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This course is part of the Big Data Specialization

Big Data - Capstone Project

Image of instructor, Ilkay Altintas

Financial aid available

About this Course

Welcome to the Capstone Project for Big Data! In this culminating project, you will build a big data ecosystem using tools and methods form the earlier courses in this specialization. You will analyze a data set simulating big data generated from a large number of users who are playing our imaginary game "Catch the Pink Flamingo". During the five week Capstone Project, you will walk through the typical big data science steps for acquiring, exploring, preparing, analyzing, and reporting. In the first two weeks, we will introduce you to the data set and guide you through some exploratory analysis using tools such as Splunk and Open Office. Then we will move into more challenging big data problems requiring the more advanced tools you have learned including KNIME, Spark's MLLib and Gephi. Finally, during the fifth and final week, we will show you how to bring it all together to create engaging and compelling reports and slide presentations. As a result of our collaboration with Splunk, a software company focus on analyzing machine-generated big data, learners with the top projects will be eligible to present to Splunk and meet Splunk recruiters and engineering leadership.

Could your company benefit from training employees on in-demand skills?

Skills you will gain



Ilkay Altintas


Amarnath Gupta


University of California San Diego

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.

See how employees at top companies are mastering in-demand skills

Syllabus - What you will learn from this course

Simulating big data for an online game.

This week we provide an overview of the Eglence, Inc. Pink Flamingo game, including various aspects of the data which the company has access to about the game and users and what we might be interested in finding out.

Acquiring, Exploring, and Preparing the Data

Next, we begin working with the simulated game data by exploring and preparing the data for ingestion into big data analytics applications.

Data Classification with KNIME

This week we do some data classification using KNIME.

Clustering with Spark

This week we do some clustering with Spark.

Graph Analytics of Simulated Chat Data With Neo4j

This week we apply what we learned from the 'Graph Analytics With Big Data' course to simulated chat data from Catch the Pink Flamingos using Neo4j. We analyze player chat behavior to find ways of improving the game.

Reporting and Presenting Your Work

Final submission.


This is great platform to enhance your skills with periodic learning even from busy schedule and make yourself in pace with new IT.

Excellent course, 100% recommend to people want to learn big data fundamentals.

The project is really helpful to sum up the whole process of the 5 previous courses, but there is a bit problem with the week 4 assignment.

What a challenge, I came into this course as a London Black Cab Taxi Driver, I thought the knowledge was hard but this capstone was a challenge more intense than the Knowledge of London!!!

About the Big Data Specialization

Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.

********* Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.

Big Data

Frequently Asked Questions

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center .

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In their final semester of the UW Data Science program, students are required to take DS 785 , the capstone course. Below are example capstone projects to give you an idea of the types of opportunities available to our students.

Using Mock Draft Data to Create a Player Availability Dashboard for the NFL Draft

capstone project big data

A Practical Data Science Application: Developing Prediction Models for Product Inventory Reduction and Ongoing Monitoring to Create Efficiency

capstone project big data

An In-Depth Review Customer Segmentation, Recommendation Systems, and the Benefits of Combined Use

Time-series forecasting of maple tree sap harvesting.

capstone project big data

Comparative Study on Employee Turnover

capstone project big data

The Development of Feed Type Classification Algorithms for a Commercial Testing Laboratory

capstone project big data

Daily Driving Route Optimization for Small Businesses Using Metaheuristics

capstone project big data

Cost Analysis of a Local Union’s Digital Transformation

Examining and predicting the university of wisconsin’s system library ebook usage.

capstone project big data

Advertisement campaign targeting attributes recommendation engine

capstone project big data

Qlik Application Creation for Deeper Analysis of Department of Defense Budget

capstone project big data

Exploring Rural Road Crash Data with Statistical Models

capstone project big data

780 Regent Street Suite 130 Madison WI, 53715

Advising: 608-800-6762 [email protected]

Current students can email: [email protected]

Technical Support: 1-877-724-7883

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Big Data Capstone Project

Further develop your knowledge of big data by applying the skills you have learned to a real-world data science project.

Big Data Capstone Project

There is one session available:

About this course.

The Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.

Working with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.

This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.

This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.

You’ll deepen your learning of social and ethical concerns in relation to data science, including an analysis of ethical concerns and ethical frameworks in relation to data selection and data management.

By communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written presentation of your project design, plan, methodologies, and outcomes.

At a glance

Candidates interested in pursuing this program are advised to complete Programming for Data Science , Computational Thinking and Big Data , Big Data Fundamentals & Big Data Analytics before this course.

What you'll learn

The Big Data Capstone project will give you the chance to demonstrate practically what you have learned in the Big Data MicroMasters program including:

Dataset overview, data selection and ethics Understand ethical issues and concerns around big data projects;Describe how ethical issues apply to the sample dataset;Describe up to three ethical approaches;Apply ethical analysis to scenarios.

Exam (timed, proctored) The exam will cover content from the first four courses in the Big Data MicroMasters program, including the Ethics section of this capstone course, DataCapX. Itwill include questions on topics such as code structure and testing, variable types, graphs, big data algorithms, regression and ethics.

Project Task 1: Data cleaning and Regression Understand the basic data cleaning and preprocessing steps required in the analysis of a real data set;Create computer code to read data and perform data cleaning and preprocessing;Judge the appropriateness of a fitted regression model to the data;Determine whether simplification of a regression model is appropriate;Apply a fitted regression model to obtain predictions for new observations.

Project Task 2: Classification Build classifiers to predict the output of a desired factor;Analyse learned classifiers;Design a feature selection scheme;Design a scheme for evaluating the performance of classifiers.

About the instructors

Frequently asked questions.

Question: This course is self-paced, but is there a course end date? Answer: Yes. The first course release started on December 1, 2017 and ends on April 1, 2019. The new release of the course starts on March 1, 2019 and ends on December 1, 2020.

Who can take this course?

Ways to take this course, interested in this course for your business or team.

capstone project big data

UCSD Big Data Specialization General Materials and my Capstone Project.


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Big Data Specialization - UCSD

Cousera online course, Big Data specilization , created by University of California, San Diego, taught by Ilkay Altintas (Chief Data Science Officer), Amarnath Gupta (Director, Advanced Query Processing Lab) and Mai Nguyen (Lead for Data Analytics), they all work in San Diego Supercomputer Center(SDSC) .

During this Specialization I was trying to understand big data and how it will impact business, try to gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers.Try to be guided through the basics of using Hadoop with Map-reduce, Spark, Pig and Hive. By following along with provided code, try to get experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare me to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex data-sets. In the final Capstone Project, developed in partnership with data software company Splunk, i'll apply the skills i learned to do basic analyses of big data.

This specilization contains 6 following courses:

Master of Science in Analytics

As a culminating experience, Master of Science in Analytics (MScA) students put into practice the knowledge and skills they have learned during their coursework by completing a capstone project. The project is a degree requirement and is completed during the last two quarters of their program. It provides students and project sponsors the opportunity to develop and implement a data science solution to address problems the organization is trying to solve, enhance their analytics capabilities, and explore potential employment partnerships. There is no cost associated with sponsoring a project.

The project spans two 10-week quarters. Students may begin their Capstone experience in any quarter, with most students starting in the Spring quarter. The expectation is that students will work in teams of four.

MScA Capstone Project Learning Objectives

Capstone Sponsor Incentives

Professionals in a conference room go over paperwork.

Interested in Sponsoring a Capstone Project?

Get in touch with us to submit your idea for a collaboration or ask us questions about how the partnership process works.

Selected Capstone Projects

Copd readmission and cost reduction assessment.

UChicago Analytics students built data models and evaluated them across different frameworks. They determined that the resulting model is capable of rank-ordering readmission risk and allowing for flexibility in applying interventions to prevent readmission.

An NFL Ticket Pricing Study: Optimizing Revenue Using Variable And Dynamic Pricing Methods

UChicago Analytics students found a way for an NFL team to implement ticket pricing that responds to changing factors and gives the team the chance to fill more seats.

Using Image Recognition To Identify Yoga Poses

Master of Science in Analytics students built an app that uses a one-step neural network to examine images of yoga poses and recognize the poses in order to provide feedback to the app's yoga-practicing user.

Using Image Recognition to Measure the Speed of a Pitch

One capstone team developed an app that applied image recognition algorithms to measure the speed of a pitched baseball. Their app captured video, isolated the pitched ball, calculated the velocity of the pitch, and displayed this measurement so that users would be able to measure the speed of a pitch with their smartphones.

Real-Time Credit Card Fraud Detection

Credit card fraud puts consumers' identities at risk while credit card providers are forced to cover fraudulent charges. A team of analytics students carefully studied this problem: they created synthetic data that represented a large population of credit card users and were able to build a model that catches credit card fraud in real time.

Interested in Becoming an Industry Research Partner?

Get in touch with us to submit your idea for a collaboration or ask us questions about how the partnership process works.

Capstone Projects

Education is one of the pillars of the data science institute..

Through educational activities, we strive to create a community in Data Science at Columbia. The capstone project is one of the most lauded elements of our MS in Data Science program. As a final step during their study at Columbia, our MS students work on a project sponsored by a DSI industry affiliate or a faculty member over the course of a semester.

Faculty-Sponsored Capstone Projects

A DSI faculty member proposes a research project and advises a team of students working on this project. This is a great way to run a research project with enthusiastic students, eager to try out their newly acquired data science skills in a research setting. This is especially a good opportunity for developing and accelerating interdisciplinary collaboration.

2022-2023 Academic Year FALL 2022: July 15, 2022 SPRING 2023: TBA

Project Archive

Professional and Lifelong Learning

In-person, blended, and online courses, data science: capstone.

capstone project big data

Associated Schools

capstone project big data

Harvard T.H. Chan School of Public Health

What you'll learn.

capstone project big data

Course description

To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors. When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.

capstone project big data

Rafael Irizarry

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Big Data Capstone Project

University of Adelaide via edX Help

The Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.

Working with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.

This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.

This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.

You’ll deepen your learning of social and ethical concerns in relation to data science, including an analysis of ethical concerns and ethical frameworks in relation to data selection and data management.

By communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written presentation of your project design, plan, methodologies, and outcomes.

Dataset overview, data selection and ethics Understand ethical issues and concerns around big data projects;Describe how ethical issues apply to the sample dataset;Describe up to three ethical approaches;Apply ethical analysis to scenarios.

Exam (timed, proctored) The exam will cover content from the first four courses in the Big Data MicroMasters program, including the Ethics section of this capstone course, DataCapX. Itwill include questions on topics such as code structure and testing, variable types, graphs, big data algorithms, regression and ethics.

Project Task 1: Data cleaning and Regression Understand the basic data cleaning and preprocessing steps required in the analysis of a real data set;Create computer code to read data and perform data cleaning and preprocessing;Judge the appropriateness of a fitted regression model to the data;Determine whether simplification of a regression model is appropriate;Apply a fitted regression model to obtain predictions for new observations.

Project Task 2: Classification Build classifiers to predict the output of a desired factor;Analyse learned classifiers;Design a feature selection scheme;Design a scheme for evaluating the performance of classifiers.

Dr. Frank Neumann, Dr. Lewis Mitchell and Dr. ​Claudia Szabo

Related Courses

Big data analytics, big data fundamentals, big data - capstone project, data analysis with python, analyzing data with python, related articles.

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upGrad blog

13 Ultimate Big Data Project Ideas & Topics for Beginners [2023]

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Table of Contents

Big Data Project Ideas

Big Data is an exciting subject. It helps you find patterns and results you wouldn’t have noticed otherwise. This skill highly in demand , and you can quickly advance your career by learning it. So, if you are a big data beginner, the best thing you can do is work on some big data project ideas. But it can be difficult for a beginner to find suitable big data topics as they aren’t very familiar with the subject. 

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting big data project ideas which beginners can work on to put their big data knowledge to test. In this article, you will find top big data project ideas for beginners to get hands-on experience on big data

Check out our  free courses  to get an edge over the competition.

However, knowing the theory of big data alone won’t help you much. You’ll need to practice what you’ve learned. But how would you do that?

You can practice your big data skills on big data projects. Projects are a great way to test your skills. They are also great for your CV. Especially big data research projects and data processing projects are something that will help you understand the whole of the subject most efficiently. 

Read : Big data career path

capstone project big data

You won’t belive how this Program Changed the Career of Students

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What are the areas where big data analytics is used.

Before jumping into the list of  big data topics t hat you can try out as a beginner, you need to understand the areas of application of the subject. This will help you invent your own topics for data processing projects once you complete a few from the list. Hence, let’s see what are the areas where big data analytics is used the most. This will help you navigate how to identify issues in certain industries and how they can be resolved with the help of big data as big data research projects.

The banking industry often deals with cases of card fraud, security fraud, ticks and such other issues that greatly hamper their functioning as well as market reputation. Hence to tackle that, the securities exchange commission aka SEC takes the help of big data and monitors the financial market activity. 

This has further helped them manage a safer environment for highly valuable customers like retail traders, hedge funds, big banks and other eminent individuals in the financial market. Big data has helped this industry in the cases like anti-money laundering, fraud mitigation, demand enterprise risk management and other cases of risk analytics. 

It is needless to say that the media and entertainment industry heavily depends on the verdict of the consumers and this is why they are always required to put up their best game. For that, they require to understand the current trends and demands of the public, which is also something that changes rapidly these days.

To get an in-depth understanding of consumer behaviour and their needs, the media and entertainment industry collects, analyses and utilises customer insights. They leverage mobile and social media content to understand the patterns at a real-time speed. 

The industry leverages Big data to run detailed sentiment analysis to pitch the perfect content to the users. Some of the biggest names in the entertainment industry such as Spotify and Amazon Prime are known for using big data to provide accurate content recommendations to their users, which helps them improve their customer satisfaction and, therefore, increases customer retention. 

Even though the healthcare industry generates huge volumes of data on a daily basis which can be ustilised in many ways to improve the healthcare industry, it fails to utilise it completely due to issues of usability of it. Yet there is a significant number of areas where the healthcare industry is continuously utilising Big Data.

The main area where the healthcare industry is actively leveraging big data is to improve hospital administration so that patients can revoke best-in-class clinical support. Apart from that, Big Data is also used in fighting lethal diseases like cancer. Big Data has also helped the industry to save itself from potential frauds and committing usual man-made errors like providing the wrong dosage, medicine etc. 

Similar to the society that we live in, the education system is also evolving. Especially after the pandemic hit hard, the change became even more rapid. With the introduction of remote learning, the education system transformed drastically, and so did its problems.

On that note, Big Data significantly came in handy, as it helped educational institutions to get the insights that can be used to take the right decisions suitable for the circumstances. Big Data helped educators to understand the importance of creating a unique and customised curriculum to fight issues like students not being able to retain attention. 

It not only helped improve the educational system but to identify the student’s strengths and channeled them right. 

Likewise the field of government and public services itself, the applications of Big Data by them are also extensive and diverse. Government leverages big data mostly in areas like financial market analysis, fraud detection, energy resource exploration, environment protection, public-health-related research and so forth. 

The Food and Drug Administration (FDA) actively uses Big Data to study food-related illnesses and disease patterns. 

In spite of having tons of data available online in form of reviews, customer loyalty cards, RFID etc. the retail and wholesale industry is still lacking in making complete use of it. These insights hold great potential to change the game of customer experience and customer loyalty. 

Especially after the emergence of e-commerce, big data is used by companies to create custom recommendations based on their previous purchasing behaviour or even from their search history. 

In the case of brick-and-mortar stores as well, big data is used for monitoring store-level demand in real-time so that it can be ensured that the best-selling items remain in stock. Along with that, in the case of this industry, data is also helpful in improving the entire value chain to increase profits.  

The demand for resources of every kind and manufactured product is only increasing with time which is making it difficult for industries to cope. However, there are large volumes of data from these industries that are untapped and hold the potential to make both industries more efficient, profitable and manageable. 

By integrating large volumes of geospatial and geographical data available online, better predictive analysis can be done to find the best areas for natural resource explorations. Similarly, in the case of the manufacturing industry, Big Data can help solve several issues regarding the supply chain and provide companies with a competitive edge. 

The insurance industry is anticipated to be the highest profit-making industry but its vast and diverse customer base makes it difficult for it to incorporate state-of-the-art requirements like personalized services, personalised prices and targeted services. To tackle these prime challenges Big Data plays a huge part.

Big data helps this industry to gain customer insights that further help in curating simple and transparent products that match the recruitment of the customers. Along with that, big data also helps the industry analyse and predict customer behaviours and results in the best decision-making for insurance companies. Apart from predictive analytics, big data is also utilised in fraud detection. 

What problems you might face in doing Big Data Projects

Big data is present in numerous industries. So you’ll find a wide variety of big data project topics to work on too.

Apart from the wide variety of project ideas, there are a bunch of challenges a big data analyst faces while working on such projects.

They are the following:

Limited Monitoring Solutions

You can face problems while monitoring real-time environments because there aren’t many solutions available for this purpose.

That’s why you should be familiar with the technologies you’ll need to use in big data analysis before you begin working on a project.

Timing Issues

A common problem among data analysis is of output latency during data virtualization. Most of these tools require high-level performance, which leads to these latency problems.

Due to the latency in output generation, timing issues arise with the virtualization of data.

The requirement of High-level Scripting

When working on big data analytics projects, you might encounter tools or problems which require higher-level scripting than you’re familiar with.

In that case, you should try to learn more about the problem and ask others about the same.

Data Privacy and Security

While working on the data available to you, you have to ensure that all the data remains secure and private.

Leakage of data can wreak havoc to your project as well as your work. Sometimes users leak data too, so you have to keep that in mind.

Knowledge Read: Big data jobs & Career planning

Unavailability of Tools

You can’t do end-to-end testing with just one tool. You should figure out which tools you will need to use to complete a specific project.

When you don’t have the right tool at a specific device, it can waste a lot of time and cause a lot of frustration.

That is why you should have the required tools before you start the project.

Check out big data certifications  at upGrad

Too Big Datasets

You can come across a dataset which is too big for you to handle. Or, you might need to verify more data to complete the project as well.

Make sure that you update your data regularly to solve this problem. It’s also possible that your data has duplicates, so you should remove them, as well.

While working on big data projects, keep in mind the following points to solve these challenges:

We recommend the following technologies for beginner-level big data projects:

Each of these technologies will help you with a different sector. For example, you will need to use cloud solutions for data storage and access.

On the other hand, you will need to use R for using data science tools . These are all the problems you need to face and fix when you work on big data project ideas. 

If you are not familiar with any of the technologies we mentioned above, you should learn about the same before working on a project. The more big data project ideas you try, the more experience you gain.

Otherwise, you’d be prone to making a lot of mistakes which you could’ve easily avoided.

So, here are a few  Big Data Project ideas  which beginners can work on:

Read : Career in big data and its scope.

Big Data Project Ideas: Beginners Level

This list of big data project ideas for students is suited for beginners, and those just starting out with big data. These big data project ideas will get you going with all the practicalities you need to succeed in your career as a big data developer.

Further, if you’re looking for big data project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some big data project ideas that will strengthen your base and allow you to climb up the ladder.

We know how challenging it is to find the right project ideas as a beginner. You don’t know what you should be working on, and you don’t see how it will benefit you.

That’s why we have prepared the following list of big data projects so you can start working on them: Let’s start with big data project ideas.

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1. classify 1994 census income data.

One of the best ideas to start experimenting you hands-on big data projects for students is working on this project. You will have to build a model to predict if the income of an individual in the US is more or less than $50,000 based on the data available.

A person’s income depends on a lot of factors, and you’ll have to take into account every one of them.

You can find the data for this project here .

2. Analyze Crime Rates in Chicago

Law enforcement agencies take the help of big data to find patterns in the crimes taking place. Doing this helps the agencies in predicting future events and helps them in mitigating the crime rates.

You will have to find patterns, create models, and then validate your model.

You can get the data for this project here .

3. Text Mining Project

This is one of the excellent deep learning project ideas for beginners. Text mining is in high demand, and it will help you a lot in showcasing your strengths as a data scientist. In this project, you will have to perform text analysis and visualization of the provided documents.  

You will have to use Natural Language Process Techniques for this task.

You can get the data here .

In-Demand Software Development Skills

Big data project ideas: advanced level, 4. big data for cybersecurity.

big data projects

This project will investigate the long-term and time-invariant dependence relationships in large volumes of data. The main aim of this Big Data project is to combat real-world cybersecurity problems by exploiting vulnerability disclosure trends with complex multivariate time series data. This cybersecurity project seeks to establish an innovative and robust statistical framework to help you gain an in-depth understanding of the disclosure dynamics and their intriguing dependence structures.

5. Health status prediction

This is one of the interesting big data project ideas. This Big Data project is designed to predict the health status based on massive datasets. It will involve the creation of a machine learning model that can accurately classify users according to their health attributes to qualify them as having or not having heart diseases. Decision trees are the best machine learning method for classification, and hence, it is the ideal prediction tool for this project. The feature selection approach will help enhance the classification accuracy of the ML model.

6. Anomaly detection in cloud servers

In this project, an anomaly detection approach will be implemented for streaming large datasets. The proposed project will detect anomalies in cloud servers by leveraging two core algorithms – state summarization and novel nested-arc hidden semi-Markov model (NAHSMM). While state summarization will extract usage behaviour reflective states from raw sequences, NAHSMM will create an anomaly detection algorithm with a forensic module to obtain the normal behaviour threshold in the training phase.

7. Recruitment for Big Data job profiles

Recruitment is a challenging job responsibility of the HR department of any company. Here, we’ll create a Big Data project that can analyze vast amounts of data gathered from real-world job posts published online. The project involves three steps:

The goal of this project is to help the HR department find better recruitments for Big Data job roles.

8. Malicious user detection in Big Data collection

This is one of the trending deep learning project ideas. When talking about Big Data collections, the trustworthiness (reliability) of users is of supreme importance. In this project, we will calculate the reliability factor of users in a given Big Data collection. To achieve this, the project will divide the trustworthiness into familiarity and similarity trustworthiness. Furthermore, it will divide all the participants into small groups according to the similarity trustworthiness factor and then calculate the trustworthiness of each group separately to reduce the computational complexity. This grouping strategy allows the project to represent the trust level of a particular group as a whole. 

9. Tourist behaviour analysis

This is one of the excellent big data project ideas. This Big Data project is designed to analyze the tourist behaviour to identify tourists’ interests and most visited locations and accordingly, predict future tourism demands. The project involves four steps:  

big data projects

10. Credit Scoring

big data project ideas topics

This project seeks to explore the value of Big Data for credit scoring. The primary idea behind this project is to investigate the performance of both statistical and economic models. To do so, it will use a unique combination of datasets that contains call-detail records along with the credit and debit account information of customers for creating appropriate scorecards for credit card applicants. This will help to predict the creditworthiness of credit card applicants.

11. Electricity price forecasting

This is one of the interesting big data project ideas. This project is explicitly designed to forecast electricity prices by leveraging Big Data sets. The model exploits the SVM classifier to predict the electricity price. However, during the training phase in SVM classification, the model will include even the irrelevant and redundant features which reduce its forecasting accuracy. To address this problem, we will use two methods – Grey Correlation Analysis (GCA) and Principle Component Analysis. These methods help select important features while eliminating all the unnecessary elements, thereby improving the classification accuracy of the model.

12. BusBeat

BusBeat is an early event detection system that utilizes GPS trajectories of periodic-cars travelling routinely in an urban area. This project proposes data interpolation and the network-based event detection techniques to implement early event detection with GPS trajectory data successfully. The data interpolation technique helps to recover missing values in the GPS data using the primary feature of the periodic-cars, and the network analysis estimates an event venue location.

13. Yandex.Traffic

Yandex.Traffic was born when Yandex decided to use its advanced data analysis skills to develop an app that can analyze information collected from multiple sources and display a real-time map of traffic conditions in a city.

After collecting large volumes of data from disparate sources, Yandex.Traffic analyses the data to map accurate results on a particular city’s map via Yandex.Maps, Yandex’s web-based mapping service. Not just that, Yandex.Traffic can also calculate the average level of congestion on a scale of 0 to 10 for large cities with serious traffic jam issues. Yandex.Traffic sources information directly from those who create traffic to paint an accurate picture of traffic congestion in a city, thereby allowing drivers to help one another.

capstone project big data

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In this article, we have covered top big data project ideas . We started with some beginner projects which you can solve with ease. Once you finish with these simple projects, I suggest you go back, learn a few more concepts and then try the intermediate projects. When you feel confident, you can then tackle the advanced projects. If you wish to improve your big data skills, you need to get your hands on these big data project ideas.

Working on big data projects will help you find your strong and weak points. Completing these projects will give you real-life experience of working as a data scientist.

If you are interested to know more about Big Data, check out our  Advanced Certificate Programme in Big Data from IIIT Bangalore .

Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career.

How can one create and validate models for their projects?

To create a model, one needs to find a suitable dataset. Initially, data cleaning has to be done. This includes filling missing values, removing outliers, etc. Then, one needs to divide the dataset into two parts: the Training and the Testing dataset. The ratio of training to testing is preferably 80:20. Algorithms like Decision tree, Support Vector Machine (SVM), Linear and Logistic Regression, K- Nearest Neighbours, etc., can be applied. After training, testing is done using the testing dataset. The model's prediction is compared to the actual values, and finally, the accuracy is computed.

What is the Decision tree algorithm?

A Decision tree is a classification algorithm. It is represented in the form of a tree. The partitioning attribute is selected using the information gain, gain ratio, and Gini index. At every node, there are two possibilities, i.e., it could belong to either of the classes. The attribute with the highest value of information gain, Gini index or gain ratio is chosen as the partitioning attribute. This process continues until we cannot split a node anymore. Sometimes, due to overfitting of the data, extensive branching might occur. In such cases, pre-pruning and post-pruning techniques are used to construct the tree optimally.

What is Scripting?

Scripting is a process of automating the tasks that were previously done manually. Scripting languages are interpreter languages, i.e., they are executed line by line at run time. Scripts are run in an integrated environment called Shells. These include Unix, C shell, Korn shell, etc. Some examples of scripting languages are Bash, Node.js, Python, Perl, Ruby, and Javascript. Scripting is used in system administration, client, and server-side applications and for creating various extensions and plugins for the software. They are fast in terms of execution and are very easy to learn. They make web pages more interactive. Scripting is open-source and can be ported easily and shifted to various operating systems.

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20 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. Last Updated: 28 Feb 2023

Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data 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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Last Updated : 2023-02-14 16:50:40

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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. We bring the top big data projects for 2021 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, 20+ big data project ideas to help boost your resume , top big data projects on github with source code, big data projects for engineering students, big data projects 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 projects for final year students, best practices for a good big data project, master big data skills with 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 a large dataset 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, 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.

ProjectPro Free Projects on Big Data and Data Science

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

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.

Collect Data

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. Cleaning up your data 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. 

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.

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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 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. 

1. 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.

Big Data Project to Build a Data Pipeline 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:




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  

2. Snowflake Real-Time Data Warehouse Project for Beginners

Snowflake provides a cloud-based analytics and data storage service called "data warehouse-as-a-service." Work on this project to learn how to use the Snowflake architecture and create a data warehouse in the cloud to bring value to your business.

Snowflake Real-Time Big Data Project for Beginners

Data Description: For this project, you will create a sample database containing a table named ‘customer_detail.’ This table will include the details of the customers such as :  First name, Last name, Address, City, and State.

Language Used: SQL

Packages/Libraries: Services: Amazon S3, Snowflake, SnowSQL, QuickSight

Source Code: Snowflake Real-Time Data Warehouse Project for Beginners

3. Data Warehouse Design for an E-commerce Site

A data warehouse is an extensive collection of data for a business that helps the business make informed decisions based on data analysis. For an e-commerce site, the data warehouse would be a central repository of consolidated data, from searches to purchases by site visitors. By designing such a data warehouse, the site can manage supply based on demand (inventory management), take care of their logistics, modify pricing for optimum profits and manage advertisements based on searches and items purchased. Recommendations can also be generated based on patterns in a given area or based on age groups, sex, and other similar interests. While designing the data warehouse, it is essential to keep some key aspects, such as how the data from multiple sources can be stored, retrieved, structured, modified, and analyzed. If you are a student looking for Apache Big Data projects, this is a perfect place to start since this project can be developed using Apache Hive .

Access Solution to Data Warehouse Design for an E-com Site

4. 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 

5. Generating Movie/Song Recommendations

Streaming platforms can most easily appeal to their audience based on recommendations, and continuously generating recommendations suitable for a particular individual can maximize engagement on the platform. Streaming platforms today recommend content based on multiple approaches – based on previous watches, demographics, the newest and trending movies, searches, and ratings from other individuals who have watched a movie or listened to a particular song. The datasets must be gathered based on these factors to find patterns. Projects requiring the generation of a recommendation system are excellent intermediate Big Data projects. The use of Spark SQL to store the data and Apache Hive to process the data, along with a few applications of machine learning, can build the required recommendation system .

Learn more about Big Data Tools and Technologies with Innovative and Exciting Big Data Projects Examples.

 6. Analysis of Airline Datasets

Large amounts of data from any site need to be processed and analyzed to become valuable to the business. This is another excellent choice if you are searching for Big Data analytics projects for engineering students. In the case of airlines, popular routes will have to be monitored so that more airlines can be available on those routes to maximize efficiency. Does the number of people flying across a particular path change over a day/week/month/year, and what factors can lead to these fluctuations? In addition, it is also necessary to closely observe delays – are older flights more prone to delays? When is the best time of the day/week/year/month to minimize delays? Focus on this data helps the airlines and the passengers using the airlines as well. You can use Apache Hive or Apache Impala to partition and cluster the data. Apache pig can be used for data preprocessing.

A simple big data project idea for students on how to perform analysis of airline datasets is here  

7. 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.

8. 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 .

9. 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

10. 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

11. 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

12. 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.

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13. 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.

Recommended Reading: 

15 Tableau Projects for Beginners to Practice with Source Code

10+ Real-Time Azure Project Ideas for Beginners to Practice

14. Detection of Fake News on Social Media

Fake News Detection 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

15. 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.

16. 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.

17. Credit Card Fraud Detection

Credit Crad Fraud Detection Project Idea

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.

18. 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. 

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19. 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.

20. 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

21. 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.

Access Big Data Projects Example Code to Real-Time Tracking of Vehicles

22. 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.

23. 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

24. 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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1. 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

2. 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

3. 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

Join the Big Data community of developers by gaining hands-on experience in industry-level Spark Projects.

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Most executives prioritize big data projects that focus on utilizing data for business growth and profitability. But up to 85% of big data projects fail, mainly due to management's inability to properly assess project risks initially.

Here are some good practices for successful Big Data projects.

Set Definite Goals

Before building a Big Data project, it is essential to understand why it is being done. It is necessary to comprehend that the goal of a big data project is to identify solutions that boost the company's efficiency and competitiveness.

A Big Data project has every possibility of succeeding when the objectives are clearly stated, and the business problems that must be handled are accurately identified.

Select The Right Big Data Tools and Techniques

Traditional data management uses a client/server architecture to centralize data processing and storage on a single server. Big Data projects now involve the distribution of storage among multiple computers rather than its centralization in a single server to be successful.

Hadoop serves as a good example of this technology strategy. The majority of businesses employ this software implementation.

Ensure Sufficient Data Availability 

Ensuring the data is available to individuals who want it is crucial when building a Big Data project. It is easier to persuade them of the significance of the data analyzed if the business's stakeholders are appropriately targeted and given access to the data.

Organizations constantly run their operations so that every department has its data. Every data collection process is kept in a silo, isolated from other groups inside the organization. The Big Data project won't be very productive until all organizational data is constantly accessible to people who require it. The connections and trends that appear can then be fully used.

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Explore a few more big data project ideas with source code on the ProjectPro repository. Get started and build your career in Big Data from scratch if you are a beginner, or grow it from where you are now. Remember, it’s never too late to learn a new skill, and even more so in a field with so many uses at present and, even then, still has so much more to offer. We hope that some of the ideas inspire you to develop your ideas. The Big Data train is chugging at a breakneck pace, and it’s time for you to hop on if you aren’t on it already!

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization

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.

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.

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. 

Are big data projects essential to land a job?

According to research, 96% of businesses intend to hire new employees in 2022 with the relevant skills to fill positions relevant to big data analytics. Since there is a significant demand for big data skills, working on big data projects will help you advance your career quickly.

What makes big data analysis difficult to optimize?

Optimizing big data analysis is challenging for several reasons. These include the sheer complexity of the technologies, restricted access to data centers, the urge to gain value as fast as possible, and the need to communicate data quickly enough. However, there are ways to improve big data optimization-

Reduce Processing Latency- Conventional database models have latency in processing because data retrieval takes a long time. Turning away from slow hard discs and relational databases further toward in-memory computing technologies allows organizations to save processing time.

Analyze Data Before Taking Actions- It's advisable to examine data before acting on it by combining batch and real-time processing. While historical data allows businesses to assess trends, the current data — both in batch and streaming formats — will enable organizations to notice changes in those trends. Companies gain a deeper and more accurate view when accessing an updated data set.

Transform Information into Decisions- Various data prediction methods are continually emerging due to machine learning. Big data software and service platforms make it easier to manage the vast amounts of big data by organizations. Large volumes of data are transformed into trends using machine learning. Businesses need to take full advantage of this technology.

How many big data projects fail?

According to a Gartner report, around 85 percent of Big Data projects fail. There can be various reasons causing these failures, such as

Lack of Skills- Most big data projects fail due to low-skilled professionals in an organization. Hiring the right combination of qualified and skilled professionals is essential to building successful big data project solutions.

Incorrect Data- Training data's limited availability and quality is a critical development concern. Data management teams must have internal protocols, such as policies, checklists, and reviews, to ensure proper data utilization.

Poor Team Communication- Often, the projects fail due to a lack of proper interaction between teams involved in the project deployment. Ensuring strong communication between teams adds value to the success of a project.

Undefined Project Goals- Another critical cause of failure is starting a project with unrealistic or unclear goals. It's always good to ask relevant questions and figure out the underlying problem.

What are the types of Big Data?

The three primary types of big data are:

Structured Data: Structured data refers to the data that can be analyzed, retrieved, and stored in a fixed format. Machines and humans are both sources of structured data. Machine-generated data encompasses all data obtained from sensors, websites, and financial systems. Human-generated structured data primarily consists of all information that a person enters into a computer, like his name or other private information.

Semi-structured Data: It is a combination of structured and unstructured data. It is usually the kind of data that does not belong to a specific database but has tags to identify different elements. Emails, CSV/XML/JSON format files, etc., are examples of semi-structured data.

Unstructured Data: Unstructured data refers to data that has an incomprehensible format or pattern. Unstructured data can either be machine-generated or human-generated based on its source. An example of unstructured data is the results of a google search with text, videos, photos, webpage links, etc.

How Big Data Works?

As discussed at the beginning of this blog, Big Data involves handling a company's digital information and implementing tools over it to identify hidden patterns in the data. To achieve that, a business firm needs to have the infrastructure to support different types of data formats and process them.  You can build the proper infrastructure if you keep the following three main points that describe how big data works.

Integration: Sourcing data from different sources is fundamental in big data, and in most cases, multiple sources must be integrated to build pipelines that can retrieve data.

Management: The multiple sources discussed above must be appropriately managed. Since relying on physical systems becomes difficult, more and more organizations rely on cloud computing services to handle their big data.

Analysis: This is the most crucial part of implementing a big data project. Implementing data analytics algorithms over datasets assists in revealing hidden patterns that businesses can utilize for making better decisions.

What are the 7 V's of big data?

Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value are the seven V's that best define Big Data.

Volume- This is the most significant aspect of big data. Data is growing exponentially with time, and therefore, it is measured in Zettabytes, Exabytes, and Yottabytes instead of Gigabytes.

Velocity- The term "velocity" indicates the pace at which data can be analyzed and retrieved. Millions of social media articles, YouTube audio and videos, and photos posted every second should be available soon.

Variety- The term "variety" refers to various data sources available. It is one of the most challenging aspects of Big Data as the data available these days is primarily unstructured. Organizing such data is quite a difficult task in itself.

Variability- Variability is not the same as a variety, and "variability" refers to constantly evolving data. The main focus of variability is analyzing and comprehending the precise meanings of primary data.

Veracity- Veracity is primarily about ensuring that the data is reliable, which entails the implementation of policies to prevent unwanted data from gathering in your systems.

Visualization- "Visualization" refers to how you can represent your data to management for decision-making. Data must be easily readable, understandable, and available regardless of its format. Visual charts, graphs, etc., are a great choice to represent your data than excel sheets and numerical reports.

Value- The primary purpose of Big data is to create value. You must ensure your business gains value from the data after dealing with volume, velocity, variety, variability, veracity, and visualization- which consumes a lot of time and resources.

What are the uses of big data?

Big Data has a wide range of applications across industries -

Healthcare -  Big data aids the healthcare sector in multiple ways, such as lowering treatment expenses, predicting epidemic outbreaks, avoiding preventable diseases by early discoveries, etc.

Media and Entertainment - The rise in social media and other technologies have resulted in large amounts of data generated in the media industry. Big data benefits this sector in terms of media recommendations, on-demand media streaming, customer data insights, targeting the right audience, etc.

Education - By adding to e-learning systems, big data solutions have helped overcome one of the most significant flaws in the educational system: the one-size-fits-all approach. Big data applications help in various ways, including tailored and flexible learning programs, re-framing study materials, scoring systems, career prediction, etc.

Banking - Data grows exponentially in the banking sector. The proper investigation and analysis of such data can aid in the detection of any illegal acts, such as credit/debit card frauds, enterprise credit risks, money laundering, customer data misuse, etc.

Transportation - Big data has often been applied to make transportation more efficient and reliable. It helps plan routes based on customer demands, predict real-time traffic patterns, improve road safety by predicting accident-prone regions, etc.

What is an example of Big Data?

A company that sells smart wearable devices to millions of people needs to prepare real-time data feeds that display data from sensors on the devices. The technology will help them understand their performance and customers' behavior. 

What are the main components of a big data architecture?

The primary components of big data architecture are:

Sources of Data

Storage of Data

Batch processing

Ingestion of real-time messages

Stream Processing

Datastore for performing Analytics

Analysis of Data and Reports Preparation

What are the different features of big data analytics?

Here are different features of big data analytics:


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