuss in-memory computing.
• What is the difference between edge computing and in-memory computing?
• The relation between the Internet of things and big data.
• How will digital transformation make the world a better place?
• How does data analysis help in social network optimization?
• How will complex big data be essential for future enterprises?
• Compare the various big data frameworks.
• The best way to gather and monitor traffic information using the CCTV images
• Evaluate the hierarchical structure of groups and clusters in the decision tree.
• Which are the 3D mapping techniques for live streaming data.
• How does machine learning help to improve data analysis?
• Evaluate DataStream management in task allocation.
• How is big data provisioned through edge computing?
• The model-based clustering of texts.
• The best ways to manage big data.
• The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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What Are Some Real-World Examples of Big Data?

Since our first ancestors put ink to parchment, data has been part of the human experience.

From tracking the complex movements of the planets, to more basic things like bookkeeping, data has shaped the way we’ve evolved. Today, thanks to the internet, we collect such vast amounts of data that we have a whole new term to describe it: “big data.”

While big data is not only collected online, the digital space is undoubtedly its most abundant source. From social media likes, to emails, weather reports, and wearable devices, huge amounts of data are created and accumulated every second of every day. But how exactly is it used?

If you’re just starting out from scratch, then try this  free data short course on for size.

In this article, I’ll focus on some of the most big data examples out there. These are ways in which organizations—large and small—use big data to shape the way they work.

• What is big data and why is it useful?
• Big data in marketing and advertising
• Big data in education
• Big data in healthcare
• Big data in travel, transport, and logistics
• Key takeaways

First, let’s start with a quick summary of what big data is, and why so many organizations are scrambling to harness its potential.

1. What is big data and why is it useful?

“Big data” is used to describe repositories of information too large or complex to be analyzed using traditional techniques. For the most part, big data is unstructured, i.e. it is not organized in a meaningful way.

Although the term is commonly used to describe information collected online, to understand it better, it can help to picture it literally. Imagine walking into a vast office space without desks, computers, or filing cabinets. Instead, the whole place is a towering mess of disorganized papers, documents, and files. Your job is to organize all of this information and to make sense of it. No mean feat!

While digitization has all but eradicated the need for paper documentation, it has actually increased the complexity of the task. The skill in tackling big data is in knowing how to categorize and analyze it. For this, we need the right big data tools  and know-how. But how do we categorize such vast amounts of information in a way that makes it useful?

While this might seem like a fruitless task, organizations worldwide are investing huge amounts of time and money in trying to tap big data’s potential. This is why data scientists and data analysts are currently so in demand.

Learn more about it in our complete guide to what is big data .

But how is it done? Let’s take a look.

2. Big data in marketing and advertising

One of big data’s most obvious uses is in marketing and advertising. If you’ve ever seen an advert on Facebook or Instagram, then you’ve seen big data at work. Let’s explore some more concrete examples.

Netflix and big data

Netflix has over 150 million subscribers, and collects data on all of them. They track what people watch, when they watch it, the device being used, if a show is paused, and how quickly a user finishes watching a series.

They even take screenshots of scenes that people watch twice. Why? Because by feeding all this information into their algorithms, Netflix can create custom user profiles. These allow them to tailor the experience by recommending movies and TV shows with impressive accuracy.

And while you might have seen articles about how Netflix likes to splash the cash on new shows , this isn’t done blindly—all the data they collect helps them decide what to commission next.

Amazon and big data

Much like Netflix, Amazon collects vast amounts of data on its users. They track what users buy, how often (and for how long) they stay online, and even things like product reviews (useful for sentiment analysis ).

Amazon can even guess people’s income based on their billing address. By compiling all this data across millions of users, Amazon can create highly-specialized segmented user profiles.

Using predictive analytics , they can then target their marketing based on users’ browsing habits. This is used for suggesting what you might want to buy next, but also for things like grouping products together to streamline the shopping experience.

McDonald’s and big data

Big data isn’t just used to tailor online experiences. A good example of this is McDonald’s, who use big data to shape key aspects of their offering offline, too. This includes their mobile app, drive-thru experience, and digital menus.

With its own app, McDonald’s collects vital information about user habits. This lets them offer tailored loyalty rewards to encourage repeat business. But they also collect data from each restaurant’s drive-thru, allowing them to ensure enough staff is on shift to cover demand. Finally, their digital menus offer different options depending on factors such as the time of day, if any events are taking place nearby, and even the weather.

So, if it’s a hot day, expect to be offered a McFlurry or a cold drink…not a spicy burger!

3. Big data in education

Until recently, the approach to education was more or less one-size-fits-all. With companies now harnessing big data, this is no longer the case. Schools, colleges, and technology providers are all using it to enhance the educational experience.

Reducing drop-out rates with big data

Purdue University in Indiana was an early adopter of big data in education. In 2007, Purdue launched a unique, early intervention system called Signals, which was designed to help predict academic and behavioral issues.

By applying predictive modeling to student data (e.g. class prep, level of engagement, and overall academic performance) Purdue was able to accurately forecast which students were at risk of dropping out. When action was required, both students and teachers were informed, meaning the college could intervene and tackle any issues. As a result, according to one study, those taking two or more Signals courses were 21% less likely to drop out.

Improving the learner experience with big data

Some educational technology providers use big data to enhance student learning. One example of this is the UK-based company, Sparx , who created a math app for school kids. Using machine learning, personalized content, and data analytics, the app helps improve the pupil learning experience.

With over 32,000 questions, the app uses an adaptive algorithm to push the most relevant content to each student based on their previous answers. This includes real-time feedback, therefore tackling mistakes as soon as they arise. Plus, by collecting data from all their users across schools, Sparx gains broader insight into the overall learning patterns and pitfalls that students face, helping them to constantly improve their product.

Improving teaching methods with big data

Other educational technology providers have used big data to improve teaching methods. In Roosevelt Elementary School in San Francisco, teachers use an analytics app called DIBELS . The app gathers data on children’s reading habits so that teachers can see where they most need help.

Aggregating data on all pupils, teachers can group those with the same learning needs, targeting teaching where it’s most needed. This also encourages educators to reflect on their methods. For instance, if they face similar issues across multiple students, they might need to adapt their approach.

4. Big data in healthcare

From pharmaceutical companies to medical product providers, big data’s potential within the healthcare industry is huge. Vast volumes of data inform everything from diagnosis and treatment, to disease prevention, and tracking.

Electronic health records and big data

Our medical records include everything from our personal demographics to our family histories, diets, and more. For decades, this information was in a paper format, limiting its usefulness.

However, health systems around the world are now digitizing these data, creating a substantial set of electronic health records (EHRs). EHRs have vast potential. On a day-to-day level, they allow doctors to receive reminders or warnings when a patient needs to be contacted (for instance, to check their medication).

However, EHRs also allow clinical researchers to spot patterns between things like disease, lifestyle, and environment—correlations that would previously have been impossible to detect. This is revolutionizing how we detect, prevent, and treat disease, informing new interventions, and changes in government health policy.

Big data and wearable devices

Healthcare providers are always seeking new ways to improve patient care with faster, cheaper, more effective treatments. Wearables are a key part of this. They allow us to track patient data in real-time.

For instance, a heart monitor worn to detect blood pressure can allow doctors to track patients for extended periods at home, rather than relying on the results of a quick hospital test. If there’s a problem, doctors can quickly intervene. More importantly though, using big data analytics tools, information collected from countless patients can offer invaluable insights, helping healthcare providers improve their products. This ultimately saves money and lives.

Big data for disease tracking

Another application of big data in healthcare is disease tracking. The current coronavirus pandemic is a perfect example. Since the coronavirus outbreak began, governments have been scrabbling to launch track-and-trace systems to stem the spread of disease.

In China, for instance, the government has introduced heat detectors at train stations to identify those with fever. Because every passenger is legally required to use identification before using public transport, authorities can quickly alert those who may have been exposed. The Chinese government also uses security cameras and mobile phone data to track those who have broken quarantine. While this does come with privacy concerns, China’s approach nevertheless demonstrates the power of big data.

5. Big data in travel, transport, and logistics

From flying off on vacation to ordering packages to your front door, big data has myriad applications in travel, transport, and logistics. Let’s explore further.

Big data in logistics

Tracking warehouse stock levels, traffic reports, product orders, and more, logistics companies use big data to streamline their operations. A good example is UPS. By tracking weather and truck sensor data, UPS learned the quickest routes for their drivers.

This itself was a useful insight, but after analyzing the data in more detail, they made an interesting discovery: by turning left across traffic, drivers were wasting a lot of fuel . As a result, UPS introduced a ‘no left turn’ policy. The company claims that they now use 10 million gallons less gas per year, and emit 20,000 tonnes less carbon dioxide. Pretty impressive stuff!

Big data and city mobility

Big data is big business in urban mobility, from car hire companies to the boom of e-bike and e-scooter hire. Uber is an excellent example of a company that has harnessed the full potential of big data. Firstly, because they have a large database of drivers, they can match users to the closest driver in a matter of seconds.

But it doesn’t stop there. Uber also stores data for every trip taken. This enables them to predict when the service is going to be at its busiest, allowing them to set their fares accordingly. What’s more, by pooling data from across the cities they operate in, Uber can analyze how to avoid traffic jams and bottlenecks. Cool, huh?

Big data and the airline industry

Aircraft manufacturer, Boeing, operates an Airplane Health Management System. Every day, the system analyzes millions of measurements across their entire fleet. From in-flight metrics to mechanical analysis, the resulting data has numerous applications.

For instance, by predicting potential failures, the company knows when servicing is required, saving them thousands of dollars annually on unnecessary maintenance. More importantly, this big data provides invaluable safety insights, improving airplane safety at Boeing, and across the airline industry at large.

6. Big data in finance and banking

Fraud detection with big data.

Banks and financial institutions process billions of transactions daily—in 2022 there were more than 21,510 credit card transactions per second ! With the rise of online banking, mobile payments, and digital transactions, the risk of fraud has also increased.

Big data analytics can help in detecting unusual patterns or behaviors in transaction data. For instance, if a credit card is used in two different countries within a short time frame, it might be flagged as suspicious. By analyzing vast amounts of transaction data in real-time, banks can quickly detect and prevent fraudulent activities.

Personalized banking with big data

With over 78% of Americans banking digitally , banks are increasingly using big data to offer personalized services to their customers. By analyzing a customer’s transaction history, browsing habits, and even social media activities, banks can offer tailored financial products, interest rates, or even financial advice.

For instance, if a bank notices that a customer is frequently spending on travel, they might offer them a credit card with travel rewards or discounts.

7. Big data in agriculture

Precision farming with big data.

Farmers are using big data to make more informed decisions about their crops. How do they achieve this? Well, with sensors placed in fields measure the moisture levels, temperature, and soil conditions, as well as on tractors and other farm machinery.

Speaking of farm machinery, here’s an unusual but not for long example: d rones . By equipping drones with cameras can provide detailed aerial views of the crops, helping in detecting diseases or pests. Hobby drone giant DJI already produces its own line of drones for this purpose.

By analyzing this data, farmers can determine the optimal time to plant, irrigate, or harvest their crops, leading to increased yields and reduced costs.

Supply chain optimization with big data

Agricultural supply chains are complex, with multiple stages from farm to table. Big data can help in tracking and optimizing each stage of the supply chain. For instance, by analyzing data from transportation vehicles, storage facilities, and retail outlets, suppliers can ensure that perishable goods like fruits and vegetables are delivered in the shortest time, reducing wastage and ensuring freshness.

These examples can be integrated into the article to provide a more comprehensive overview of the diverse applications of big data across different sectors.

8. Key takeaways

In this post, we’ve explored big data’s real-world uses in several industries. Big data is regularly used by:

• Advertisers and marketers —to tailor offers and promotions, and to make customer recommendations
• Educational institutions —to minimize drop-outs, offer tailored learning, and to improve teaching methods
• Healthcare providers —to create new treatments, develop wearable devices, and to improve clinical research
• Transport and logistics —to streamline supply chain operations, improve airline safety, and even to save fuel and reduce carbon emissions
• Banking and finance —to help prevent fraud, as well as to offer customers tailored products based on their activity
• Agriculture —to help farmers perform as efficiently as possible and to monitor their crops

This taster of big data’s potential highlights just how powerful it can be. From financial services to the food industry, mining and manufacturing, big data insights are shaping the world we live in. If you want to be a part of this incredible journey, and are curious about a career in data analytics, why not try our free, five-day data analytics short course ?

Keen to explore further? Check out the following:

• How To Become A Data Consultant: A Beginner’s Guide
• Bias in Machine Learning: What Are the Ethics of AI?
• What Are Large Language Models? A Complete Guide

Big Data Projects

Main navigation.

Big Data Projects studies the application of statistical modeling and AI technologies to healthcare.

Mohsen Bayati studies probabilistic and statistical models for decision-making with large-scale and complex data and applies them to healthcare problems. Currently, an area of focus is AI’s use in oncology, and multi-functional research efforts are underway between the GSB and the School of Medicine. For example, AI is the right technology for oncology treatment decision-making methods because of its ability to synthesize rich patient data into prospective individual-level actionable recommendations and retrospectively learn from those decisions at scale.

However, the current set of AI technologies are focused heavily on detection and diagnosis, and major challenges remain in accessing and using the rich set of patient data for the oncologist’s patient-specific treatment decision. The clinical workflow then becomes mainly experience-driven, leading to many care disparities and with many hand-offs between oncology specialists. Dr. Bayati’s research enables developing an oncologist-centric decision support tool to push oncological decision-making and AI research further and in a multidisciplinary way by using AI for day-to-day oncology treatment decisions. He also studies graphical models and message-passing algorithms.

Mohsen Bayati , Faculty Director

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• Knowledge Base
• Starting the research process
• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved September 16, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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How Brainly’s Data & Analytics Department Uses Data for Decision-Making

The modern student looks to various sources for knowledge - but one of the best ways to learn is through a conversation with an educator. That’s what Brainly is about - connecting those who are learning with those who teach.

At Brainly, data is the key to decision-making, for both internal matters and determining how to best help their users. Understanding the roles related to Data Analysis, Data Science, Data Engineering, or Data Governance helps us to better understand how Brainly uses data and why it’s crucial. We interviewed three Brainly data experts from the Data & Analytics Department: Ewa Bugajska, Senior Lead Data Analyst in the Analytics Center of Excellence, Katarzyna Bodzioch-Marczewska, Solutions Architect in Data Governance, and Tomasz Sienkiewicz, the Director of the Data & Analytics Department.

What can you tell me about your Data organization - what makes it so unique?

[TS] We see that companies have different approaches to how the Data or Analytics teams operate - quite often it’s either a centralized or a decentralized model. In a centralized model there’s one Data team that receives requests from the whole organization; in the decentralized model all Data experts are embedded in the business units they’re supporting and they don’t work together.

What makes Brainly unique is our hybrid organizational model in terms of how we use data and how Data Analysts work. On one hand, they’re embedded in teams, working with products, marketing, and more. At the same time, we have a central team to ensure that all Data Analysts are able to cooperate and share knowledge with each other, work according to similar standards, and are hired and onboarded using a standardized process.

How are Data teams set up at Brainly?

[TS] Basically, our data teams are divided into 3 main areas. The Data Analytics team helps stakeholders make data-driven decisions. Data Analysts analyze data, draw conclusions, find opportunities, and based on these, stakeholders make decisions.

Our AI/ML team uses data to build ML-based products, mostly focusing on how to use data to build solutions for end-users. And Brainly’s Data Engineers make sure that data is collected, transformed, and stored properly.

Can you tell us a bit more about how the central Data Analytics Department is divided?

[TS] There are 2 areas - the Analytics “Center of Excellence” and Data Governance.

[EB] My area is the Analytics Center of Excellence, where we make sure to hire the best data analytics talent, in cooperation with our Talent Acquisition team, and provide them with an onboarding experience that prepares them for working with our data and systems. I support Data Analysts’ professional growth, define career paths, organize opportunities for the analysts to share and acquire knowledge, as well as lead initiatives, focused on standardizing some of the work that all analysts do, regardless of the team they work in.

[KB] My area, Data Governance, is about controlling some aspects of our data management system. I build policies and processes to make sure our data quality meets our standards and our data is safe but accessible for everyone who needs it. Right now, my main focus is the data catalog that we’re building with Data Analysts and Data Engineers.

Why is data so important to Brainly and how do users benefit from it? 

[TS] The way I see it, when you run a business, you make decisions. The higher in the company hierarchy, the more important the decisions - they’re made in different ways, based on your gut, past experiences, and biases. Most importantly, you make decisions based on insights and data.

Generally speaking, companies that use data to make decisions tend to be more successful. This is how we act at Brainly - we use data to make big decisions so we can grow and build quality products as quickly as possible.

What does Brainly do to support their “Data Culture”?

[TS] We support access to and the understanding of data. For example, Kasia’s project increases accessibility, and Ewa’s A/B testing project helps us understand that data.

[KB] We support the growth of the Data Community. One way of doing that is gathering tribal knowledge about data and building a data catalog. The data catalog tool that we’re currently onboarding will help everybody discover data faster, understand it and collaborate around it. It will be a trustworthy and easily accessible source of information about the data for everyone in Brainly.

[EB] One of the projects that we’re currently running in partnership with the Product division is focused on improving our approach to running A/B tests within the whole company through building a common framework, proper education, organization, and a standardized approach to reporting. Thanks to that, our Analysts can be more efficient when summarizing the A/B test results, and business stakeholders understand the outcomes so they can make data-driven decisions faster.

Can you describe Brainly’s company culture and how you work?

[EB] Brainly is a company with a great mission and amazing people who help to realize it. You rarely have the privilege to work with people who care about each other and the company’s mission and who are so open to learning and sharing knowledge. 

Since we’re an education company, it may sound obvious that learning is a big deal for us, but any initiative that is focused on sharing knowledge is well received. We learn and grow together, in more than just our own area of expertise.

Do you feel the Brainly value “Stay Curious: Always wonder. Always explore” represents your work at Brainly?

[TS] Absolutely. I like to say, “Win or learn” instead of “Win or Lose”!

[EB] Even though we aren’t a product team, we still have regular retrospective meetings to identify if there are issues we should address right away, and we also actively ask for feedback within the processes we participate in.

‍ [KB] We talk about technical problems and review our solutions. Nothing gets swept under the rug. Our team members are open to doing it because they feel they are in a safe space. It’s always a great opportunity to share experiences and learn from each other.

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39 Big Data Examples and Applications

Check out these examples of how companies use big data to predict the next big step.

By helping companies uncover hidden patterns and trends, big data is now used in nearly every industry to plan future products, services and more. As of 2022, in fact, approximately 97 percent of businesses are investing in big data’s growing power.

At its best, though, big data grounds and enhances human intuition.

These companies are using big data to shape industries from marketing to cybersecurity and much more.

Big Data Examples to Know

• Marketing:  forecast customer behavior and product strategies.
• Transportation: assist in GPS navigation, traffic and weather alerts.
• Government and public administration: track tax, defense and public health data.
• Business: streamline management operations and optimize costs.
• Healthcare: access medical records and accelerate treatment development.
• Cybersecurity: detect system vulnerabilities and cyber threats.

Big Data Examples in Marketing 

Big data and marketing go hand-in-hand, as businesses harness consumer information to forecast market trends, buyer habits and other company behaviors. All of this helps businesses determine what products and services to prioritize.

AnthologyAI

Location: New York, New York

AnthologyAI empowers consumers to take control of their personal data and earn money from it using the company’s mobile app. Simultaneously, the company offers businesses access to a wealth of consumer behavioral data through its intelligent Knowledge Graph, which is compiled with the explicit consent of users. AnthologyAI collects over 100 million data points daily, covering various aspects such as media consumption and financial transactions, enabling enterprises to gain valuable insights while maintaining consumer privacy.

Location: Marina del Rey, California

System1 develops software to support streamlined, effective digital marketing operations. The company uses AI, machine learning and data science to power its response acquisition marketing platform, known as RAMP, which helps brands engage high-intent customers at the right time.

Location: Los Angeles, California

VALID is VideoAmp ’s big data and technology engine that’s designed to power solutions for ensuring ad content reaches target audiences and measuring ad performance across TV, streaming and digital platforms, while still respecting consumer privacy. The company’s offerings are designed to serve agencies, brands and publishers.

Centerfield

Location: Los Angeles, California 

Getting more information on customers is a great way to discover their desires and how to meet them. Centerfield analyzes customer data to uncover new insights into customer behavior, which influences the marketing and sales techniques it recommends to clients. The company is able to use this information to discover new customers that fit the same patterns as existing customers.

Location: San Mateo, California

At 3Q/DEPT , big data underpins strategies that blend search engine, social, mobile and video marketing . The in-house Decision Sciences team perfects the mix of marketing channels by studying data on transactions, consumer behavior and more, using multi-touch attribution . This big data-informed technique allows analysts to distinguish between effective and ineffective ad impressions on a micro level.

Location: Glendale, California 

With insight help from big data, DISQO offers products for measuring brand and customer experience. The company specializes in research and marketing lift (sales) efforts, providing API and optimization software for tracking key performance and outcome metrics. Over 125 marketing firms utilize DISQO research tools, while over 300 firms utilize its lift solutions.

Location: Seattle, Washington 

Like Facebook and Google, Amazon got sucked into the adtech business by the sheer amount of consumer data at its disposal. Since its founding in 1994, the company has collected reams of information on what millions of people buy, where those purchases are delivered and which credit cards they use. In recent years, Amazon has begun offering more and more companies — including marketing companies — access to its self-service ad portal, where they can buy ad campaigns and target them to ultra-specific demographics, including past purchasers.

Marketing Evolution

Location: New York, New York 

Marketing Evolution pulls data from hundreds of online and offline sources to create detailed consumer profiles that encompass beliefs, location and purchasing habits as well as environmental data like current local weather conditions. Analysts then use a software stack dubbed the “ROI Brain” to craft targeted campaigns where every element, from the messaging itself to the channel it arrives through, reflects individual users’ preferences.

Big Data Examples in Transportation 

Navigation apps and databases, whether used by car drivers or airplane pilots, frequently rely on big data analytics to get users safely to their destinations. Insights into routes, travel time and traffic are pulled from several data points and provide a look at travel conditions and vehicle demands in real time.

Location: Fully Remote

Vizion provides shipping container tracking for freight companies, using multiple data sources to keep close tabs on thousands of ships, containers, railways and ports around the world. Using geocodes for locations and facilities, it can provide GPS coordinates to shippers and cargo owners, logistics service providers and freight forwarders across ocean and rail.

Location: Chicago, Illinois 

FourKites ’ platform uses GPS and a host of other location data sources to track packages in real time, whether they’re crossing oceans or traveling by rail. A predictive algorithm then factors in data on traffic, weather and other external factors to calculate the estimated times of arrival for packages, so FourKites clients can give customers advance warning about delays and early deliveries — while also avoiding fees.

Location: San Francisco, California 

As a rideshare company, Uber monitors its data in order to predict spikes in demand and variations in driver availability. That information allows the company to set the proper pricing of rides and provide incentives to drivers so the necessary number of vehicles are available to keep up with demand. Data analysis also forms the basis of Uber’s estimated times of arrival predictions, which goes a long way toward fulfilling customer satisfaction.

Location: Fairfield, Connecticut 

GE ’s Flight Efficiency Services, adopted in 2015 by Southwest Airlines and used by airlines worldwide, can optimize fuel use, safety and more by analyzing the massive volumes of data airplanes generate. How massive? One transatlantic flight generates an average of 1,000 gigabytes . GE’s scalable aviation analytics takes it all in, crunching numbers on fuel efficiency, weather conditions, and passenger and cargo weights.

HERE Technologies

The experts at HERE Technologies leverage location data in several ways, most notably in the HD Live Map , which feeds self-driving cars the layered, location-specific data they need. The map pinpoints lane boundaries and senses a car’s surroundings. Thanks to data from intelligent sensors, the map can see around corners in a way the human eye can’t. And a perpetual stream of intel from fleets of roaming vehicles helps the map warn drivers about lane closures miles away.

Big Data Examples in Government

To stay on top of citizen needs and other executive duties, governments may look toward big data analytics. Big data helps to compile and provide insights into suggested legislation, financial procedure and local crisis data, giving  authorities an idea of where to best delegate resources.

RapidDeploy

RapidDeploy is a public safety company that creates reporting and analytics software and operates a data platform for emergency response centers. Using AI and big data to increase location accuracy and situational awareness, RapidDeploy’s products are meant to offer insights about how to find callers faster, improve emergency care and reduce response time.

RapidSOS funnels emergency-relevant data to first responders out on 911 calls. Thanks to partnerships with Apple, Android providers and apps like Uber, the company can pull relevant data from patients’ phones and wearables in crisis situations. Free to public safety offices, Clearinghouse integrates into pre-existing call-taking and dispatch channels so the data — including GPS location data and real-time sensor data — reaches EMTs more reliably and securely.

Big Data Examples in Business

Succeeding in business means companies have to keep track of multiple moving parts — like sales, finances and operations — and big data helps to manage it all. Using data analytics, professionals can follow real-time revenue information, customer demands and managerial tasks to not only run their organization but also continually optimize it.

Location: San Francisco, California

DataGrail offers an enterprise platform intended to protect brands and foster customer trust through streamlined data privacy management solutions. For example, businesses can use DataGrail’s automated data mapping capabilities to inventory where sensitive data is stored across their tech stacks. DataGrail also has tools for customizing the consent experience so it contributes to a positive customer journey.

Location: Pasadena, California

Spokeo describes itself as “a people intelligence service,” providing users with a search engine for finding information about and connecting with people. The data on Spokeo’s site comes from billions of records, including social media profiles, property records and court records. Spokeo also offers enterprise solutions, giving businesses access to important data that can support use cases like fraud investigations and debt collections.

PureSpectrum

Location: Westlake Village, California

PureSpectrum builds technology for conducting market research to inform business decisions. Its solutions allow researchers to efficiently create and manage surveys, target specific respondents, access comprehensive data on each question and easily export data for presentations and reports.

Monte Carlo

Monte Carlo ’s data observability platform offers solutions for identifying data quality issues so they can be quickly resolved, cutting down on potential downtime. Businesses across industries such as retail, life sciences and financial services use Monte Carlo’s technology to ensure data reliability.

LoanStreet Inc.

LoanStreet offers a digital platform for financial institutions like banks, credit unions, and direct lenders to manage and trade loans as assets. The platform includes features such as a digital loan marketplace, automated loan reporting, loan servicing and analytics on loan performance, all accessible through a single dashboard. LoanStreet, headquartered in New York City, helps over 1,300 financial institutions streamline their loan management and diversify their balance sheets.

Arity is a data and analytics firm that works in the automotive insurance space, sourcing data from nearly 30 million connected devices. Operating independently under the umbrella of the Allstate Insurance Corporation, it uses AI to analyze driver behavior on behalf of local governments and insurance providers, who then use its data and insights to make pricing and policy decisions. 

Location: Fully Remote Enigma ’s big data analysis platform takes vast data sets of information ranging from merchant transactions and financial health indicators to identity and firmographic information. It then returns insights on private businesses, guiding its clients’ B2B decision making. These data-driven insights are so more accurate than previous methods of investigations into areas like financial health, for example. As a result, only applications that are likely to be approved will be sent forward in the application process, which can lead to increased approval rates on loans. 

Forge provides tech, data and marketplace services for the private securities market. Private securities, which include privately traded equities, fractional loans and derivatives, are traded between individuals rather than on an exchange the way publicly traded stocks are. The Forge Intelligence app uses big data to allow users to see real-time trading activity and pricing information in the private market.

The PC-based Skupos platform pulls transaction data from 15,000 convenience stores nationwide. Over the course of a year, that adds up to billions of transactions that can be dissected using the platform’s business analytics tools. Store owners can use the insights to determine location-by-location bestsellers and set up predictive ordering . Distributors, meanwhile, can forecast demand, and brands can analyze a constant influx of product sales data.

Companies often scatter their data across various platforms, but Salesforce is all about cohesion. Their customer relationship management platform integrates data from various facets of a business, like marketing, sales, and services, into a comprehensive, single-screen overview. The platform’s analytics provide automatic AI-informed insights and predictions on metrics like sales and customer churn . Users can also connect Salesforce with outside data management tools rather than toggling between multiple windows.

Location: Los Gatos, California 

The premise of Netflix ’s first original TV show — the David Fincher-directed political thriller House of Cards — had its roots in big data. Netflix invested $100 million in the first two seasons of the show, which premiered in 2013, because consumers who watched House of Cards also watched movies directed by David Fincher and starring Kevin Spacey . Executives correctly predicted that a series combining all three would be a hit. 

Today, big data impacts not only which series Netflix invests in, but how those series are presented to subscribers. Viewing histories, including the points at which users hit pause in any given show, reportedly influence everything from the thumbnails that appear on their homepages to the contents of the “Popular on Netflix” section

Location: Amsterdam, Netherlands

Adyen is a global fintech solution that enables businesses like Meta, Uber, Spotify and L’Oréal to handle online, mobile and in-store transactions, alongside risk management, from a unified platform. Based in Amsterdam, Adyen has a worldwide presence with 27 offices, including a location in Bengaluru, India.

Location: Boston, Massachusetts

Nasuni is a hybrid cloud storage solution designed to support business growth by offering scalability and built-in security through a cloud-native architecture. Its platform claims to help eliminate data silos and simplify management, without requiring changes to existing workflows. Nasuni is headquartered in Boston and has other offices in Marlborough, Massachusetts, and Cary, North Carolina. 

Big Data Examples in Healthcare

When it comes to medical cases, healthcare professionals may use big data to determine the best treatment. Patterns and insights can be drawn from millions of patient data records, which guide healthcare workers in providing the most relevant remedies for patients and how to best advance drug development.

Location: Chicago, Illinois

Kalderos is a healthtech company building solutions to support compliant drug discount programs. Its platform brings together data from multiple sources to identify and resolve noncompliance and improve transparency and collaboration among stakeholders. Kalderos says its technology has identified more than $1 billion in noncompliance so that organizations across the healthcare landscape can avoid revenue losses and focus their efforts on serving patients.

Tempus ’ tablet-based tool has made file cabinets of medical records portable and accessible in real time. Designed to inform physicians’ decisions during appointments, Tempus trawls huge digital archives of clinical notes, genomic data, radiology scans and more to turn out data-driven treatment recommendations . These recommendations are personalized, too, though — based on data from past cases in which patients had similar demographic traits, genetic profiles and cancer types.

SOPHiA GENETICS

Location: Boston, Massachusetts 

SOPHiA GENETICS provides data solutions for healthcare professionals based on big data metrics, with specializations in oncology, inherited diseases and biopharmacy. The company’s SOPHiA DDM platform provides multimodal insights from clinical, biological, genomics and radiomics datasets for screening and diagnosis purposes. Sophia Genetics’ technology has analyzed over one million genomic profiles, and intends to provide future insight support for data relating to proteomics, metabolomics and more.

Garner Health

Garner Health offers users access to data-powered search tools that patients can use to connect with doctors. The app provides users with a list of local doctors based on the user’s medical needs. It offers users details about possible providers that include appointment availability and patient reviews.

Propeller Health

Location: Madison, Wisconsin

Propeller Health reimagined the inhaler as an IoT gadget. Widely used for the treatment of asthma and other chronic obstructive pulmonary diseases, the sensor-equipped inhalers export data to a smartphone app that tracks inhaler use, as well as environmental factors like humidity and air quality. Over time, in-app analytics can help identify possible flare-up triggers and produce reports that patients can share with their doctors.

Location: Eschborn, Hessen, Germany and San Francisco, California

Innoplexus ’ Ontosight life sciences data library, featuring search tools rooted in AI and blockchain technology , was compiled to help pharmaceutical researchers sift more quickly through relevant data and streamline drug development. A truly massive repository, it includes everything from unpublished PhD dissertations to gene profiles to a whopping 26 million pharmaceutical patents.

Big Data Examples in Cybersecurity

As cyber threats and data security concerns persist, big data analytics are used behind the scenes to protect customers every day. By reviewing multiple web patterns at once, big data can help identify unusual user behavior or online traffic and defend against cyber attacks before they even start.

Location: Foster City, California

Cyber attacks are so sophisticated and prevalent that it’s hard for the research into prevention to catch up. Luckily, big data can provide some of the same insights by analyzing patterns in cyber attacks and recommending strategies for staying safe. Exabeam analyzes data from companies that have suffered attacks to help companies build models of what common attacks look like and how to detect and deter them before they are successful.

Splunk ’s Security Operations Suite relies on big data to identify and respond to cybersecurity threats and fraud. Systemwide data flows through Splunk’s analytics tools in real time, allowing it to pinpoint anomalies with machine learning algorithms. Splunk’s data-driven insights also help it prioritize concurrent breaches, map out multipart attacks and identify potential root causes of security issues.

Own Company

Location: Englewood Cliffs, New Jersey 

Own  is a cloud-based platform for data security, backup, archiving and sandbox seeding. Using big data insights, the software provides automated backups and security risk metrics for Salesforce, Microsoft and ServiceNow data environments. Own has partnered with AWS, nCino and Veeva to provide data protection and compliance services for businesses across the country .

Arista Networks

Location: Santa Clara, California

Arista ’s Awake Security platform works a bit like the human brain. Sensors scan data where it’s stored, whether in the cloud or embedded in an IoT device. Much as our nerves relay information back to our brain, Awake’s sensors port key findings back to the Awake Nucleus, a centralized deep learning center that can detect threats and parse the intent behind unusual data. 

In certain cases, it’s used in collaboration with a network of human cybersecurity experts who are up to date on the latest cyber attack techniques and industry-specific protocols.

Exterro Inc.

Location: Beaverton, Oregon

Exterro ’s Forensic Toolkit , or FTK, stores enterprise-scale data in a straightforward database structure, processing and indexing it up front. In an emergency situation, that allows for quicker searches that are further accelerated through the use of distributed processing across an array of computers. FTK makes full use of its hardware resources, focusing all of its available processing power on extracting evidence that clients can leverage in civil and criminal cases.

Mia Goulart, Sara B.T. Thiel, Brennan Whitfield, Margo Steines, Ana Gore and Tammy Xu contributed reporting to this story.

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Top Big Data Projects to Sharpen Your Skills and Build Your Big Data Portfolio

Big data is quickly becoming a sought-after IT field. It is an interesting subject that helps you to uncover patterns in large sets of data. Big data skills are in high demand, and this career path has a promising future. Employers are constantly on the lookout for skilled big data specialists. So, if you are an upcoming big data professional, the best way to advance your career is to work on big data projects.

Data science projects provide a means of putting theoretical concepts into practice. In this article, we will discuss some exciting big data project ideas that you can try out to put your big data skills to the test. These projects are divided into three categories: beginner, intermediate, and advanced. You should experiment with these projects based on your skill level.

Find your bootcamp match

5 skills that big data projects can help you practice.

Big Data projects offer a quick way to gain hands-on experience and advance your education. Theoretical knowledge alone will not suffice to develop your skills and proficiency in big data. Therefore, it is critical to practice with projects that mimic a real-world work environment. Here are a few examples of the big data skills that projects can help you practice. 

• Data Analytics. This is one of the most important big data skills. Big data experts should possess analytical skills to understand complex data and solve problems using data science tools.
• Data Visualization Skills. This is the ability to interpret and present data to convey a particular message. Being able to visually present data analysis plays a major role in a big data career. Anyone interested in a career in big data should develop their data visualization skills. 
• Programming Skills. Big data projects can improve your knowledge and expertise in data analytics and programming languages such as Scala, C, Python, and Java. To be considered an expert in big data, you must have a solid understanding of the fundamentals of algorithms, data structures, and object oriented languages.
• Data Mining. Learning data mining through projects can provide you with practical knowledge in data mining concepts and data mining tools like KNIME, Apache Mahout, or Rapid Miner. Having strong data mining skills is fundamental to succeeding in a career in big data.
• Use of Cloud Services. Advanced projects can introduce you to the use and application of public and hybrid clouds. As you must utilize clouds to store data, it is important to be familiar with cloud software providers such as Amazon Web Services (AWS), Microsoft Azure, OpenStack, Vagrant, Docker, and Kubernetes.

Best Big Data Project Ideas for Beginners 

Data science could be difficult and confusing to learn for beginners. However, it becomes easier with constant practice. Taking on projects that expose you to big data is the best way to grasp the various concepts and terminologies and build your skills. Here are a few big data projects for beginners.

Health Status Prediction

• Big Data Skills Practiced: Data Analytics, Programming

This project involves gathering information on different health conditions such as breast cancer, diabetes, and Parkinson’s disease. By compiling and analyzing this data in datasets, we can build a system to identify risk factors and predict the likelihood of these diseases.

Fake News Detection

• Big Data Skills Practiced: Programming, Data Analytics

Another project to consider is the fake news detection project. The goal of this project is to determine the authenticity of information found on social media platforms. You can achieve this with Python programming. You can employ TfidfVectorizer and PassiveAggressiveClassifier to analyze news and classify real news from false news. 

Forest Fire Prediction System

• Big Data Skills Practiced: Data Mining, Data Analytics

The forest fire prediction system employs data science capabilities to predict and control the destructive nature of wildfires. You’ll need to utilize k-means clustering to identify major fire hotspots and the probability of future wildfire occurrence. For more accurate predictions, you may also include meteorological data to identify the seasons and common times when wildfires might occur.

Breast Cancer Classification Program

If you’re looking for a big data project in the healthcare industry, this is the one to take on. The breast cancer detection system identifies cancer at an early stage by checking and analyzing patients’ databases. This enables patients to take necessary preventive measures.

Real-Time Traffic Analysis

This project entails the creation of a system that monitors traffic on major roads and recommends alternate routes. You can instruct the system to use real-time analysis of traffic to program traffic lights so that they remain green for a longer period of time on busier roads and for a shorter period of time on free roads.

Best Intermediate Big Data Project Ideas 

Taking on a few beginner projects will help you to develop proficiency in the fundamental concepts of big data. When you feel confident in your abilities, you can advance to intermediate projects. Intermediate projects take you out of your comfort zone and introduce you to more advanced big data applications. Here are a few ideas for intermediate big data projects.

Speech Emotion Recognition

This project takes students through the use and applications of different libraries. Speech emotion recognition (SER) is a system that analyzes human speech with librosa to pick up notes that relate to human emotion and affective states. This project may be a bit complicated because human emotions are subjective to each person.

Gender and Age Detection with Data Science

• Big Data Skills Practiced: Data Analytics, Data Visualization

In this deep learning project idea, you will create a system that processes images to predict the gender and age group of a person. You’ll learn to use computer vision networks and apply the principles to build a convolutional neural network. You will also utilize models trained by Hassner and Gil Levi to analyze the Adience datasets. 

Building Chatbots

• Big Data Skills Practiced: Programming, Data Mining

This is a simple mini-project that you can try out that uses artificial intelligence. It guides you through the process of creating a chatbot using Python programming. Chatbots are used by businesses to quickly respond to massive datasets of customer queries and messages. Chatbots analyze client messages and respond appropriately.

Analysis of Airline Datasets

This is another data science project idea that is perfect for intermediate students to practice their skills. Airlines employ detailed analysis techniques to monitor air routes and maximize efficiency. For this project, you’ll need to consider a wide range of factors such as the number of people flying over a certain period, delays, and the best days of the week to avoid delays.

Driver Drowsiness Detection in Python

Thousands of accidents happen every year due to drivers falling asleep as they drive. In this project, you will design a system that can identify drivers and wake them up with an alarm. You will use Keras and OpenCV. Keras helps us to analyze the face and the eyes, while OpenCV allows us to detect drowsiness by checking if the eyes are open or not. 

Best Advanced Big Data Project Ideas

If you consider yourself an expert or have advanced mastery of big data techniques, you should try out some advanced big data project ideas. Here are a few examples.

Build a Scalable Event-Based GCP Data Pipeline

• Big Data Skills Practiced: Programming, Use of Cloud Services

This is a technical project involving designing an event-based data integration system using Dataflow on the Google Cloud Platform. When an event occurs, the system automatically updates the data. You’ll use Python programming languages and other services such as Cloud Composer, Google Cloud Storage, Pub-Sub, Cloud Functions, BigQuery, and BigTable.

Generating Image Captions  

• Big Data Skills Practiced: Data Mining, Data Visualization 

It is unusual for an image to be posted on social media without related image captions. This deep learning project idea involves handling large datasets that correlate with images and captions. To analyze the image, you will need to use deep learning techniques and image processing. Then use artificial intelligence to generate appropriate captions for the image.

Snowflake Real-Time Data Warehouse Project for Beginners-1

If you’re looking for a challenging project, this is it. Snowflake is a data warehouse company that makes use of cloud computing and data storage services. This project mimics the Snowflake architecture. You’ll learn how to use SQL to create a data warehouse in the cloud for a business. 

Web Server Log Processing

• Big Data Skills Practiced: Data Analytics, Data Mining

This project is ideal for advanced big data analysts because it involves processing a web server log to extract data that can be used for web page ads and search engine optimization (SEO). A web server log contains a list of page requests and other browsing activities. 

Log Analytics Project with Spark Streaming and Kafka

Log analytics is the process of evaluating and analyzing logs from programming technologies. A log contains a list of messages that describes the operation of a system. In this project, you’ll use real-world production logs from NASA Kennedy Space center WWW servers to perform log analytics with web visualization apps like Apache Spark and Kafka. 

Electricity Price Forecasting

This is one of the most exciting big data project ideas. It involves designing a system that predicts electricity prices by leveraging big data sets. You will need to use an SVM classifier to analyze the data and predict future electricity prices. To improve the accuracy of the system and eliminate irrelevant data, you would have to employ Grey Correlation Analysis (GCA) and Principle Component Analysis.

Big Data Starter Project Templates

A starter template is a guide that contains source codes that can be easily modified to meet the needs of your project. It simplifies concepts so you don’t have to start from scratch. You can use big data starter templates to assist you during your projects.

• Classify 1994 Census Income Data . This template involves the development of a model that analyzes a dataset to predict if a person’s income in the US is more than $50,000.
• Analyze Crime Rates in Chicago . This template is a dataset analysis of reported crime in Chicago from 2001 to the present.
• Text Mining Project . This is a template of statistical text analysis on a Star Wars movie script. It employs data visualization and natural language process techniques.

Next Steps: Start Organizing Your Big Data Portfolio

A big data portfolio highlights your skills, experience, and expertise. It works just like a resume. However, it also includes projects that demonstrate that you have the skills to succeed in getting a job in data science.

When you have a well-packaged portfolio that advertises your technical expertise, you have a higher chance of landing the job. Here are some tips to help you build your big data portfolio.

Include Your Best Big Data Projects

You must demonstrate your best projects, the ones you are most proud of, and effectively describe your skills to potential employers. You want your employer to have a clear picture of your abilities so that you can present yourself as a qualified candidate.

Showcase Your Actual Projects

Simply listing your projects in your portfolio is insufficient, you must also include the work itself. Include a link to the actual, launched projects in your portfolio . You can accomplish this by using a free software repository such as Github, Bitbucket, or Gitlab. This would persuade prospective employers of your abilities even more.

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Venus, Software Engineer at Rockbot

Include Relevant Projects

When applying for a job, it is best to include projects relevant to the job role. You should include relevant project details so that interviewers can assess your abilities. This increases your chances of being chosen as the best candidate.

Big Data Projects FAQ

The three types of big data are structured data, unstructured data, and semi-structured data. Structured data is highly organized and defined by parameters. Unstructured data is any data set that contains less than 20 percent structured data. Finally, semi-structured data is a category of data that falls between structured and unstructured data.

There are six big data analysis techniques, namely, A/B testing, Data Fusion and Data Integration, Data Mining, Machine Learning, Natural Language Processing, and Statistics.

There are four types of analytics in big data. They are Descriptive Analytics, Diagnostics, Predictive Analytics, and Prescriptive Analytics.

The best big data technologies are Apache Hadoop, Apache Spark, MongoDB, Cassandra, and Tableau.

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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How Brainly Powers Viral Marketing with Customer Feedback

You won’t know what your customers think unless you ask.

To make smart business decisions, you need customer feedback . But sometimes, staying in touch with your user base is easier said than done.

For companies with huge international audiences, the process of gathering, sharing, and analyzing feedback needs to be a well-oiled machine ready to operate on a massive scale. When you have millions of customers and opinions to account for, you need larger sample sizes.

At the same time, enterprise-level survey solutions are often a hassle to implement. And that doesn’t help small marketing teams that want to capture opinions about current events.

Those factors led Brainly , the global online learning platform, to turn to Survicate. Our tool lets them set up a multi-team surveying process. The customer data helps every department make more data-driven decisions and learn their audience’s current thoughts and insights.

We talked to Noah Berg, the Outreach Manager at Brainly, about how Survicate surveys help power his company’s marketing strategy. Noah told us:

• Why Brainly chose Survicate
• How Survicate helps Brainly create viral content (that doubled their blog traffic)
• How recurring surveys help them stay on top of user needs

If you’re looking for inspiration on how surveys can power your marketing – read on!

Why Brainly turned to customer feedback surveys 

Brainly is the largest online knowledge-sharing community of over 300 million students, parents, and experts. The company has users in over 35 countries, and with the funding of $150M, it’s an indisputable market leader.

Brainly’s staff knew that with a user base that large, they needed to capture feedback efficiently to keep up with their audiences’ needs. The sheer number of visitors going through their page every day made numerical user behavior data insufficient. Brainly needed to know the “why” behind the visitors’ actions to improve their platform, tailor the message to the users, and define their personas .

In the beginning, Brainly looked for a survey software that would let them ask focus groups about new product features . All product improvements at the company are heavily based on the users’ opinions, so collecting their feedback at scale was necessary to grow.

Survicate struck a perfect balance between ease of use and advanced features. The product team at Brainly appreciated the targeted website surveys that made it easier to segment the vast user base.

Soon, other departments realized that they could also use the power of customer feedback. And thanks to the unlimited seats and workspaces that let different teams share the same Survicate account, they quickly got on board.

Survicate: Unlimited users, speed, and reliability 

Brainly has been with Survicate since 2015. They have run over 800 surveys and gathered almost two million responses – with an average of a total of 25 790 responses every month. 

Currently, ten people at Brainly regularly set up their surveys (with more people being able to access the data). Here are their most common use cases:

• The product team uses Survicate to learn more about the motivations behind user behavior, collect feedback on new changes and gather product ideas
• The marketing team runs Net Promoter Score surveys to measure the loyalty of different customer segments and identify product issues
• The communications team uses surveys to gather data for content used in their PR and marketing campaigns. They also run a recurring “Brainly value” survey to stay on top of their users' opinions and impressions.

But there are more reasons why Brainly has stuck with Survicate for so long than just unlimited users and flexibility.

For Noah’s team, speed is of the essence. They want to capture their audience’s opinion on current events to prepare viral case studies and reports. At the same time, they want to reach as many respondents as possible. And Survicate lets Noah’s team launch website surveys fast and exactly where they want them – without the help of software developers or a dedicated research team.

Other than that, Survicate proved to be reliable and trouble-free. As Noah said: 

"Every time I've tried to contact support, it's been very quick. There are no issues nor missing features that I can think of.”

So now that we know why Brainly uses Survicate let’s drill down into the “how.” In this case study, we’ll focus on the two initiatives run by the outreach team: viral content creation and “the value of Brainly” survey. 

Free-to-use NPS survey template

Doubling the blog traffic with feedback-fueled reports

Noah noticed the potential of using customer feedback in his content marketing efforts right as he joined Brainly. With the number of students visiting their site every day, missing their feedback would have been a waste:

“When I joined, we were using [Survicate] to take opportunities, like with current events (...), to get some data from students, who are our core user base. We could use it to write interesting research-based articles.”

Survey data has been a starting point for many in-depth reports and infographics on Brainly’s blog. That already guaranteed Brainly the status of industry thought leader and generated steady media buzz around their content.

But Noah quickly understood that their reports needed to become more high-level to go viral. And the more frequently they appear, the more value they bring. Therefore, the surveys needed to be easier to answer so that they could gather a maximum number of responses in a short amount of time.

Noah’s team has gone for concise, close-ended surveys. The feedback they provide lets them quickly report on the most pressing issues.

One of the best examples of such content is Brainly’s report on students’ anxiety about returning to school during the pandemic . In 2021, as schools started reopening in the USA, the topic was on everybody’s lips. The media was full of politicians’, experts’, and teachers’ opinions. Yet one crucial voice was largely missing: the students’. Noah told us:

“Parents and students were really nervous about [going back to school]. And we got very unique insights – the student angle on that (...). The student voice about current events is often tough to get, especially on a mass level. And we were able to get that feedback very quickly, which was really cool. I think that became a theme for that year. Fear was at a high, and education was a huge question mark(...). Journalists and our readers found that really interesting.”

Brainly presented their findings in a report. They decided to share all the survey questions and answers, which resulted in a quick-to-write yet knowledge-packed report.

The report resulted in a boom in blog traffic that surprised the team. According to Noah, they got 1.8x the blog traffic they had expected. 

“We got plenty of reactions and conversations (...). That was certainly a big success that people remembered us talking about something important that matters to us.”

But how did Brainly manage to collect so many responses so quickly? It was all thanks to website surveys placed on the right pages.

Instead of limiting the respondents to users who were already signed up to Brainly, the outreach team launched website surveys on their most visited pages. The surveys fit naturally into the visitors' journeys and ensured a high response rate .

Feedback-based content helps Brainly create reports on current events relevant to their user base. This viral content fuels their PR and marketing efforts and creates media buzz around the brand. Survicate’s ease of use helps Noah’s team win the race with time.

Free-to-use content preferences survey template

Finding value proposition and gathering product feedback with “the value of Brainly” survey

Surveys are not just about catching the hottest topics for Brainly’s marketing teams. They also use them to gather feedback from their user base consistently.

As Noah told us, the online education landscape changes all the time. With lots of stakeholders and external factors influencing the niche, you have to keep the dialogue with your audience going to stay on top of their needs. It’s important to adjust buyer personas and value propositions constantly.

Recurring surveys help Brainly make sure their communication strategy is correct and give their audience exactly what they want.

Their biggest recurring survey, which Noah calls “the value of Brainly,” is run every six months and targets about 5.000 sample users (parents and students who are paying subscribers). It helps the marketing team see the subjective value of their products across several life stages of the customer base.

“The value of Brainly” survey starts with multiple-choice questions, such as:

• What do you use Brainly for?
• When do you use it?
• What is Brainly best for?

For each question, there’s an “other” option available. The respondents can type their own answers. 

These questions are followed by a series of statements that the respondents have to agree or disagree with (on a 5-point scale ranging from “completely agree” to “completely disagree”).

Here are the examples:

• Thanks to Brainly, I’m better prepared for school.
• Brainly helps me get higher grades.
• Brainly helps me finish my homework faster.

The last question is the open-ended “What is missing from Brainly?”. It lets the respondents freely voice their opinions and ideas.

The survey runs on Brainly’s website, and Noah uses targeting options to make it appear only to the desired audience segment.

The outreach team keeps the questions more or less the same in every survey iteration. It lets them effectively compare results over time.

The survey lets the outreach team:

• Understand how different user segments (e.g., parents and students, users on a higher-tier plan, and non-paying users) compare to one another – to improve communication and get ideas for targeted marketing campaigns
• Spot the most popular themes among all users to adjust the messaging on their website

As Noah told us:

“Doing homework is the most popular answer for ‘What do you use Brainly for?’ question. And that tells us we have to have a lot of keywords around ‘Homework help.’ It's our headline on our site. There are other things that kids can use us for, but people are sticking to the main use case, which is a good sign that our take on advertising is right. Some other answers, like ‘preparing for tests,’ ‘checking answers to questions,’ ‘learning more about subjects I'm interested in’ (...) are also popular, but not as much as the one that we put the most effort into.”

The recurring surveys also helped Noah’s team collect data points that become value propositions, selling points, or social proof for ad campaigns.

One more benefit comes from “the value of Brainly” survey: ideas for product improvements and new features. According to Noah, the respondents often leave their suggestions in the open-ended question text field. Then, the outreach team passes the insights to the product team.

Overall, “the value of Brainly” survey helps the marketing team connect with their audience and ensure their users get what they need.

Building a data-driven company with Survicate

As Noah told us, Survicate is the marketing team’s main data source.

With Brainly’s huge scale, quantitative data is not enough. To get to know their audience, they needed to know the “why” behind their actions and let them speak in their own voice. 

Survicate’s workspace organization and unlimited seats allowed them to overcome the challenge of running large-scale international surveys across different departments. At the same time, the ease of use still let them set up surveys quickly to gather insights about current events.

Brainly first used Survicate to look for product improvements. Still, it turned out the tool can fulfill all their customer feedback needs – measuring NPS, investigating the value of their product, and interviewing the audience for viral reports.

Survicate helped Brainly:

• Stay in touch with their user base
• Refine their value proposition, as well as the website and ad copy
• Double their blog traffic and increase social media hits and mentions
• Collect product improvement ideas

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