40 Free Datasets for Building an Irresistible Portfolio (2023)
In this post, we’ll show you where to find datasets for various projects in the following areas:
- Data science
- Data visualization
- Data cleaning
- Machine learning
- Probability and statistics
Whether you want to strengthen your portfolio by showing that you can visualize data well, or you have a spare few hours and want to practice your machine learning skills, this article has everything you need.
Looking for Datasets to Build Projects? We've Got You Covered
If you're trying to find free datasets so that you can learn by building projects, we have plenty of options for you.
Here at Dataquest, a majority of our courses contain projects for you to complete using real, high-quality datasets. The projects are designed to help you showcase your skills and give you something to add to your portfolio.
If you’re interested, check out some of the projects we have available below. Signing up is completely free and the datasets are downloadable.
- Identify Customers Likely to Churn: Use an Excel dataset to conduct an exploratory data analysis (EDA) for a telecommunications provider to identify customers that are at risk of churn.
- Analyze Retail Sales: Work with retail sales data to explore trends and relationships. Build basic models to confirm the statistical significance of your insights.
Our Data Analysis with Excel path contains 2 other projects. Sign up for free here.
Data Cleaning (Python)
- Analyze Star Wars Surveys: Use survey data to better understand Star Wars fans.
- Explore eBay Car Sales Data: Use a custom-scrapped dataset of eBay’s used car listings to practice data cleaning and data exploration.
- Find Heavy Traffic Indicators on I-94: Use a dataset about traffic on an interstate highway and perform exploratory data visualization.
- Explore Hacker News Posts: Use a dataset from Hacker News submissions to practice using loops, cleaning strings, and dates in Python.
Our Data Cleaning with Python path contains 4 other projects. Sign up for free here.
Data Analysis and Visualization (Python)
- Create Data Visualization on Euro Exchange Rates: Use a dataset from the European Central Bank to create visualizations using Matplotlib.
- Determine Which Mobile Apps Attract More Users: Use two separate datasets to analyze Android and iOS apps to determine the types of apps that are likely to attract users.
Our Data Analysis and Visualization with Python path contains 3 other projects. Sign up for free here.
Data Analysis (R)
- Investigate COVID-19 Trends: Use a Kaggle dataset and RStudio to analyze important COVID-19 trends.
- Analyze Sales Data of a Bookstore: Use a dataset to apply control flow loops and functions to create a reusable data workflow.
Our R Basics for Data Analysis path contains 2 other projects. Sign up for free here.
Machine Learning (Python)
- Predict House Sale Prices: Use housing data from a city in the United States to build and improve linear regression models.
- Predict the Stock Market: Use historical data from the S&P 500 Index to make predictions about future prices.
- Predict Bike Rentals: Use a dataset of bike rentals and apply decision trees and random forests to predict the number of future bike rentals.
Our Machine Learning Intro with Python path contains 15 other projects. Sign up for free here .
Probability and Statistics (Python)
- Investigate Fandango Movie Ratings: Use a custom data set made by our team and perform practical analysis to determine if there’s a bias in Fandango’s movie rating system.
- Find the Best Markets to Advertise In: Use survey data from freeCodeCamp to determine the most effective advertising markets for an e-learning platform.
- Build a Spam Filter: Use an SMS spam collection dataset to build a spam filter using conditional probability and Naive Bayes.
Our Probability and Statistics with Python path contains 9 other projects. Sign up for free here .
Public Datasets for Data Visualization Projects
A typical data visualization project might be something along the lines of “I want to make an infographic about how income varies across the different states in the US.” There are a few considerations to keep in mind when looking for a good dataset for a data visualization project:
- It shouldn’t be messy, because you don’t want to spend a lot of time cleaning data.
- It should be nuanced and interesting enough to make charts about.
- Ideally, each column should be well-explained, so the visualization is accurate.
- The data set shouldn’t have too many rows or columns, so it’s easy to work with.
Good places to find good datasets for data visualization projects are news sites that release their data publicly. They typically clean the data for you and already have charts that you can replicate or improve.
1. FiveThirtyEight
FiveThirtyEight is an incredibly popular interactive news and sports site started by Nate Silver . They write interesting data-driven articles, like “Don’t blame a skills gap for lack of hiring in manufacturing” and “2016 NFL Predictions .”
FiveThirtyEight makes the datasets used in its articles available online on GitHub .
View the FiveThirtyEight Datasets
Here are some examples:
- Airline Safety — contains information on accidents from each airline.
- US Weather History — historical weather data for the US.
Study Drugs — data on who’s taking Adderall in the US.
2. BuzzFeed
BuzzFeed started as a purveyor of low-quality articles, but has since evolved and now writes some investigative pieces, like “The court that rules the world” and “The short life of Deonte Hoard .”
BuzzFeed makes the data sets used in its articles available on Github.
View the BuzzFeed Datasets
- Federal Surveillance Planes — contains data on planes used for domestic surveillance.
- Zika Virus — data about the geography of the Zika virus outbreak.
- Firearm Background Checks — data on background checks of people attempting to buy firearms.
NASA is a publicly-funded government organization, and thus all of its data is public. It maintains websites where anyone can download its datasets related to earth science and datasets related to space . You can even sort by format on the earth science site to find all of the available CSV datasets, for example.
Public Datasets for Data Processing Projects
Sometimes you just want to work with a large dataset. The end result doesn’t matter as much as the process of reading in and analyzing the data. You might use tools like Spark or Hadoop to distribute the processing across multiple nodes. Things to keep in mind when looking for a good data processing dataset:
- The cleaner the data, the better — cleaning a large dataset can be very time consuming.
- The dataset should be interesting.
- There should be an interesting question that can be answered with the data.
Good places to find large public data sets are cloud-hosting providers like Amazon and Google . They have an incentive to host the data sets because they make you analyze them using their infrastructure (and pay them to use it).
4. AWS Public Data sets
Amazon makes large datasets available on its Amazon Web Services platform. You can download the data and work with it on your own computer or analyze the data in the cloud using EC2 and Hadoop via EMR . You can read more about how the program works here .
Amazon has a page that lists all of the datasets for you to browse. You’ll need an AWS account, although Amazon provides a free access tier for new accounts that will enable you to explore the data without being charged.
View AWS Public Datasets
- Lists of n-grams from Google Books — common words and groups of words from a huge set of books.
- Common Crawl Corpus — data from a crawl of over 5 billion web pages.
- Landsat Images — moderate resolution satellite images of the surface of the Earth.
5. Google Public Data sets
Much like Amazon, Google also has a cloud-hosting service, called Google Cloud Platform . With GCP, you can use a tool called BigQuery to explore large datasets.
Google lists all of the data sets on a page. You’ll need to sign up for a GCP account, but the first 1TB of queries you make are free .
View Google Public Datasets
- USA Names — contains all Social Security name applications in the US, from 1879 to 2015.
- Github Activity — contains all public activity on over 2.8 million public Github repositories.
Historical Weather — data from 9000 NOAA weather stations from 1929 to 2016.
6. Wikipedia
Wikipedia is a free, online, community-edited encyclopedia. Wikipedia contains an astonishing breadth of knowledge, containing pages on everything from the Ottoman-Habsburg Wars to Leonard Nimoy . As part of Wikipedia’s commitment to advancing knowledge, they offer their content for free and regularly generate dumps of all the articles on the site. Additionally, Wikipedia offers edit history and activity, so you can track how a page on a topic evolves over time and who contributes to it.
You can find the various ways to download the data on the Wikipedia site. You’ll also find scripts to reformat the data in various ways.
View Wikipedia Datasets
- All Images and Other Media from Wikipedia — all the images and other media files on Wikipedia.
- Full Site Dumps — of the content on Wikipedia, in various formats.
Public Datasets for Machine Learning Projects
When you’re working on a machine learning project, you want to be able to predict a column from the other columns in a dataset. In order to be able to do this, we need to make sure that:
- The dataset isn’t too messy — if it is, we’ll spend all of our time cleaning the data.
- There’s an interesting target column to make predictions for.
- The other variables have some explanatory power for the target column.
There are a few online repositories of datasets that are specifically for machine learning. These datasets are typically cleaned up beforehand, and allow for testing of algorithms very quickly.
Kaggle is a data science community that hosts machine learning competitions. There are a variety of externally-contributed, interesting datasets on the site. Kaggle has both live and historical competitions. You can download data for either, but you have to sign up for Kaggle and accept the terms of service for the competition.
You can download data from Kaggle by entering a competition . Each competition has its own associated dataset. There are also user-contributed datasets found in the new Kaggle Datasets offering.
View Kaggle Datasets
- Satellite Photograph Order — a dataset of satellite photos of Earth — the goal is to predict which photos were taken earlier than others.
- Manufacturing Process Failures — a dataset of variables that were measured during the manufacturing process. The goal is to predict faults with the manufacturing.
Multiple Choice Questions — a dataset of multiple choice questions and the corresponding correct answers. The goal is to predict the answer for any given question.
8. UCI Machine Learning Repository
The UCI Machine Learning Repository is one of the oldest sources of datasets on the web. Although the datasets are user-contributed, and thus have varying levels of documentation and cleanliness, the vast majority are clean and ready for machine learning to be applied. UCI is a great first stop when looking for interesting datasets.
You can download data directly from the UCI Machine Learning repository, without registration. These datasets tend to be fairly small, and don’t have a lot of nuance, but are good for machine learning.
View UCI Machine Learning Repository
- Email Spam — contains emails, along with a label of whether or not they’re spam.
- Wine Classification — contains various attributes of 178 different wines.
Solar Flares — attributes of solar flares, useful for predicting characteristics of flares.
Quandl is a repository of economic and financial data. Some of this information is free, but many datasets require purchase. Quandl is useful for building models to predict economic indicators or stock prices. Due to the large number of available datasets, it’s possible to build a complex model that uses many datasets to predict values in another.
View Quandl Datasets .
- Entrepreneurial Activity By Race and Other Factors — contains data from the Kauffman foundation on entrepreneurs in the US.
- US Federal Reserve Data — US economic indicators, from the Federal Reserve.
Public Datasets for Data Cleaning Projects
When looking for a good dataset for a data cleaning project, you want:
- Be spread over multiple files.
- Have a lot of nuance, and many possible angles to take.
- Require a good amount of research to understand.
- Be as “real-world” as possible.
These types of datasets are typically found on aggregators of datasets. These aggregators tend to have datasets from multiple sources, without much curation. Too much curation gives us overly neat datasets that are hard to do extensive cleaning on.
10. data.world
data.world describes itself as ‘the social network for data people,’ but could be more correctly described as ‘GitHub for data.’ It’s a place where you can search for, copy, analyze, and download datasets. In addition, you can upload your data to data.world and use it to collaborate with others.
In a relatively short time it has become one of the ‘go to’ places to acquire data, with lots of user contributed datasets as well as fantastic datasets through data.world’s partnerships with various organizations, including a large amount of data from the US Federal Government.
One key differentiator of data.world is they have built tools to make working with data easier – you can write SQL queries within their interface to explore data and join multiple datasets. They also have SDK’s for R and Python to make it easier to acquire and work with data in your tool of choice (You might be interested in reading our tutorial on the data.world Python SDK .)
View data.world Datasets
11. Data.gov
Data.gov is a relatively new site that’s part of a US effort towards open government. Data.gov makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Much of the data requires additional research, and it can sometimes be hard to figure out which dataset is the “correct” version. Anyone can download the data, although some datasets require additional hoops to be jumped through, like agreeing to licensing agreements.
You can browse the data sets on Data.gov directly, without registering. You can browse by topic area or search for a specific dataset.
View Data.gov Datasets
- Food Environment Atlas — contains data on how local food choices affect diet in the US.
- School System Finances — a survey of the finances of school systems in the US.
Chronic Disease Data — data on chronic disease indicators in areas across the US.
12. The World Bank
The World Bank is a global development organization that offers loans and advice to developing countries. The World Bank regularly funds programs in developing countries, then gathers data to monitor the success of these programs.
You can browse World Bank datasets directly, without registering. The datasets have many missing values, and sometimes take several clicks to actually get to data.
View World Bank Datasets
- World Development Indicators — contains country-level information on development.
- Educational Statistics — data on education by country.
World Bank Project Costs — data on World Bank projects and their corresponding costs.
13. /r/datasets
Reddit , a popular community discussion site, has a section devoted to sharing interesting datasets. It’s called the datasets subreddit , or /r/datasets. The scope of these datasets varies a lot, since they’re all user-submitted, but they tend to be very interesting and nuanced.
You can browse the subreddit here . You can also see the most highly upvoted datasets here .
View Top /r/datasets Posts
- All Reddit Sublessons — contains reddit sublessons through 2015.
- Jeopardy Questions — questions and point values from the game show Jeopardy.
New York City Property Tax Data — data about properties and assessed value in New York City.
14. Academic Torrents
Academic Torrents is a new site that is geared around sharing the datasets from scientific papers. It’s a newer site, so it’s hard to tell what the most common types of datasets will look like. For now, it has tons of interesting datasets that lack context.
You can browse the datasets directly on the site. Since it’s a torrent site, all of the datasets can be immediately downloaded, but you’ll need a Bittorrent client. Deluge is a good free option.
View Academic Torrents Datasets
- Enron Emails — a set of many emails from executives at Enron, a company that famously went bankrupt.
- Student Learning Factors — a set of factors that measure and influence student learning.
- News Articles — contains news article attributes and a target variable.
Bonus: Streaming data
It’s very common when you’re building a data science project to download a dataset and then process it. However, as online services generate more and more data, an increasing amount is generated in real-time, and not available in dataset form. Some examples of this include data on tweets from Twitter , and stock price data. There aren’t many good sources to acquire this kind of data, but we’ll list a few in case you want to try your hand at a streaming data project.
15. Twitter
Twitter has a good streaming API, and makes it relatively straightforward to filter and stream tweets. You can get started here . There are tons of options here — you could figure out what states are the happiest, or which countries use the most complex language. We also recently wrote an article to get you started with the Twitter API here .
Get started with the Twitter API
GitHub has an API that allows you to access repository activity and code. You can get started with the API here . The options are endless — you could build a system to automatically score code quality, or figure out how code evolves over time in large projects.
Get started with the Github API
17. Wunderground
Wunderground has an API for weather forecasts that free up to 500 API calls per day. You could use these calls to build up a set of historical weather data, and make predictions about the weather tomorrow.
Get started with the Wunderground API
18. Global Health Observatory
The World Health Organization (WHO) maintains a large dataset on global health at the Global Health Observatory (GHO) . The dataset includes all the WHO data on the COVID-19 global pandemic. The GHO offers a diverse range of data on topics such as antimicrobial resistance, dementia, air pollution, and immunization.
You can find data on pretty much any health-related topic at the GHO, making it an extremely valuable free dataset resource for data scientists working in the health field.
View WHO’s datasets.
19. Pew Research Center
The Pew Research Center is well-known for political and social science research. In the interest of furthering research and public discourse, they make all of their datasets publicly downloadable for secondary analysis, after a set period of time elapses.
You can choose from datasets on US politics, journalism and media, internet and tech, science and society, religion and public life, amongst other topics.
20. National Climatic Data Center
Climate change is a hot topic at the moment, if you’ll pardon the pun. Data scientists who want to crunch the numbers on weather and climate can access large US datasets from the National Centers for Environmental Information (NCEI) .
Bonus: Personal Data
The internet is full of cool datasets you can work with. But for something truly unique, what about analyzing your own personal data?
Here are some popular sites that make it possible to download and work with data you’ve generated.
Amazon allows you to download your personal spending data, order history, and more. To access it, click this link (you’ll need to be logged in for it to work) or navigate to the Accounts and Lists button in the top right.
On the next page, look for the Ordering and Shopping Preferences section, and click on the link under that heading that says “Download order reports.”Here is a simple data project tutorial that you could do using your own Amazon data to analyze your spending habits.
22. Facebook
Facebook also allows you to download your personal activity data. To access it, click this link (you’ll need to be logged in for it to work) and select the types of data you’d like to download.Here is an example of a simple data project you could build using your own personal Facebook data .
23. Netflix
Netflix allows you to request your own data for download , although it will make you jump through a few hoops, and will warn you that the process of collating your data may take 30 days. As of the last time we checked, the data they allow you to download is fairly limited, but it could still be suitable for some types of projects and analysis.
Extra Bonus: Powerful Dataset Search Tool
24. google dataset search.
OK, so this isn’t strictly a dataset – rather a search tool to find relevant datasets. As you already know, Google is a data powerhouse, so it makes sense that their search tool knocks the socks off of other ways to find specific datasets.
All you need to do is head over to Google Dataset Search and type a keyword or phrase related to the dataset you’re looking for in the search bar. The results will list all the datasets indexed on Google for that particular search term. The datasets are generally from high-quality sources, of which some are free and others available for a fee or subscription.
In this post, we covered good places to find datasets for any type of data science project. We hope that you find something interesting that you want to sink your teeth into!
At Dataquest , our interactive guided projects are designed to help you start building a data science portfolio to demonstrate your skills to employers and get a job in data. If you’re interested, you can sign up and do our first module for free .
If you liked this, you might like to read the other posts in our ‘Build a Data Science Portfolio’ series:
- Storytelling with data
- How to set up up a data science blog
- Building a machine learning project
- The key to building a data science portfolio that will get you a job
How to present your data science portfolio on Github .
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Oct 5, 2018
Data science capstone ideas (and how to get started)
Capstones are standalone projects meant to integrate, synthesize, and demonstrate all your data science knowledge in a multi-faceted way. Capstone projects show your readiness for using data science in real life, and are ideally something you can add to your resume, show to employers, or even use to start a career.
I find data science capstone ideas are like puppies: you want all of them, but can only keep one. Below is a list of some of my ideas and starting points.
Idea #1: Nutritional analysis from Instacart orders
In 2017 Instacart released a dataset of over 3 million grocery orders from over 200,000 users as a Kaggle competition . With a dataset this juicy, immediately a few ideas come time to mind:
- Predict what products users will order again (this was the goal of the Kaggle challenge).
- Build a model to stock the store so there are never any product shortages, but no wasted space or money in ordering.
- Predict a user’s healthiness from order content.
- Make a recommender system for healthier order alternatives.
The first and second are doable with the data you already have, which is nice.
The third was my personal choice, using the USDA food composition database to look up products and create a nutritional breakdown (by the way, they have an API ). But it also introduced a lot of hurdles:
- Users don’t eat everything they order (e.g. cat food, soap, toilet paper). This would require a lot of cleaning and munging.
- Users don’t order just for themselves (e.g. companies, birthday parties, families).
- Users order on different timelines (e.g. once per week, once every two weeks, once a month).
- Items such as deli food may not have entries in the USDA database.
The fourth would also utilize the USDA database, but would not require any user-specific information or messing about with time-series.
I dea #2: Predicting solar output from satellite imaging/historical weather
One of the big issues with mainstream adoption of solar power is unlike other energy sources (hydroelectric, oil, nuclear), you can’t control how long the sun shines for. Overestimating this amount means losses for producers and investors, and downtime for users. Underestimating means a lower chance of adoption in upfront decision-making. Sounds like a job for… machine learning!
Many datasets can be found at NREL , however they are in different years and different locations with limits on how much you can download at once. They have an API , which is useful.
SolarAnywhere has an academic license, allowing you to look up any location (but only for the year 2013). They too have an API .
Also, the NREL NSRDB data viewer .
There are three immediate approaches I can think of:
- Using previous solar output to predict current solar output (time-series or RNN).
- Using weather datasets
- Using satellite imaging datasets
There are a lot of academic papers on this last subject ( a quick Google Scholar search returns about 30,000 results ), but not a lot of publicly available satellite time-series datasets.
Idea #3: Fake news detection
This is a hot one. Without going into full rant-mode, fake news is obviously deleterious for democracy and individual mental stability.
So how to accurately identify what’s fake and what’s true? Here are a few leads on this as a data science problem:
1. Fake News Challenge
This is the best-formatted challenge around this topic, with organizers, advisors, and volunteers from the academic, ML, and fact-checking communities. Includes GitHub repos of winning submissions. Check out the competition page on Codalab.
2. Snopes Junk News
A starting point for well-verified fake news stories vs. actual events.
3. Getting Real About Fake News — Kaggle Dataset
A collection of nearly 13,000 items from 244 websites tagged “BS” from the BS Detector chrome extension. The BS Detector is powered by Open Sources , a project that classifies biased and fake websites.
Where To Get More Ideas
Never stop searching! Here are some ways to get more leads, either in the form of project ideas or datasets to use.
1. Academic papers
2. Kaggle Competitions
3. Kaggle Datasets
4. reddit.com/r/datasets
5. Awesome Public Datasets GitHub Repo
6. Google Datasets
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9 Project Ideas for Your Data Analytics Portfolio
Finding projects for your data analytics portfolio can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.
In this post, we’ll highlight the key elements that your data analytics portfolio should demonstrate. We’ll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping , exploratory analysis , and data visualization .
We’ll cover:
- What should you include in your data analytics portfolio?
Data scraping project ideas
Exploratory data analysis project ideas, data visualization project ideas.
- What’s next?
Ready to get inspired? Let’s go!
1. What should you include in your data analytics portfolio?
Data analytics is all about finding insights that inform decision-making. But that’s just the end goal. As any experienced data analyst will tell you, the insights we see as consumers are the result of a great deal of work. In fact, about 80% of all data analytics tasks involve preparing data for analysis. This makes sense when you think about it—after all, our insights are only as good as the quality of our data.
Yes, your portfolio needs to show that you can carry out different types of data analysis . But it also needs to show that you can collect data, clean it, and report your findings in a clear, visual manner. As your skills improve, your portfolio will grow in complexity. As a beginner though, you’ll need to show that you can:
- Scrape the web for data
- Carry out exploratory analyses
- Clean untidy datasets
- Communicate your results using visualizations
If you’re inexperienced, it can help to present each item as a mini-project of its own. This makes life easier since you can learn the individual skills in a controlled way. With that in mind, we’ll keep it nice and simple with some basic ideas, and a few tools you might want to explore to help you along the way.
2. Data scraping project ideas for your portfolio
What is data scraping.
Data scraping is the first step in any data analytics project. It involves pulling data (usually from the web) and compiling it into a usable format. While there’s no shortage of great data repositories available online, scraping and cleaning data yourself is a great way to show off your skills.
The process of web scraping can be automated using tools like Parsehub , ScraperAPI , or Octoparse (for non-coders) or by using libraries like Beautiful Soup or Scrapy (for developers). Whichever tool you use, the important thing is to show that you understand how it works and can apply it effectively.
Before scraping a website, be sure that you have permission to do so. If you’re not certain, you can always search for a dataset on a repository site like Kaggle . If it exists there, it’s a good bet you can go straight to the source and scrape it yourself. Bear in mind though—data scraping can be challenging if you’re mining complex, dynamic websites. We recommend starting with something easy—a mostly-static site. Here are some ideas to get you started.
The Internet Movie Database
A good beginner’s project is to extract data from IMDb. You can collect details about popular TV shows, movie reviews and trivia, the heights and weights of various actors, and so on. Data on IMDb is stored in a consistent format across all its pages, making the task a lot easier. There’s also a lot of potential here for further analysis.
Job portals
Many beginners like scraping data from job portals since they often contain standard data types. You can also find lots of online tutorials explaining how to proceed. To keep it interesting, why not focus on your local area? Collect job titles, companies, salaries, locations, required skills, and so on. This offers great potential for later visualization, such as graphing skillsets against salaries.
E-commerce sites
Another popular one is to scrape product and pricing data from e-commerce sites. For instance, extract product information about Bluetooth speakers on Amazon, or collect reviews and prices on various tablets and laptops. Once again, this is relatively straightforward to do, and it is scalable. This means you can start with a product that has a small number of reviews, and then upscale once you’re comfortable using the algorithms.
For something a bit less conventional, another option is to scrape a site like Reddit. You could search for particular keywords, upvotes, user data, and more. Reddit is a very static website, making the task nice and straightforward. Later, you can carry out interesting exploratory analyses, for instance, to see if there are any correlations between popular posts and particular keywords. Which brings us to our next section.
3. Exploratory data analysis project ideas
What is exploratory data analysis.
The next step in any data analyst’s skillset is the ability to carry out an exploratory data analysis (EDA). An EDA looks at the structure of data, allowing you to determine their patterns and characteristics. They also help you to clean your data. You can extract important variables, detect outliers and anomalies, and generally test your underlying assumptions.
While this process is one of the most time-consuming tasks for a data analyst, it can also be one of the most rewarding. Later modeling focuses on generating answers to specific questions. An EDA, meanwhile, helps you do one of the most exciting bits—generating those questions in the first place.
Languages like R and Python are often used to carry out these tasks. They have many pre-existing algorithms that you can use to carry out the work for you . The real skill lies in presenting your project and its results. How you decide to do this is up to you, but one popular method is to use an interactive documentation tool like Jupyter Notebook . This lets you capture elements of code, along with explanatory text and visualizations, all in one place. Here are some ideas for your portfolio.
Global suicide rates
This global suicide rates dataset covers suicide rates in various countries, with additional data including year, gender, age, population, GDP, and more. When carrying out your EDA, ask yourself: What patterns can you see? Are suicides rates climbing or falling in various countries? What variables (such as gender or age) can you find that might correlate to suicide rates?
World Happiness Report
On the other end of the scale, the World Happiness Report tracks six factors to measure happiness across the world’s citizens: life expectancy, economics, social support, absence of corruption, freedom, and generosity. So, which country is the happiest? Which continent? Which factor appears to have the greatest (or smallest) impact on a nation’s happiness? Overall, is happiness increasing or decreasing?
Aside from the two ideas above, you could also use your own datasets . After all, if you’ve already scraped your own data, why not use them? For instance, if you scraped a job portal, which locations or regions offer the best-paid jobs? Which offer the least well-paid ones? Why might that be? Equally, with e-commerce data, you could look at which prices and products offer the best value for money.
Ultimately, whichever dataset you’re using, it should grab your attention. If the data are too complex or don’t interest you, you’re likely to run out of steam before you get very far. Keep in mind what further probing you can do to spot interesting trends or patterns, and to extract the insights you need.
We’ve compiled a list of ten great places to find free datasets for your next project here .
4. Data visualization project ideas
What is data visualization.
Scraping, tidying, and analyzing data is one thing. Communicating your findings is another. Our brains don’t like looking at numbers and figures, but they love visuals. This is where the ability to create effective data visualizations comes in. Good visualizations—whether static or interactive—make a great addition to any data analytics portfolio. Showing that you can create visualizations that are both effective and visually appealing will go a long way towards impressing a potential employer.
Check out this video with Dr. Humera, where she explains how visualization helps tell a story with data.
Some free visualization tools include Google Charts , Canva Graph Maker (free), and Tableau Public . Meanwhile, if you want to show off your coding abilities, use a Python library such as Seaborn , or flex your R skills with Shiny . Needless to say, there are many tools available to help you. The one you choose depends on what you’re looking to achieve. Here’s a bit of inspiration…
Topical subject matter looks great on any portfolio, and the pandemic is nothing if not topical! What’s more, sites like Kaggle already have thousands of Covid-19 data sets available . How can you represent the data? Could you use a global heatmap to show where cases have spiked, versus where there are very few? Perhaps you could create two overlapping bar charts to show known infections versus predicted infections. Here’s a handy tutorial to help you visualize Covid-19 data using R, Shiny, and Plotly .
Most followed on Instagram
Whether you’re interested in social media, or celebrity and brand culture, this dataset of the most-followed people on Instagram has great potential for visualization. You could create an interactive bar chart that tracks changes in the most followed accounts over time. Or you could explore whether brand or celebrity accounts are more effective at influencer marketing. Otherwise, why not find another social media dataset to create a visualization? For instance, this map of the USA by data scientist Greg Rafferty nicely highlights the geographical source of trending topics on Instagram.
Travel data
Another topic that lends itself well to visualization is transport data. Here’s a great project by Chen Chen on github , using Python to visualize the top tourist destinations worldwide, and the correlation between inbound/outbound tourists with gross domestic product (GDP).
5. What’s next?
In this post, we’ve explored which skills every beginner needs to demonstrate in their data analytics portfolio. Regardless of the dataset you’re using, you should be able to demonstrate the following abilities:
- Web scraping —using tools like Parsehub, Beautiful Soup, or Scrapy to extract data from websites (remember: static ones are easier!)
- Exploratory data analysis and data cleaning —manipulating data with tools like R and Python, before drawing some initial insights.
- Data visualization —utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.
Once you’ve mastered the basics, you can start getting more ambitious with your data analytics projects. For example, why not introduce some machine learning projects, like sentiment analysis or predictive analysis? The key thing is to start simple and to remember that a good data analytics portfolio needn’t be flashy, just competent.
To further develop your skills, there are loads of online courses designed to set you on the right track. To start with, why not try our free, five-day data analytics short course ?
And, if you’d like to learn more about becoming a data analyst and building your portfolio, check out the following:
- How to build a data analytics portfolio
- The best data analytics certification programs on the market right now
- These are the most common data analytics interview questions
26 Data Analytics Project Ideas and Datasets (2022)
Data analytics projects help you build your portfolio and land interviews. However, it’s not enough to just do an interesting analytics project. You also have to market your project to ensure it gets found.
The first step in starting any data analytics project is to come up with an interesting problem to investigate. Then, you need to find a dataset to analyze the problem. Some of the best categories for data analytics project ideas include:
- Python Analytics Projects - Python allows you to scrape interesting data, as well as perform analysis with pandas dataframes and SciPy libraries.
- Housing Analytics Projects - Housing data is readily available, or you can create your own dataset, and there are a variety of interesting analyses you can perform.
- Sports and NBA Analytics Projects - Sports data can be easily scraped, and using player and game stats, you can analyze strategies and performance.
- Data Visualization Projects - Visualizations allow you to create graphs, charts, etc, to tell a story about the data.
- Beginner Analytics Projects - For early-career data analysts, beginner projects help you practice new skills.
A data analytics portfolio is a powerful tool for landing an interview. But how can you build one effectively?
Start with a data analytics project and build your portfolio around it. A data analytics project involves taking a dataset and analyzing it in a specific way to showcase results. Not only do they help you build your portfolio, but analytics projects also help you:
- Learn new tools and techniques
- Work with complex datasets
- Practice packaging your work and results
- Prep for a case study and take-home interviews
- Give you inbound interviews from hiring managers that have read your blog post!
Python Data Analytics Projects
Python is a powerful tool for data analysis projects. Whether you’re web scraping data - on sites like the New York Times and Craigslist- or you’re conducting Exploratory Data Analysis (EDA) on Uber trips, here are three Python data analytics project ideas to try:
1. Enigma Transforming CSV file Take-Home
This take-home challenge - which requires 1-2.5 hours to complete - is a Python script writing task. You’re asked to write a script to transform input CSV data to desired output CSV data. A take-home like this is good practice for the type of Python take-homes that are asked of data analysts, data scientists, and data engineers.
As you work through this practice challenge, focus specifically on the grading criteria, which include:
- How well do you solve the problems
- The logic and approach you take to solving them
- Your ability to produce, document, and comment on code
- Ultimately, the ability to write clear and clean scripts for data preparation.
2. Wedding Crunchers
Todd W. Schneider’s Wedding Crunchers is a great example of a data analysis project using Python. Essentially, Todd scraped wedding announcements from the New York Times, and performed analysis on the data, finding interesting tidbits like:
- Distribution of common phrases
- Average age trends of brides and grooms
- Demographic trends
Using the data and his analysis Schneider created a lot of cool visuals, like this:
How you can do it: Follow the example of Wedding Crunchers. Choose a news or media source, scrape titles and text, and analyze the data for trends. Here’s a tutorial for scraping news APIs with Python.
3. Scraping Craigslist
Craigslist is a great data source for an analytics project, and there is a wide range of things you can analyze. One of the most common listings is for apartments.
Riley Predum created a handy tutorial that walks you through the steps of using Python and Beautiful Soup to scrape the data to pull apartment listings, and then was able to do some pretty cool analysis of pricing by neighborhood and price distributions. When graphed, his analysis looked like this:
How you can do it: Follow the tutorial to learn how to scrape the data using Python. Some analysis ideas: Look at apartment listings for another area, analyze used car prices for your market, or check out what used items sell on Craigslist.
4. Uber Trip Analysis
Here’s an interesting project from Aman Kharwal: An analysis of Uber trip data from NYC. The project used this Kaggle dataset from FiveThirtyEight , containing nearly 20 million Uber pickups. There are a lot of angles to analyze this dataset, like popular pickup times or the busiest days of the week.
Here’s a data visualization on pickup times by hour of the day from Aman:
How you can do it: This is a data analysis project idea if you’re prepping for a case study interview. You can emulate this one, using the dataset on Kaggle, or you can use these similar taxies and Uber datasets on data.world, including one for Austin, TX.
5. Twitter Sentiment Analysis
Twitter is the perfect data source for an analytics project, and you can perform a wide range of analyses based on Twitter datasets. Sentiment analysis projects are great for practicing beginner NLP techniques.
One option would be to measure sentiment in your dataset over time like this:
How you can do it: This tutorial from Natassha Selvaraj provides step-by-step instructions to do sentiment analysis in Twitter. Or see this tutorial from the Twitter developer forum . For data, you can scrape your own or pull some from these free datasets .
6. Home Pricing Predictions
This project was featured in our list of Python data science projects . With this project, you can take the classic California Census dataset , and use it to predict home prices by region, zip code, or details about the house.
Python can be used to produce some great visualizations, like this heat map of price by location:
How you can do it: Because this dataset is so well known, there are a lot of helpful tutorials to learn how to predict price in Python . Then, once you’ve learned the technique, you can start practicing it on a variety of datasets like stock prices, used car prices, or airfare.
Rental and Housing Data Analytics Project Ideas
There’s a ton of accessible housing data online, e.g. sites like Zillow and Airbnb, and these datasets are perfect for analytics and EDA projects.
If you’re interested in price trends in housing, market predictions, or just want to analyze the average home prices for a specific city or state, jump into these projects:
7. Airbnb Data Analytics Take-Home Assignment
- Overview: Analyze the provided data and make product recommendations to help increase bookings in Rio de Janeiro.
- Time Required: 6 hours
- Skills Tested: Analytics, EDA, growth marketing, data visualization
- Deliverable: Summarize your recommendations in response to the questions above in a Jupyter Notebook intended for the Head of Product and VP of Operations (who is not technical).
This take-home is a classic product case study. You have booking data for Rio de Janeiro, and you must define metrics for analyzing matching performance and make recommendations to help increase the number of bookings.
This take-home includes grading criteria, which can help direct your work. Assignments are judged on the following:
- Analytical approach and clarity of visualizations
- Your data sense and decision-making, as well as the reproducibility of the analysis
- Strength of your recommendations
- Your ability to communicate insights in your presentation
- Your ability to follow directions
8. Zillow Housing Prices
Check out Zillow’s free datasets. The Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted average of housing market values by region and housing type. There are also datasets on rentals, housing inventories, and price forecasts.
Here’s an analytics project based in R that might give you some direction. The author analyzes Zillow data for Seattle, looking at things like the age of inventory (days since listing), % of homes that sell for a loss or gain, and list price vs. sale price for homes in the region:
How you can do it: There are a ton of different ways you can use the Zillow dataset. Examine listings by region, explore individual list price vs. sale price, or take a look at the average sale price over the average list price by city.
9. Inside Airbnb
On Inside Airbnb , you’ll find data from Airbnb that has been analyzed, cleaned, and aggregated. You’ll find data for dozens of cities around the world, including number of listings, calendars for listings, and reviews for listings.
Here’s a look at a project from Agratama Arfiano examining Airbnb data for Singapore. There are a lot of different analyses you can do, including finding the number of listings by host or listings by neighborhood. Arfiano has produced some really great visualizations for this project, like the following:
How you can do it: Download the data from Inside Airbnb, then choose a city for analysis. You can look at the price, listings by area, listings by the host, the average number of days a listing is rented, and much more.
10. Car Rentals
Have you ever wondered which cars are the most rented? Curious how fares change by make and model? Check out the Cornell Car Rental Dataset on Kaggle. Kushlesh Kumar created the dataset, which features records on 6,000+ rental cars. There are a lot of interesting questions you can answer with this dataset: Fares by make and model, fares by city, inventory by city, and much more. Here’s a cool visualization from Kushlesh:
How you can do it: Using the dataset, you could analyze rental cars by make and model, a specific location, or analyze specific car manufacturers. Another option: Try a similar project with these datasets: Cash for Clunkers cars , Carvana sales data or used cars on eBay.
11. Analyzing NYC Property Sales
This real estate dataset shows every property that sold in New York City between September 2016 and September 2017. You can use this data (or a similar dataset you create) for a number of projects, including EDA, price predictions, regression analysis, and data cleaning.
A beginner analytics project you can try would with this data would be a missing values analysis project like:
How you can do it: There are a ton of helpful Kaggle notebooks you can browse to learn how to: perform price predictions, do data cleaning tasks, or do some interesting EDA with this dataset.
Sports and NBA Data Analytics Projects
Sports data analytics projects are fun if you’re a fan, and also, because there are numerous free data sources available like Pro-Football-Reference and Basketball-Reference. These sources allow you to pull numerous statistics and build your own unique dataset to investigate a problem.
12. NBA Data Analytics Project
Check out this NBA data analytics project from Jay at Interview Query. Jay analyzed data from Basketball Reference (a great source, by the way) to determine the impact of the 2-for-1 play in the NBA. The idea: In basketball, the 2-for-1 play refers to the strategy that at the end of a quarter, a team aims to shoot the ball with between 25 and 36 seconds on the clock. That way the team that shoots first has time for an additional play while the opposing team only gets one response. (You can see the source code on GitHub).
The main metric he was looking for was the differential gain between the score just before the 2-for-1 shot and the score at the end of the quarter. Here’s a look at a differential gain:
How you can do it: Read this tutorial on scraping Basketball Reference data. You can analyze in-game statistics, play career statistics, playoff performance, and much more. One option would be to analyze a player’s high school ranking vs. their success in the NBA. Or you could visualize a player’s career.
13. Olympic Medals Analysis
This is a great dataset for a sports analytics project. Featuring 35,000 medals awarded since 1896, there’s plenty of data to analyze, and it’s great for identifying performance trends by country and sport. Here’s an interesting visualization from Didem Erkan:
How you can do it: Check out the Olympics medals dataset. Angles you might take for analysis include: Medal count by country (as in this visualization ), medal trends by country, e.g. how U.S. performance evolved during the 1900s, or even grouping countries by region to see how fortunes have risen or faded over time.
14. Soccer Power Rankings
FiveThirtyEight is a wonderful source of sports data; they have NBA datasets, as well as data for the NFL and NHL. The site uses its Soccer Power Index (SPI) ratings for predictions and forecasts, but it’s also a good source for analysis and analytics projects. To get started, check out Gideon Karasek’s breakdown of working with the SPI data.
How you can do it: Check out the SPI data. Questions you might try to answer include: How has a team’s SPI changed over time, comparisons of SPI amongst various soccer leagues, and goals scored vs. goals predicted?
15. Home Field Advantage Analysis
Does home-field advantage matter in the NFL? Can you quantify how much it matters? First, gather data from Pro-Football-Reference.com. Then you can perform a simple linear regression model to measure the impact.
There are a ton of projects you can do with NFL data. One would be to determine WR rankings, based on season performance .
How you can do it: See this Github repository on performing a linear regression to quantify home field advantage .
16. Daily Fantasy Sports
Creating a model to perform in daily fantasy sports requires you to:
- Predict which players will perform best based on matchups, locations, and other indicators
- Build a roster based on a “salary cap” budget
- Determine which players will have the top ROI during the given week
If you’re interested in fantasy football, basketball, or baseball, this would be a great project.
How you can do it: Check out the Daily Fantasy Data Science course , if you want a step-by-step look.
Data Visualization Projects
All of the datasets we’ve mentioned would make for amazing data visualization projects. To cap things off we are highlighting three more ideas for you to use as inspiration that potentially draws from your own experiences or interests!
17. Supercell Data Scientist Pre-Test
This is a classic SQL/data analytics take-home. You’re asked to explore, analyze, visualize and model Supercell’s revenue data. Specifically, the dataset contains user data and transactions tied to user accounts.
You must answer questions about the data, like which countries produce the most revenue. Then, you’re asked to create a visualization of the data, as well as apply machine learning techniques to it.
18. Visualize Your Favorite Book
Books are full of data, and you can create some really amazing visualizations using the patterns from them. Take a look at this project by Hanna Piotrowska, turning an Italo Calvo book into cool visualizations. The project features visualizations of word distributions, themes and motifs by chapter, and a visualization of the distribution of themes throughout the book:
How you can do it: This Shakespeare dataset , which features all of the lines from his plays, would be great for recreating this type of project. Another option: Create a visualization of your favorite Star Wars script .
19. Visualizing Pollution
This project by Jamie Kettle visualizes plastic pollution by country, and it does a scarily good job of showing just how much plastic waste enters the ocean each year. Take a look for inspiration:
How you can do it: There are dozens of pollution datasets on data.world . Choose one and create a visualization that shows the true impact of pollution on our natural environments.
20. Visualizing Top Movies
There are a ton of great movie and media datasets on Kaggle: The Movie Database 5000 , Netflix Movies and TV Shows , Box Office Mojo data , etc. And just like their big-screen debuts, movie data makes for great visualizations.
Take a look at this visualization of the Top 100 movies by Katie Silver , which features top movies based on box office gross and the Oscars each received:
How you can do it: Take a Kaggle movie dataset, and create a visualization that shows: Gross earnings vs. average IMDB rating, Netflix shows by rating, or visualization of top movies by the studio.
21. Gender Pay Gap Analysis
Salary is a subject everyone is interested in and it makes a great subject for visualization. One idea: Take this dataset from the U.S. Bureau of Labor Statistics , and create a visualization looking at the gap in pay by industry.
You can see an example of a gender pay gap visualization on InformationIsBeautiful.net:
How you can do it: You can re-create the gender pay visualization, and add your own spin. Or use salary data to visualize, fields with the fastest growing salaries, salary differences by cities, or data science salaries by the company .
Beginner Data Analytics Projects
Projects are one of the best ways for beginners to practice data science skills, including visualization, data cleaning, and working with tools like Python and pandas.
22. Relax Predicting User Adoption Take-Home
This data analytics take-home assignment, which has been given to data analysts and data scientists at Relax Inc., asks you to dig into user engagement data. Specifically, you’re asked to determine who an “adopted user” is, which is a user who has logged into the product on three separate days in at least one seven-day period.
Once you’ve identified adopted users, you’re asked to surface factors that predict future user adoption.
How you can do it: Jump into the Relax take-home data. This is an intensive data analytics take-home challenge, which the company suggests you spend 12 hours on (although you’re welcome to spend more or less). This is a great project for practicing your data analytics EDA skills, as well as surfacing predictive insights from a dataset.
23. Data Cleaning Practice
This Kaggle Challenge asks you to clean data , and perform a variety of data cleaning tasks. This is a great beginner data analytics project, that will provide hands-on experience performing techniques like handling missing values, scaling and normalization, and parsing dates.
How you can do it: You can work through this Kaggle Challenge, which includes data. Another option, however, would be to choose your own dataset that needs to be cleaned, and then work through the challenge and adapt the techniques to your own dataset.
24. Skilledup Messy Product Data Analysis Take-Home
This data analytics take-home from Skilledup, asks participants to perform analysis on a dataset of product details that is formatted inconveniently. This challenge provides an opportunity to show your data cleaning skills, as well as your ability to perform EDA and surface insights from an unfamiliar dataset. Specifically, the assignment asks you to consider one product group, named Books.
Each product in the group is associated with categories. Of course, there are tradeoffs to categorization, and you’re asked to consider these questions:
- Is there redundancy in the categorization?
- How can redundancy be identified and removed?
- Is it possible to reduce the number of categories dramatically by sacrificing relatively few category entries?
How you can do it: You can access this EDA takehome on Interview Query. Open the dataset and perform some EDA to familiarize yourself with the categories. Then, you can begin to consider the questions that are posed.
25. Marketing Analytics Exploratory Data Analysis
This marketing analytics dataset on Kaggle includes customer profiles, campaign successes and failures, channel performance, and product preferences. It’s a great tool for diving into marketing analytics, and there are a number of questions you can answer from the data like:
- What factors are significantly related to the number of store purchases?
- Is there a significant relationship between geographical regional and success of a campaign?
- How does the US compare to the rest of the world in terms of total purchases?
How you can do it: This Kaggle Notebook from user Jennifer Crockett is a great place to start, which includes a lot of great visualizations and analyses (like the one above).
If you want to take it a step further, there’s a lot of statistical analysis you can perform as well.
26. UFO Sightings Data Analysis
The UFO Sightings dataset is a fun one to dive into, and it contains data from more than 80,000 sightings over the last 100 years. This is a great source for a beginner EDA project, and you can draw a lot of insights out like where sightings are reported most frequently sightings in the US vs the world, and more.
How you can do it: Jump into the dataset on Kaggle. There are a number of notebooks you can check out with helpful code snippets. If you’re looking for a challenge, one user created an interactive map with sighting data .
More Analytics Project Resources
If you are still looking for inspiration, see our compiled list of free datasets which features sites to search for free data, datasets for EDA projects and visualizations, as well as datasets for machine learning projects.
You should also read our guide on the data analyst career path , how to build a data science project from scratch and list of 30 data science project ideas .
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13 Ultimate Big Data Project Ideas & Topics for Beginners [2023]
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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
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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.
- Banking and Safety:
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.
- Media and Entertainment industry
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.
- Healthcare Industry
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.
- Government and Public Services
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.
- Retail and Wholesale Industry
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.
- Manufacturing and Resources Industry
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.
- Insurance Industry
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:
- Use the right combination of hardware as well as software tools to make sure your work doesn’t get hampered later on due to the lack of the same.
- Check your data thoroughly and get rid of any duplicates.
- Follow Machine Learning approaches for better efficiency and results.
- What are the technologies you’ll need to use in Big Data Analytics Projects:
We recommend the following technologies for beginner-level big data projects:
- Open-source databases
- C++, Python
- Cloud solutions (such as Azure and AWS)
- SAS
- R (programming language)
- Tableau
- PHP and Javascript
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.
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:
- Identify four Big Data job families in the given dataset.
- Identify nine homogeneous groups of Big Data skills that are highly valued by companies.
- Characterize each Big Data job family according to the level of competence required for each Big Data skill set.
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:
- Textual metadata processing to extract a list of interest candidates from geotagged pictures.
- Geographical data clustering to identify popular tourist locations for each of the identified tourist interests.
- Representative photo identification for each tourist interest.
- Time series modelling to construct a time series data by counting the number of tourists on a monthly basis.
10. Credit Scoring
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.
Additional Topics
- Predicting effective missing data by using Multivariable Time Series on Apache Spark
- Confidentially preserving big data paradigm and detecting collaborative spam
- Predict mixed type multi-outcome by using the paradigm in healthcare application
- Use an innovative MapReduce mechanism and scale Big HDT Semantic Data Compression
- Model medical texts for Distributed Representation (Skip Gram Approach based)
Learn: Mapreduce in big data
Read our Popular Articles related to Software Development
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 .
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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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Online Programming Lessons, Tutorials and Capstone Project guide
27 Free Capstone Project Ideas and Tutorials
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Here are the lists of capstone project ideas, programming related tutorials and free source code for the month of March 2022. The compilation consists of the following:
- Conceptual Framework
- Free Source code
- Programming Tutorials
- Entity Relationship Diagram
- Database Design
- IPO Model Conceptual Framework of Pharmacy Stocks Management System
The capstone project “Pharmacy Stocks Management System” enables pharmacies to electronically manage and monitor their medicine inventories. The Pharmacy Stocks Management System will help guarantee that the pharmacy has enough medication and supplies on hand to meet the demands of the patients.
This article will walk you through the process of developing a conceptual framework for your capstone project, Pharmacy Stocks and Management System. This study’s conceptual framework was based on the input, process, and output (IPO) model.
- Quiz Master in Flutter Free Download Source code
- Ecommerce App using WooCommerce with Flutter
Items will be sold and purchased over the internet on this platform. As a result of the platform, businesses will be able to expand their retail operations and services. They have the ability to reach a large number of individuals because to the ease with which they can be purchased through the system. Researchers even created a user-friendly application that allows them and their customers to access their business no matter where they are. The platform will be simple to use and access; users will be able to store and sell as many items as they like, and purchasers will be able to acquire access by simply downloading the app; they will then be able to buy, scroll, and add to cart for some of the platform’s products.
- IPO Model Conceptual Framework of Gold Fish Guide Mobile App
You may learn everything you need to know about caring for and breeding goldfish with the Gold Fish Guide Mobile App. It includes detailed instructions on how to choose the right goldfish for your tank, how to set up and maintain your tank, as well as how to feed and care for your goldfish. The app also includes a gallery of goldfish photos as well as a list of links with more information. The Gold Fish Guide Mobile App is an excellent resource for goldfish enthusiasts on the go.
- POS, CRM, and Inventory Manager using Laravel and TailwindCSS
- IPO Model Conceptual Framework of Crowdfunding Platform
A crowdfunding platform is a website or app that lets people ask for money from other people to help them with a project or business they are working on. In exchange for small donations of money on crowdfunding sites like Kickstarter, people can get a prize or other perk for their help with the project. Crowdfunding platforms can also be used to help businesses, charities, and personal causes. They can also be used to help a wide range of projects and causes.
- Movie App in Flutter Free Source Code
This study is all about how the Movie app was made with the flutter framework. The app doesn’t let people search for movies and watch them online or offline. If the users want to watch the movie on their own time, they need to download the app. Movie fans and people who go to the movies are the target audience for the app. Download link on the project is in the article.
- IPO Model Conceptual Framework of Virtual Online Tour Application
This article talked about how the conceptual framework for the investigation was made. The input, process, and output model was used by the researchers to help them think about their study (IPO model). The researchers will start the project by figuring out what the problem is and what the system’s users need. In order to help with the project, the researchers will look for literature that is relevant to their work. After getting all of the project’s inputs, the researchers will start the project’s development process. A SDLC model that is right for the project will be chosen. When all of the steps are done, the project will come to life and be used in the real world. A new project is started, and it will be kept up for the long run of the project.
- Social Media App in Flutter and Firebird Free Source Code
“Social Media App in Flutter and Firebird Free Source Code” is the goal of this research. It’s going to help make that idea a reality. This study doesn’t have any specific goals for the people who will use it. As long as they know how to use the platform and have the skills to do it, anyone can use it. If you’re an end user, you can do anything you want with the platform. You can post, share, like, and do business on it. It’s made for you.
- Waste Management with Reward System ER Diagram
To keep a clean and safe environment, waste management is very important, so it’s important to do it. People will be rewarded for their efforts, so introducing a waste management system with a reward system will help improve waste management in general. This will make people want to be more environmentally friendly and recycle more. Those who work hard will be rewarded, and the environment will be cleaner and safer as a bonus. In order to improve waste management, a waste management with reward system is very important and should be used.
- IPO Model Conceptual Framework of Virtual and Remote Guidance Counselling System
- Vaccine Distribution System ER Diagram
The technology that has been proposed will assist people in registering for vaccinations and scheduling their immunization appointments more efficiently. In addition, the system will electronically handle information regarding medical front-line employees, patients, vaccination clinics, and vaccines. In order to complete the immunization process, patients and medical front-line personnel will have fast and convenient access to the information they require.
- Invoicing App in Figma and Flutter
This investigation focuses on the process of developing an invoicing application in Figma and Flutter. Currently, the application is solely limited to the electronic recording and storing of invoice data, which will allow firms to keep track of their sales and financial transactions. An invoicing application that is simple to use, safe, dependable, and convenient will be developed by the researchers. The researchers will compile a representative sample of business company employees, finance department employees, and their clients for analysis.
- IPO Model Conceptual Framework of Mobile Based Mangrove Species Field Guide
This post will teach you how to develop a conceptual framework for the capstone project titled MobileMangrove: Mobile Based Mangrove Species Field Guide, which is a mobile-based mangrove species field guide. For the purpose of laying the conceptual groundwork for this inquiry, the input, process, and output (IPO) model was employed.
- IPO Model Conceptual Framework of AdviseMobile A Web and Mobile Based Guidance Consultation System
Using the guidance consultation system, which is accessible via the internet and mobile devices, users can request assistance and advice on a wide range of topics. It is comprised of two components: a web portal and a mobile application for smartphones and tablets. A variety of topics can be explored on the web portal, while the mobile application allows users to receive personalized guidance and assistance while on the go.
- Modern E-commerce Website in Reactjs and Redux
- BMI Calculator in Flutter Based on Neumorphic
The program will be user-friendly and may be used by a wide spectrum of people who want to keep track of their body mass index (BMI) and live a healthy lifestyle. There is a download link for the project, and consumers can choose to have it installed on their mobile phones. Following the entry of their weight and height, their BMI will be calculated in a matter of clicks, and the resulting health range will be displayed immediately following the computation. In addition, the application will recommend health-related ideas and activities based on your health range. Calculations of body mass index (BMI) will be quick, simple, and efficient, and they will be constantly accessible and available.
- Hospital Management System in Laravel 8 Free Source code
The Hospital Management System designed in Laravel 8 has received high praise from the researchers that conducted the study. Due of the efficiency and dependability it is likely to deliver to the intended audience, it is strongly recommended. It will be possible to make hospital records and information ready and accessible at the touch of a button across all departments thanks to the technology. The adoption of the system that has been developed would improve the services and transaction modes that are offered by the hospital. It is anticipated that the aforementioned project will eventually replace the current manual operation technique. The project’s primary purpose is to make it possible to conduct transactions without the use of paper. It also seeks to deliver low-cost, dependable automation of existing systems, among other things. High-level data protection is provided at all levels of user-system interaction, and the system also includes a variety of storage and backup options that are both resilient and reliable.
- Restaurant Food Delivery System Free Database Design Tutorial
The Restaurant Meal Delivery System is a web-based tool that helps restaurants to manage all of their food delivery orders in one convenient location. This software helps restaurants to manage their deliveries in a centralized location, including tracking delivery times, controlling delivery routes, and tracking customer feedback. It is built on top of the Laravel framework, which is also utilized by the Restaurant Food Delivery System application to provide food delivery. Along with a single-page user interface, the application was constructed using the MVC paradigm and makes considerable use of the jQuery client-side library, which was developed by the author.
- Boarding House Management System ER Diagram
A significant deal of information and assistance will be provided by the ER Diagram design for the Boarding House Management System throughout the subsequent stage of the project, which will be the actual database design.
In addition, we will provide you with a PowerPoint or Video Presentation that contains the whole ER Diagram in detail. To view the videos, be sure to visit and subscribe to our YouTube channel.
- IPO Model Conceptual Framework of Digital Wallet Solution
People used to pay for products and services with traditional cash-based transactions before the invention of electronic payment systems. All financial transactions must be completed by a human in the traditional fashion, which necessitates the use of physical cash for all transactions. It is also necessary to manually record payments and other financial transactions for future reference, which is prone to errors and can become misplaced or difficult to retrieve. The manual technique is considered ineffective since it necessitates a significant investment of time and effort, and it leaves a significant margin for mistake. Given that manual payment and other financial operations entail the transfer of money, consumers want a dependable and secure platform to keep funds and speed financial transactions.
- IPO Model Conceptual Framework of Transcribe Medical – Medical Speech to Text Converter
A capstone project, Transcribe Medical – Medical Speech to Text Converter, will be demonstrated in this post, which will show you how to construct a conceptual foundation for it. Developing the conceptual foundation for the inquiry was accomplished by usage of the input, process, and output (IPO) model.
- IPO Model Conceptual Framework of Multi-branch Travel Agency and Booking System PHP and Bootstrap Script
Prior to the emergence of the COVID-19 pandemic, travel agencies conducted their operations and transacted with their clients within the confines of their own offices. Clients would come into the office to inquire about and make reservations, which was time-consuming and required them to put in a great deal of effort on their part. However, as soon as a pandemic strikes, various physical interaction limits are implemented in order to prevent the transmission of infectious diseases among people. The pandemic has posed a significant threat to the tourism industry and firms that fall into this category, such as travel and reservation services. The epidemic has hampered their business operations, causing them to incur financial losses as a result of the inability of the company to operate in the same manner as before. At the moment, tourism is slowly but steadily resuming operations under the authority granted by the government. They are now permitted to operate, but they must continue to comply to the health guidelines in order to ensure the safety of their clientele. Travel companies are currently on the lookout for cutting-edge technology that will enable them to function more efficiently and provide their customers with a safe and efficient service.
- EDelivery Platform in PHP and MySQL with Source code
It is the responsibility of a courier firm to manage the administration and delivery of cargo or shipments to their final destination. In addition to a variety of responsibilities, the organization is in charge of calculating shipping charges, selecting delivery parameters, such as who will receive the products and where they will be delivered, and processing payments until the transaction is completed. Manipulating shipping operations by hand takes time and is prone to errors due to human error. Businesses are looking for superior solutions to automate all activities in order to expedite transportation while keeping costs low and operating costs low.
- C# IF Statement Video Tutorial and Source code
The article is an example of source code for the if statement in C#. The article contains also a video tutorial that will go over the source code line by line so that you can better grasp how the if statement operates and functions.
- IPO Model Conceptual Framework of Car Parking System
The capstone project, dubbed “Car Parking System,” is intended to modernize the system that is currently in use to manage car parking spaces on campus. By utilizing the system, the parking lot administrator will be able to encode and retain records of available parking spots, parking fees, parking time, and customer and vehicle information in real time. In order to efficiently manage automobile parking spaces, records must be maintained in order to avoid errors and hassles for both the parking administrator and the consumers.
- C# IF ELSE Statement Video Tutorial and Source code
To facilitate the flow of a C# program based on particular logical circumstances, many decision-making statements are available in C#. You’ll learn how to utilize if, else, and nested if else statements to govern the flow of data based on the circumstances that exist.
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Capstone Projects
M.S. in Data Science students are required to complete a capstone project. Capstone projects challenge students to acquire and analyze data to solve real-world problems. Project teams consist of two to four students and a faculty advisor. Teams select their capstone project at the beginning of the year and work on the project over the course of two semesters.
Most projects are sponsored by an organization—academic, commercial, non-profit, and government—seeking valuable recommendations to address strategic and operational issues. Depending on the needs of the sponsor, teams may develop web-based applications that can support ongoing decision-making. The capstone project concludes with a paper and presentation.
Key takeaways:
- Synthesizing the concepts you have learned throughout the program in various courses (this requires that the question posed by the project be complex enough to require the application of appropriate analytical approaches learned in the program and that the available data be of sufficient size to qualify as ‘big’)
- Experience working with ‘raw’ data exposing you to the data pipeline process you are likely to encounter in the ‘real world’
- Demonstrating oral and written communication skills through a formal paper and presentation of project outcomes
- Acquisition of team building skills on a long-term, complex, data science project
Capstone projects have been sponsors by a variety of organizations and industries, including: Capital One, City of Charlottesville, Deloitte Consulting LLP, Metropolitan Museum of Art, MITRE Corporation, a multinational banking firm, The Public Library of Science, S&P Global Market Intelligence, UVA Brain Institute, UVA Center for Diabetes Technology, UVA Health System, U.S. Army Research Laboratory, Virginia Department of Health, Virginia Department of Motor Vehicles, Virginia Office of the Governor, Wikipedia, and more.
Sponsor a Capstone Project
View previous examples of capstone projects and check out answers to frequently asked questions.
What does the process look like?
- The School of Data Science periodically puts out a Call for Proposals . Prospective project sponsors submit official proposals, vetted by the SDS Associate Director for Research Development .
- Sponsors present their projects to students at “Pitch Day” near the start of the Fall term, where students have the opportunity to ask questions.
- Students individually rank their top project choices. An algorithm sorts students into capstone groups of approximately 3 to 4 students per group.
- Each group is assigned a faculty mentor, who will meet groups each week in a seminar-style format.
What is the seminar approach to mentoring capstones?
We utilize a seminar approach to managing capstones to provide faculty mentorship and streamlined logistics. This approach involves one mentor supervising three to four loosely related projects and meeting with these groups on a regular basis. Project teams often encounter similar roadblocks and issues so meeting together to share information and report on progress toward key milestones is highly beneficial.
Do all capstone projects have sponsors?
Not necessarily. Generally, each group works with a sponsor from outside the School of Data Science. Some sponsors are corporations, some are from nonprofit and governmental organizations, and some are from in other departments at UVA.
Why do we have to work in groups?
Because data science is a team sport!
All capstone projects are completed by group work. While this requires additional coordination , this collaborative component of the program reflects the way companies expect their employees to work. Building this skill is one of our core learning objectives for the program.
I didn’t get my first choice of capstone project from the algorithm matching. What can I do?
Remember that the point of the capstone projects isn’t the subject matter; it’s the data science. Professional data scientists may find themselves in positions in which they work on topics assigned to them, but they use methods they enjoy and still learn much through the process. That said, there are many ways to tackle a subject, and we are more than happy to work with you to find an approach to the work that most aligns with your interests.
Can I work on a project for my current employer?
Each spring, we put forward a public call for capstone projects. You are encouraged to share this call widely with your community, including your employer, non-profit organizations, or any entity that might have a big data problem that we can help solve. As a reminder, capstone projects are group projects so the project would require sufficient student interest after ‘pitch day’. In addition, you (the student) cannot serve as the project sponsor (someone else within your employer organization must serve in that capacity).
If my project doesn’t have a corporate sponsor, am I losing out on a career opportunity?
The capstone project will provide you with the opportunity to do relevant, high-quality work which can be included on a resume and discussed during job interviews. The project paper and your code on Github will provide more career opportunities than the sponsor of the project. Although it does happen from time to time, it is rare that capstones lead to a direct job offer with the capstone sponsor's company. Capstone projects are just one networking opportunity available to you in the program.
Capstone Project Reflections From Alumni
“Capstone projects are opportunities for you to deliver valuable, quantifiable results that you can use as a testimony of your long-term project success to the company you work for and other companies in future interviews.” — Gabriel Rushin, MSDS 2017, Procter & Gamble, Senior Machine Learning Engineer Manager
“For my capstone project, I worked to develop a clustering model to assess biogeographic ancestry, using DNA profiles. I felt like I was finally doing real-world data science and loved working with such an important organization as the Department of Defense.” — Colleen Callahan, Online MSDS 2021, Associate Research Analyst, CNA (Arlington, Virginia)
Capstone Project Reflections From Sponsors
“For us, the level of expertise, and special expertise, of the capstone students gives us ‘extra legs’ and an extra push to move a project forward. The team was asked to provide a replicable prototype air quality sensor that connected to the Cville Things Network, a free and community supported IoT network in Charlottesville. Their final product was a fantastic example that included clear circuit diagrams for replication by citizen scientists.” — Lucas Ames, Founder, Smart Cville
“Working with students on an exploratory project allowed us to focus on the data part of the problem rather than the business part, while testing with little risk. If our hypothesis falls flat, we gain valuable information; if it is validated or exceeded, we gain valuable information and are a few steps closer to a new product offering than when we started.” — Ellen Loeshelle, Senior Director of Product Management, Clarabridge
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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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105 Original Capstone Project Ideas for STEM Students
What is a Capstone Project? A capstone project refers to a final or culminating project high school or college seniors need to earn their degrees. It’s usually a project that takes several months to complete and should demonstrate students’ command over particular subjects within an area of study. It may be similar to master’s thesis writing. There are endless capstone project ideas to choose from, but sometimes students struggle to come up with research topic ideas, so we’ve explored several fresh capstone project topics for consideration.
Business Capstone Project Ideas
Nursing capstone project ideas, ideas for high school, computer science capstone project ideas, cybersecurity capstone project ideas, it project ideas, capstone project ideas for nursing, senior capstone project ideas, high school senior project ideas, capstone project ideas for information technology, more information technology ideas, data science capstone project ideas, creative project ideas, interesting science topics, mba capstone project ideas.
- How important are small businesses and startups to the United States’ economy?
- Is diversity in the workplace an important quality of how successful a business is?
- Is a free market truly achievable or this is just an outdated utopian idea from the past?
- How difficult is it for entrepreneurs to gain funding support to open up a business?
- How are advances in crisis management changing the ways that businesses find success?
- Is it important to have a social media presence when starting a new small business?
- What business or industries do the best during times of extended international conflict?
- What are the healthiest diets and how do nurses help promote them for in-patients?
- What are some of the psychological conditions affecting healing in patients with cancer?
- What are the most effective nursing techniques for dealing with cancer patients?
- Should nurses take a more proactive role in investigating instances of patient abuse?
- Should nurses be required to learn how to use technological tools for better care?
- How do nurses manage anxiety and fear in their patients who are dealing with illness?
- Should nurses take a greater role in providing recommendations for patients in care?
- Should physical education courses be a mandatory subject throughout high school?
- How effective are standardized tests in determining students’ skill level and knowledge?
- What is the evidence suggesting that video game violence is connected to real violence?
- Are mobile phones tools that should be allowed in classes to enhance the school experience?
- What is the most effective way of dealing with bullies at school? What is the evidence?
- Should students earning good grades receive monetary incentives or other rewards?
- Will the legalization of sports betting help raise more money for public schools?
- Are SCRUM methodologies still an effective way of dealing the product development?
- Is software engineering still a sought-after technical skill or is the subject outdated?
- In what ways are search algorithms being advanced to help the use of data mining?
- What are the most versatile programming languages in the field of computer science?
- How has computer science helped further the study of biomedicine and biology?
- What kind of impact has computer science and engineering had on human learning?
- Will computer science play a role in developing food science to end hunger?
- How has encryption and decryption technology changed in the last two decades?
- Is bank security at risk from international hackers or has security up-to-date?
- How is the internet affecting the way our private information is communicated?
- Should governments have the right to monitor citizens’ electronic activities?
- Does a federal judge need to issue warrants before people’s tech activities are checked?
- Does open source software put users at risk of having their information stolen?
- How safe are mobile phones in keeping our information safe from hackers?
- How important is it for companies to test their software updates for quality assurance?
- What are some of the more serious challenges government agencies experience daily?
- How important is the user of CMS technology in e-commerce for small businesses?
- Are our IT skills still relevant in a world where AI is increasingly becoming more cost-effective?
- In what ways is information technology important for improving standardized testing?
- What are the most important economic models in current use in developing IT?
- What benefits do human-computer interfaces systems have for today’s small businesses?
- What are the best critical care methods currently in practice in medical emergencies?
- What effects has the growing shortage of qualified nurses had on the United States?
- Are the growing cost of nursing school and training leading to a shortage of professionals?
- How important is point-of-care testing and why are health care facilities ending programs?
- Are nurses appropriately trained to deal with patients that suffer from breathing issues?
- What are the skills needed for nurses to work in high-stress stations such as the ER or trauma?
- How important is patient communication when it comes to proper diagnoses of illnesses?
- Which is the United States’ favorite sports pastime and how has this changed over time?
- Do you believe that students who participate in hazing should be punished for negligence?
- How important is it for schools to prevent hazing rituals conducted by their students?
- What evidence is there in support of alien life? Do governments know of alien life?
- Is damage to religious property considered a hate crime despite the actual intention?
- How influential is the United States’ political system towards its international allies?
- In what ways did the Cold War affect the U.S.’s international relationships with allies?
- How effective will revenue generation from legalized gambling be for the economy?
- Is it possible for gamblers to use tech to gain advantages over hotel sportsbooks?
- Is it important for major coffee companies to be socially and environmentally responsible?
- Why is it so important to protect victims’ rights in instances of domestic violence?
- Do you believe it is ethical for people to clone their beloved pets so they live on?
- Should communities be responsible for ensuring students are adequately fed at school?
- What kind of animal makes for a better childhood pet? Dogs, cats, or something else?
- What are some of the benefits and negatives of living in a tech-driven modern society?
- How does your experience in dealing with people affect the way you deal with tech?
- What is the most important information technology advancement to affect the world?
- Do you think the internet needs better censorship of certain negative material?
- Are children better off today because of the access to IT in comparison to prior gens?
- Do you believe that China will be the world’s technological leader in the next decade?
- How has technology changed the countries engage in modern warfare and conflict?
- How important is it to further develop mobile technologies for social media use?
- Is social media becoming obsolete and in what ways are consumers using the tech?
- Does web-based training improve one’s ability to learn new skills at a fraction of the cost?
- Should internet providers take better care of keeping consumers’ privacy secure?
- How important is it to monitor how social media uses consumers’ browsing histories?
- In what ways does IT play a role in how engineers develop transportation routes?
- How has IT changed the way companies conduct their business around the world?
- How are gun laws being affected by the kind of information provided by data science?
- Gathering information for disease control has changed how in the last 20 years?
- In what ways is the information gathered from big data a company’s biggest asset?
- How did Trump benefit from the use of data science leading up to the election?
- How effective are sports franchises in making decisions based on big data science?
- Is it possible to avoid over-saturation of information in the age of data science?
- How is big data working to make artificial intelligence in business a real possibility?
- How are infographics affecting the way people consume information in today’s world?
- Is it possible for another major election to be tampered with by foreign governments?
- Are people becoming less educated as a result of the amount of information consumed?
- Will video games play a role in removing soldiers from harmful front-line combat zones?
- Do you think public colleges and universities should move towards faith-based teaching?
- Is it still sufficient to have a college-level education to succeed in today’s economy?
- Should the United States invest in and provide longer paid leave for new parents?
- Does economics or science play a bigger role in Europe’s decision to ban modified crops?
- What are the most optimal diets safe for human consumption in the long term?
- Is it possible to incorporate physical exercise as a way to modify DNA coding in humans?
- Do you believe that personal medication that is designed specifically for genomes is possible?
- Is it scientifically ethical to alter the DNA of a fetus for reasons related to genetic preference?
- Is science an effective discipline in the way people are being tried for violent crimes?
- How effective is stem cell science and its use in treatments for diseases such as cancer?
- How important is business diplomacy in successful negotiations for small companies?
- What role does a positive and healthy workplace have in retaining high-quality staff?
- What sort of challenges does small business face that large corporations don’t experience?
- Should workplace diversity rules and standards be regulated by state or federal law?
- How important is it to be competitive in advertising to open a small business?
- Are large corporations making the right kinds of innovative investments to stay relevant?
- How important is the word of mouth marketing in today’s age of digital communications?
The above capstone project ideas are available to use or modify at no cost. For even more capstone project topics or to get capstone project examples, contact a professional writing service for affordable assistance. A reliable service can help you understand what is a capstone project even more so by providing clear instructions on the capstone project meaning as well as the most common requirements you can expect from today’s academic institutions.
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This course is part of the Big Data Specialization
Big Data - Capstone Project
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
Instructors.
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.
- 5 stars 66.06%
- 4 stars 21.85%
- 3 stars 5.91%
- 2 stars 1.79%
- 1 star 4.37%
TOP REVIEWS FROM BIG DATA - CAPSTONE PROJECT
waoh.. it's incredible.. .. I strongly recommend this Capstone Project. Be sure to put on frank effort.
Good exercise to cover the whole essence of many weeks of other courses of the Big Data Specialization.
All the sessions were very informative and provided the required knowledge from basics.
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.
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.
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20+ Data Engineering Projects for Beginners with Source Code
Explore top 20 real-world data engineering projects ideas for beginners with source code to gain hands-on experience on diverse data engineering skills. Last Updated: 17 Mar 2023
Most of us have observed that data scientist is usually labeled the hottest job of the 21st century, but is it the only most desirable job? For beginners or peeps who are utterly new to the data industry, Data Scientist is likely to be the first job title they come across, and the perks of being one usually make them go crazy. Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role. This role is gradually picking up the pace of popularity and is on the verge of beating Data Scientist as the sexiest job of the 21st century.
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According to a Dice Tech Job Report - 2020 , it’s happening, i.e., the demand for Data Engineering roles is boosting up. According to this report, the Data Engineering Job postings grew by 50% yearly. Google trends echos the exact fact the demand for Data Engineering roles is booming if we look at the statistics for the past 5years. In this graph, there is a dip in the growth between March and April, it could be because of the COVID-19 outbreak, but even the crisis couldn't stop the growth. The trends graph also shows that the demand for the so-called sexiest data scientist job is lower than that of data engineer jobs.
A candidate's evaluation for data engineering jobs begins from the resume. Most recruiters look for real-world project experience and shortlist the resumes based on hands-on experience working on data engineering projects . But before you send out your resume for any data engineer job, and if you want to get shortlisted for further rounds, you need to have ample knowledge of various data engineering technologies and methods.
Don't worry; ProjectPro industry experts are here to help you with a list of data engineering project ideas. :)
But before you start data engineering project ideas list, read the next section to know what your checklist for prepping for data engineering role should look like and why.
Table of Contents
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There are a few data-related skills that most data engineering practitioners must possess. And if you are aspiring to become a data engineer, you must focus on these skills and practice at least one project around each of them to stand out from other candidates.
Explore different types of Data Formats: A data engineer works with various dataset formats like .csv, .josn, .xlx, etc. They are also often expected to prepare their dataset by web scraping with the help of various APIs. Thus, as a learner, your goal should be to work on projects that help you explore structured and unstructured data in different formats.
Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis. So, working on a data warehousing project that helps you understand the building blocks of a data warehouse is likely to bring you more clarity and enhance your productivity as a data engineer.
Data Analytics: A data engineer works with different teams who will leverage that data for business solutions. To understand their requirements, it is critical to possess a few basic data analytics skills to summarize the data better. So, add a few beginner-level data analytics projects to your resume to highlight your Exploratory Data Analysis skills.
Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines.
Big Data Tools: Without learning about popular big data tools, it is almost impossible to complete any task in data engineering. Thus, we suggest you explore as many big data tools as possible by working on multiple data engineering projects.
If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of best data engineering project examples below.
We recommend over 20 top data engineering project ideas with an easily understandable architectural workflow covering most industry-required data engineer skills.
If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below.
Smart IoT Infrastructure
Aviation Data Analysis
Shipping and Distribution Demand Forecasting
Event Data Analysis
Data Ingestion
Data Visualization
Data Aggregation
- Building a web-based Surfline Dashboard
Let us discuss them in detail.
Build a Job Winning Data Engineer Portfolio with Solved End-to-End Big Data Projects
1) Smart IoT Infrastructure
In this IoT project , you will be discussing a general architecture for building smart IoT infrastructure. With the trending advance of IoT in every facet of life, technology has enabled us to handle a large amount of data ingested with high velocity. This big data project discusses IoT architecture with a sample use case.
This is a fictitious pipeline network system called SmartPipeNet, a network of sensors with a back-office control system that can monitor pipeline flow and react to events along various branches to give production feedback, detect and reactively reduce loss, and avoid accidents.
This architecture shows that simulated sensor data is ingested from MQTT to Kafka. The data in Kafka is analyzed with Spark Streaming API, and the data is stored in a column store called HBase. Finally, the data is published and visualized on a Java-based custom Dashboard.
Source Code: Smart IoT Infrastructure Data Engineering Project with Source Code
2) Aviation Data Analysis
Aviation Data can segment passengers, observe their behavioral patterns, and reach out to them with relevant, targeted promotions. This helps improve customer service, enhance customer loyalty, and generate new revenue streams for the airline.
In this use case, you will learn how to get streaming data from an API, cleanse data, transform it to get insights, and visualize the data in a dashboard.
The primary step in this data project is to gather streaming data from Airline API using NiFi and batch data using AWS redshift using Sqoop .
The next step is to build a data engineering pipeline to analyze the data using Apache Hive and Druid.
You will then compare the performances to discuss hive optimization techniques and visualize the data using AWS Quicksight.
Source Code: Aviation Data Analysis using Big Data Tools
3) Shipping and Distribution Demand Forecasting
This one of the best data engineering projects for beginners and it uses historical demand data to forecast demand for the future across various customers, products, and destinations. A real-world use case for this data engineering project is when a logistics company wants to predict the quantities of the products customers want to be delivered at various locations in the future. The company can use demand forecasts as input to an allocation tool. The allocation tool can then optimize operations, such as delivery vehicle routing and planning capacity in the longer term. A related example is when a vendor or insurer wants to know the number of products that will be returned because of failures.
This data engineering project uses the following big data stack -
Azure Structured Query Language ( SQL ) Database instance for persistent storage; to store forecasts and historical distribution data.
Machine Learning web service to host forecasting code.
Blob Storage for intermediate storage of generated predictions.
Data Factory to orchestrate regular runs of the Azure Machine Learning model.
Power BI dashboard to display and drill down the predictions.
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4) Event Data Analysis
NYC Open Data is free public data published by New York City agencies and partners. This project is an opportunity for data enthusiasts to engage in the information produced and used by the New York City government. You will analyze accidents happening in NYC. This is an end-to-end big data project for building a data engineering pipeline involving data extraction, data cleansing, data transformation, exploratory analysis , data visualization, data modeling, and data flow orchestration of event data on the cloud.
In this big data project , you will explore various data engineering processes to extract real- time streaming event data from the NYC city accidents dataset, processing the data on AWS to extract KPIs that will eventually be pushed to Elasticsearch for text-based search and analysis using Kibana visualization.
Source Code: Event Data Analysis using AWS ELK Stack
5) Data Ingestion
This project involves data ingestion and processing pipeline with real-time streaming and batch loads on the Google cloud platform (GCP). The Yelp dataset, which is used for academic and research purposes, is processed here.
Create a service account on GCP and download Google Cloud SDK(Software developer kit). Then, Python software and all other dependencies are downloaded and connected to the GCP account for other processes. Then, the Yelp dataset downloaded in JSON format is connected to Cloud SDK, following connections to Cloud storage which is then connected with Cloud Composer. The Yelp dataset JSON stream is published to the PubSub topic. Cloud composer and PubSub outputs are Apache Beam and connected to Google Dataflow. Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization .
Source Code: Data Ingestion with SQL using Google Cloud Dataflow
6) Data Visualization
A data engineer is occasionally asked to perform data analysis; it will thus be beneficial if they understand how data needs to be visualized for smooth analytics. This is because often, data analysts create an automated dashboard, the backbone of which relies primarily on the kind of data the team of data engineers provides.
Project Idea: We’ll explore the usage of Apache Airflow for managing workflows. Learn how to process Wikipedia archives using Hadoop and identify the lived pages in a day. Utilize Amazon S3 for storing data, Hive for data preprocessing, and Zeppelin notebooks for displaying trends and analysis . Understand the importance of Qubole in powering up Hadoop and Notebooks.
Source Code: Visualize Daily Wikipedia Trends with Hive, Zeppelin, and Airflow (projectpro.io)
7) Data Aggregation
Data Aggregation refers to collecting data from multiple sources and drawing insightful conclusions from it. It involves implementing mathematical operations like sum, count, average, etc. to accumulate data over a given period for better analysis. There are many more aspects to it and one can learn them better if they work on a sample data aggregation project.
Project Idea: Explore what is real-time data processing, the architecture of a big data project, and data flow by working on a sample of big data. Learn how to use various big data tools like Kafka, Zookeeper, Spark, HBase, and Hadoop for real-time data aggregation. Also, explore other alternatives like Apache Hadoop and Spark RDD.
Source Code: Real-time data collection & aggregation using Spark Streaming
8) Building a web-based Surfline Dashboard
In this project, a web-based dashboard will be built for surfers that provides real-time information about surf conditions for popular surfing locations around the world. The aim is to a build a data pipeline that collects surf data from the Surfline API, processes it, and stores it in a Postgres data warehouse.
The first step in the pipeline is to collect surf data from the Surfline API. The data is then exported to a CSV file and uploaded to an AWS S3 bucket. S3 is an object storage service provided by AWS that allows data to be stored and retrieved from anywhere on the web. The most recent CSV file in the S3 bucket is then downloaded and ingested into the Postgres data warehouse. Postgres is an open-source relational database management system that is used to store and manage structured data. Airflow is used for orchestration in this pipeline. And Plotly is used to visualize the surf data stored in the Postgres database.
Source Code: Surfline Dashboard u/andrem8 on GitHub
Ace your Big Data engineer interview by working on unique end-to-end solved Big Data Projects using Hadoop
Here are the data engineering project ideas that you can explore and add to your portfolio to showcase practical experience with data engineering problems.
Log Analytics Project
COVID-19 Data Analysis
Movielens Data Analysis for Recommendations
Retail Analytics Project Example
Let us now discuss them in detail.
9) Log Analytics Project
Logs help understand the criticality of any security breach and help discover any operational trends and establish a baseline, along with forensic and audit analysis.
In this project, you will apply your data engineering and analysis skills to acquire server log data, preprocess the data, and store it into reliable distributed storage HDFS using the dataflow management framework Apache NiFi. This data engineering project involves cleaning and transforming data using Apache Spark to glean insights into what activities are happening on the server, such as the most frequent hosts hitting the server and which country or city causes the most network traffic with the server. You will then visualize these events using the Plotly-Dash to tell a story about the activities occurring on the server and if there is anything your team should be cautious about.
The current architecture is called Lambda architecture, where you can handle both real-time streaming data and batch data. Log files are pushed to Kafka topic using NiFi, and this Data is Analyzed and stored in Cassandra DB for real-time analytics. This is called Hot Path. The extracted data from Kafka is also stored in the HDFS path, which will be analyzed and visualized later, called the cold path in this architecture.
Source Code: Log Analytics Project with Spark Streaming and Kafka
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10) COVID-19 Data Analysis
This is an exciting portfolio project example where you will learn how to preprocess and merge datasets to prepare them for the Live COVID19 API dataset analysis. After preprocessing, cleansing, and data transformation, you will visualize data in various Dashboards.
Country-wise new recovered
Country-wise new confirmed cases
Covid-19 data will be pushed to Kafka topic and HDFS using NiFi. The data will be processed and analyzed in the PySpark cluster and ingested to the Hive database. This data will be finally published as data Plots using Visualization tools like Tableau and Quicksight.
Source Code: Real-World Data Engineering Project on COVID-19 Data
11) Movielens Data Analysis for Recommendations
Recommender System is a system that seeks to predict or filter preferences according to the user's choices. Recommender systems are utilized in various areas, including movies, music, news, books , research articles, search queries, social tags, and products in general. This use case focuses on the movie recommendation system used by top streaming services like Netflix, Amazon Prime, Hulu, Hotstar, etc, to recommend movies to their users based on historical viewing patterns. Before the final recommendation is made, a complex data pipeline brings data from many sources to the recommendation engine.
In this project, you will explore the usage of Databricks Spark on Azure with Spark SQL and build this data pipeline.
Download the dataset from GroupLens Research, a research group in the Department of Computer Science and Engineering at the University of Minnesota.
Upload it to Azure Data lake storage manually.
Create a Data Factory pipeline to ingest files. Then you use databricks to analyze the dataset for user recommendation.
This is a straightforward data engineering pipeline architecture, but it allows exploring data engineering concepts and confidence to work on a cloud platform.
Source Code: Analyse Movie Ratings Data
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12) Retail Analytics Project Example
For retail stores , inventory levels, supply chain movement, customer demand, sales, etc. directly impact the marketing and procurement decisions. They rely on Data Scientists who use machine learning and deep learning algorithms on their datasets to improve such decisions, and data scientists have to count on Big Data Tools when the dataset is huge. So, if you’re interested in understanding the retail stores’ analytics and their decision-making process, check out this project.
Project Objective: Analysing dataset of a retail store to support its growth by enhancing its decision-making process.
Learnings from the Project: You will get an idea of working on real-world data engineering projects through this project. You will use AWS EC2 instance and docker-composer for this project. You will learn about HDFS and the significance of different HDFS commands. You will be guided on using Sqoop Jobs and performing various transformation tasks in Hive. You will set up MySQL for table creation and migrate data from RDBMS to Hive warehouse to arrive at the solution.
Tech Stack: Language: SQL, Bash
Services: AWS EC2, Docker, MySQL, Sqoop, Hive, HDFS
Engineering Projects on GitHub
13) realtime data analytics .
A Cab service company called Olber is collecting data about each cab trip. Per trip, two different devices generate additional data. The Cab meter sends information about each trip's duration, distance, and pick-up and drop-off locations. A mobile application accepts payments from customers and sends consistent and acessible data about fares. The Cab company wants to calculate the average tip per KM driven, in real-time, for each area to spot passenger trends.
This architecture diagram demonstrates an end-to-end stream processing pipeline. This type of pipeline has four stages: extract, transform, load, and report. In this reference architecture, the pipeline extracts data from two sources, performs a join on related records from each stream, enriches the result, and calculates an average. The results are stored for further analysis.
GitHub Repository Link - Olber Cab Service Realtime Data Analytics
14) Realtime Data Analytics with Azure Stream Services
This project aims to calculate the average trip per KM driven, in real-time, for each area to spot passenger trends for ride-hailing data.
This architecture diagram demonstrates an end-to-end stream processing pipeline. This type of pipeline has four stages: extract, transform, load, and report. In this reference architecture, the pipeline extracts data from two sources, performs a join on related records from each stream, enriches the result, and calculates an average. The results are stored for further analysis of the cab service data.
GitHub Repository Link: Stream Processing with Azure Databricks
15) Real-time Financial Market Data Pipeline with Finnhub API and Kafka
This project aims to build a streaming data pipeline using Finnhub's real-time financial market data API. The architecture of this project primarily comprises five layers- Data Ingestion, Message broker, Stream processing, Serving database, and Visualization. The end result is a dashboard that displays data in a graphical format for deep analysis.
The pipeline includes several components, including a producer that fetches data from Finnhub's API and sends it to a Kafka topic, a Kafka cluster for storing and processing the data. For stream processing, Apache Spark will be used. Next, Cassandra is used for storing the pipeline's financial market data that is streamed in real-time. Grafana is used to create the final dashboard that displays real-time charts and graphs based on the data in the database, allowing users to monitor the market data in real time and identify trends and patterns.
GitHub Repository: Finnhub Streaming Data Pipeline by RSKriegs
16) Real-time Music Application Data Processing Pipeline
In this project, you will use the data of Streamify, a fake platform for users to discover, listen and share music online. The aim is to build a data pipeline that intakes real-time data and stores it in data lake every two minutes.
With the help of Eventism and Million Songs dataset, you can create a sample dataset for this project. Apache Kafka and Apache Spark are the streaming platforms used for real-time data processing. Spark's Structured Streaming API allows for data to be processed real-time in mini-batches, providing low-latency processing capabilities. The processed data is stored in Google Cloud Storage and is transformed with the help of dbt. Using dbt, we can clean, transform, and aggregate the data to make it suitable for analysis. The data is then pushed to the data warehouse- BigQuery, and finally, the data is visualized using Data Studio. For orchestration, Apache AirFlow has been used, and for containerization, Docker is the preferred choice.
GitHub Repository: A data engineering project with Kafka, Spark Streaming, dbt, Docker, Airflow, Terraform, GCP by Ankur
The titles of the below-mentioned data engineering project examples are self-explanatory. Thus if you add them to your resume after working on them, your chances of getting an interview call will likely increase.
Live Twitter Sentiment Analysis
Website Monitoring
Bitcoin Mining
How to deal with slowly changing dimensions?
GCP Project to Explore Cloud Functions
Explore Categories
17) Live Twitter Sentiment Analysis
When it comes to influencing purchase decisions or finding people’s sentiment toward a political party, people’s opinion is often more important than traditional media. It means that there is a significant opportunity for brands on Twitter. The Twitter sentiment is a term used to define the analysis of sentiments in the tweets posted by the users. Generally, Twitter sentiment is analyzed in most big data projects using parsing. Analyzing users’ sentiments on Twitter is fruitful to companies for product that is mostly focused on social media trends, users' sentiments, and future views of the online community.
The data pipeline for this data engineering project has five stages - data ingestion, NiFi GetTwitter processor that gets real-time tweets from Twitter and ingests them into a messaging queue. Collection happens in the Kafka topic. The real-time data will be processed using Spark structured streaming API and analyzed using Spark MLib to get the sentiment of every tweet. MongoDB stores the processed and aggregated results. These results are then visualized in interactive dashboards using Python's Plotly and Dash libraries.
Source Code: Live Twitter Sentiment Analysis with Spark
18) Website Monitoring
Website Monitoring is used to describe any activity which involves testing a website or any web service for its availability, performance, or function. It’s the process of testing and also verifying that the end-users can interact with the website or the web application as expected. The Website Monitoring service checks and verifies that the website is up and working as expected, and website visitors can use the site without facing any difficulties.
In this AWS project , Amazon EC2 acts as a website backend generating server logs; Kinesis Datastreams read the logs and push them to Kinesis Analytics. The second stream receives alarms based on the analyzed data and triggers Lambda. Lambda triggers an SNS notification to deliver a message, and the data is saved in Aurora DB.
Source Code: Website Monitoring using AWS Services with Source Code
19) Bitcoin Mining
Bitcoin Mining is a critical component of maintaining and developing the blockchain ledger. It is the process in which new bitcoins are entered into rotation. It is performed using sophisticated computers that solve complex math problems. In this data engineering project, you will apply data mining concepts to mine bitcoin using the freely available relative data.
This is a straightforward project where you will extract data from APIs using Python, parse it, and save it to EC2 instances locally. After that upload data onto HDFS. Then read the data using Pyspark from HDFS and perform analysis. The techniques explained in this use case are the usage of Kryo serialization and Spark optimization techniques. An External table is going to be created on Hive/Presto, and at last for visualizing the data we are going to use AWS Quicksight.
Source Code: Top Data Engineering Project with Source Code on BitCoin Mining
20) How to deal with slowly changing dimensions?
Slowly changing dimensions (SCDs) are those attributes in a dataset that have their values amended over a long time and are not updated regularly. A few examples of SCDs include geographical location, employees, and customers. There are various great ways of amending the values for SCDs and in this project, you will learn how to implement those methods in a Snowflake Datawarehouse. Snowflake provides multiple services to help you create an effective data warehouse with ETL capabilities and support for several external data sources.
Data Description
This project uses Python's faker package to generate user data and store them in CSV format with the user's name and the current system time.
The data contains the following details:
Customer_id
Language Used - Python3, JavaScript, SQL
Packages/Libraries - Faker
Services- NiFi, Amazon S3, Snowflake, Amazon EC2, Docker
Source Code: How to deal with slowly changing dimensions using Snowflake?
21) GCP Project to Explore Cloud Functions
The three popular cloud service providers in the market are Amazon Web Services, Microsoft Azure, and GCP . Big Data Engineers often struggle with deciding which one will work best for them, and this project will be a good start for those looking forward to learn about various cloud computing services and who want to explore whether GCP is for them or not.
Project Objective: Understanding major services of the GCP including Cloud Storage, Cloud Engineer, and PubSub.
Learnings from the Project: This project will introduce you to the Google Cloud Console. You will learn how to create a service account on the GCP and understand cloud storage concepts. You will be guided on setting up a GCP Virtual machine and SSH configuration. Another exciting topic that you will learn is Pub/Sub Architecture and its application.
Tech Stack: Language: Python3
Services: Cloud Storage, Cloud Engine, Pub/Sub
Source Code: GCP Project to Explore Cloud Functions using Python
22) Visualizing Reddit Data
The first step in this project is to extract data using the Reddit API, which provides a set of endpoints that allow users to retrieve data from Reddit. Once the data has been extracted, it needs to be stored in a reliable and scalable data storage platform like AWS S3. The extracted data can be loaded into AWS S3 using various ETL tools or custom scripts. Once the data is stored in AWS S3, it can be easily copied into AWS Redshift, a data warehousing solution that efficiently analyzes large datasets. The next step is to transform the data using dbt, a popular data transformation tool that allows for easy data modeling and processing. dbt provides a SQL-based interface that allows for easy and efficient data manipulation, transformation, and aggregation. After transforming the data, we can create a PowerBI or Google Data Studio dashboard to visualize the data and extract valuable insights. To orchestrate the entire process, this project uses Apache Airflow.
Source Code: https://github.com/ABZ-Aaron/Reddit-API-Pipeline
23) Analyzing data from Crinacle
Scraping data from Crinacle's website can provide valuable insights and information about headphones and earphones. This data can be used for market analysis, product development, and customer segmentation. However, before any analysis can be conducted, the data needs to be processed, validated, and transformed. This is where a data pipeline comes in handy.
The first step in the data pipeline is to scrape data from Crinacle's website. This can be done using web scraping tools like Beautiful Soup or Scrapy. The data can then be stored in a CSV or JSON format and loaded to AWS S3 for storage. Once the data is loaded to AWS S3, it needs to be parsed and validated. Pydantic is a Python library that can be used for data validation and serialization. By defining data models using Pydantic, we can ensure that the data is in the correct format and structure. The parsed and validated data is then transformed into silver data, which is ready for analysis. The silver data can be loaded to AWS S3 for storage and backup. Additionally, the silver data can also be loaded to AWS Redshift, which is a data warehouse that enables fast querying and analysis of large datasets. AWS RDS can also be used to store the silver data for future projects. Finally, the data needs to be transformed and tested using dbt (data build tool). dbt is a tool that enables data analysts and engineers to build and manage data pipelines in a modular and scalable way. With dbt, we can transform the silver data into a format that is optimized for analysis and testing.
Source Code : Pipeline that extracts data from Crinacle's Headphone and InEarMonitor databases and finalizes data for a Metabase Dashboard by Omar
Yelp Data Analysis
Data Governance
Real-time Data Ingestion
24) Yelp Data Analysis
The yelp dataset consists of data about Yelp's businesses, user reviews, and other publicly available data for personal, educational, and academic purposes. Available as JSON files, use it to learn NLP for sample production data. This dataset contains 6,685,900 reviews, 192,609 businesses, 200,000 pictures in 10 metropolitan areas. This Azure project helps you understand the ETL process i.e, how to ingest the dataset, clean it and transform it to get business insights. Also, you get a chance to explore Azure Databricks, Data Factory, and Storage services.
There are three stages in this real-world data engineering project. Data ingestion: In this stage, you get data from Yelp and push the data to Azure Data lake using DataFactory. The second stage is data preparation . Here data cleaning and analysis happens using Databricks. The final step is Publish. In this stage, whatever insights we drew from the raw Yelp data will be visualized using Databricks
Source Code: Analyse Yelp Dataset with Spark & Parquet Format on Azure Databricks
Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization
25) Data Governance
An organization planning to leverage data as a resource needs to perform multiple operations over it, including cleaning, securing, transforming, etc. But, it is important to wonder how an organization will achieve the same steps on data of different types. The answer is to design a set of standard policies and processes to ensure consistency.
Project Idea: Azure Pureview is a data governance tool introduced by Microsoft that lets its users handle data better. In this project, you will learn how to use this tool as a beginner, and you will learn how to manage the ingested data and implement data analysis tools over it to draw insightful conclusions.
Source Code: Getting Started with Azure Pureview for Data Governance
26) Real-time Data Ingestion
Many businesses realize the potential of data as a resource and invest in learning new ways to capture user information and store it. And intending to store information comes with the responsibility of keeping it safe. That is why real data warehouses are often away from the offices and are located where a high level of security is ensured. This produces another challenge: the task of sourcing data from a source to a destination or in other words, the task of data ingestion.
Project Idea: This project is a continuation of the project mentioned previously. It will use the same tool, Azure Pureview, and help you learn how to perform data ingestion in real-time. You will explore various Azure apps like Azure Logic Apps, Azure Storage Account, Azure Data Factory, and Azure SQL Databases and work on the dataset of a hospital that has information for 30 different variables.
Source Code: Learn Real-Time Data Ingestion with Azure Pureview
ETL Pipeline
Data Integration
ETL and ELT Operations
Let us discuss these project ideas in detail
22) ETL Pipeline
Sales data helps with decision making, understanding your customers better, and improving future performance within your organization. Sales leaders must know how to interpret the data they collect and use its insights to improve their strategy. This data engineering project has all the data engineering knowledge a data engineer should have. It includes analyzing sales data using a highly competitive technology big data stack such as Amazon S3, EMR, and Tableau to derive metrics from the existing data. Finally, the cleansed and transformed data is visualized as various plots using Tableau.
Units Sold vs. Units cost per region
Total revenue and cost per country
Units sold by Country
Revenue vs. Profit by region and sales Channel
Get the downloaded data to S3 and create an EMR cluster that consists of hive service. Create an external table in Hive, perform data cleansing and transformation operations, and store the data in a target table. Finally, this data is used to create KPIs and visualize them using Tableau.
Source Code: ETL Pipeline on AWS EMR Cluster
23) Data Integration
Just like investing all your money in a single mode of investment isn’t considered a good idea, storing all the data at one place isn’t regarded as good either. Often companies store precious information at multiple data warehouses across the world. This poses the task of accumulating data from multiple sources, and this process of accumulation is called data integration.
Project Idea: To integrate data from different sources, data engineers use ETL pipelines. In this project, you will work with Amazon’s Redshift tool for performing data warehousing. Additionally, you will use tools like AWS Glue, AWS Step Function, VPC, and QuickSight to perform end-to-end sourcing of data and its analysis.
Source Code: Orchestrate Redshift ETL using AWS Glue and Step Functions
24) ETL and ELT Operations
One of the most important tasks of a data engineer is to build efficient pipelines that can transfer data from multiple sources to destinations and transform them into a form that allows easy management. These pipelines involve many ETL (Extract, Transform and Load) and ELT (Extract, Load, and Transform) operations that a data engineer must know.
Project Idea: Work on a data engineering project to learn how to perform ETL and ELT operations using Apache Cassandra and Apache Hive with PySpark. Use Amazon Redshift for creating clusters that will contain the database and its tables. Ensure that you learn how to integrate PySpark with Confluent Kafka and Amazon Redshift.
Source Code: Build a Data Pipeline using Kafka and Redshift
After working on these data engineering projects, you must prepare a good data engineering project portfolio that accurately summarizes all your skills. So, read the next section to find out how you can build a successful project portfolio.
Adding Data Engineering projects to your resume is very important if you look forward to outstanding your job applications from other candidates. Here are a few options on adding data engineering projects to your resume.
LinkedIn: Using LinkedIn for networking is pretty common, but you can also create your data engineering project portfolio. Write a LinkedIn article about your projects and feature it on your profile. Add the article link to your resume to showcase it to recruiters.
Personal Website: Try platforms like GoDaddy that allow you to create a personal website. You can decide the look of the website and present your projects. Ensure that the website has a simple UI and can be accessed by anyone. Do not use complex graphics as it may increase load time. Keep your portfolio short and crisp.
GitHub: GitHub is another perfect solution for building a project portfolio. It allows users to add all the files related to your project's folders and showcase their technical skills. Add all the relevant information in the read me file to make your portfolio user-friendly.
The data engineering projects mentioned in this blog might seem challenging. So, to motivate your interest in the field, we suggest you consider exploring the rewarding perks of pursuing the field and the increasing demand for data engineering jobs.
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The median average salary of a data engineer is around 8 lakhs per annum in India. Of course, the compensation varies based on educational qualifications, experience, geolocation, company size, reputation, and the demand for the role. The more experience as a data engineer you have, the higher will be your market value. From Payscale, we can figure out that data engineers with 1 to 4 years of experience make anywhere around 7 lakhs per annum at entry level. For data engineers with 5 to 9 years of experience, the salary becomes Rs.12 lakhs per annum. The average salary can go over 15 lakhs per annum for data engineers with more than ten decades of experience.
Has the increasing demand for data engineers piqued your interest in data engineering as a career choice? What skills do you need to grab as a data engineer? Don't be afraid; we can read your mind.
Let us now dive deeper into the data engineering role by exploring what tools data engineers use in their day-to-day life.
Below you will find a list of popular data engineering tools and a project idea to gain hands-on experience of working with them.
Azure DataFactory
Microsoft has developed Azure Data Factory to support big data engineers in building efficient ETL and ELT pipelines. It allows its users to integrate data from different sources with more than 90 built-in connectors, maintenance-free. It contains predefined codes that can be used to save time when building pipelines, but it also offers users the feature of writing their codes. It comes with built-in Git and CI/CD support.
Amazon Redshift
To help data engineers with data warehousing, Amazon has come up with Amazon Redshift . It can easily handle large amounts of data with the help of massively parallel processing technology borrowed from the company ParAccel. With Amazon Redshift, one can efficiently work with 16 petabytes of data on a single cluster. The company's name reflects an alternative to Oracle’s data warehouse services, as Oracle is often referred to as “Big Red” because of its red logo. To understand the tool better, start working on the project idea below.
Project Idea: Orchestrate Redshift ETL using AWS Glue and Step Functions
Another popular tool among data engineering practitioners for data warehousing is BigQuery by Google. It is a fully managed tool that supports data analysis, implementation of machine learning algorithms, geospatial analysis, and business intelligence solutions. It is a serverless tool that allows users to analyze petabyte volume datasets. With BigQuery, one need not put extra effort into infrastructure management as its serverless architecture supports the quick implementation of SQL queries.
Project Idea: GCP Project to Learn using BigQuery for Exploring Data
Snowflake is a cloud data platform that offers a data warehouse-as-a-service. It is best suited for organizations planning to switch to cloud computing and aim for fewer CPU cycles and high storage capacity. It supports the storage of large data volumes and allows users to perform different computation tasks on that data. It offers cloud services like infrastructure management, metadata management, authentication, query parsing, optimization, and access control.
Project Idea: Snowflake Real Time Data Warehouse Project for Beginners
Tableau is an American data analytics software widely popular for creating business intelligence solutions. Tableau was labeled as the leader for business intelligence and data analytics by Gartner’s Magic Quadrant. One of the primary reasons for its popularity is that it is easy to use and offers engaging dashboards perfect for narrating data visualization results. Try its free 14-day trial to know how to utilize it.
The Dice Tech Job Report - 2020 also listed the top 10 skills needed to become a Data Engineer and those have been summarized below.
Experience with any one object-oriented programming language such as Python , Java, etc.
Ability to adapt to new big data tools and technologies.
Demonstrated ability to utilize popular big data tools such Apache Hadoop , Apache Spark , etc. for building effective workflows.
Strong understanding of data science concepts and various algorithms in machine learning /deep learning.
In-depth knowledge of methods of building efficient ETL and ELT pipelines .
Strong proficiency in using SQL for data sourcing.
Exposure to various methodologies used for Data Warehousing.
Strong problem-solving and communication skills.
Though it might sound fascinating to kick start one’s career as a data engineer , it's not as simple as just learning some programming languages and preparing with the data engineer interview questions . So, how difficult is it to be a data engineer? The answer lies in the responsibilities of a data engineer.
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The responsibilities of a data engineer are as follows:
Help the business owners deduce the big data project architecture requirements as per their business needs.
Create and execute ETL and ELT operations for effective management of data.
Prepare data-driven suggestions for the overall growth of the business.
Engage with different teams like data science tean=ms, etc. that work with data in the organization, understand their requirements, and handle data accordingly.
Explore the latest big data engineering tools and identify the best possible solutions for the business.
Create documents that assist stakeholders in understanding various technical issues related to data and requirements for a given data infrastructure.
Experiment with different cloud-service-providing solutions and deduce the best fit for the organization.
Where can I practice Data Engineering?
To practice Data Engineering, you can start with exploring solved projects and contribute to the open-source projects on GitHub like Singer and Airflow ETL projects.
How do I create a Data Engineer Portfolio?
You can create a Data Engineer Portfolio by hosting your contributions on websites like GitHub. Additionally, write a few blogs about them giving a walkthrough of your projects.
How to Start your First Data Engineering Project?
A practical data engineering project has multiple components. To Start your First Data Engineering Project, follow the below checklist -
Have a clear understanding of the data that is meant to be collected.
Identify big data tools that are likely to best work with the given data type.
Prepare a layout of the design of pipelines.
Install all the necessary tools.
Prepare the infrastructure and start writing the code accordingly.
Test the design and improve the implementation.
What are the real-time data sources for data engineering projects?
You can leverage real-time data sources for data engineering projects with the help of Twitter official API, REST API, and Meetup for streaming event comments, photos, etc.
Why should you work on a data engineering project?
If you are interested in pursuing data engineering as a career, then working on a data engineering project is a must for you. It will help you understand how the industry works and give you a real-world perspective on how practical problems can be solved.
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Big Data Specialization from University of California San Diego
Big Data Specialization from University of California San Diego is an introductory learning path for the Big Data world.
This specialization covers:
- Big Data essential concepts
- Hadoop and MapReduce
- NoSQL and MongoDB
- Graph Databases and Neo4j
- Big Data Analytics and Apache Spark, Hive, Pig
Courses in this Program
- Introduction to Big Data
- Big Data Modeling and Management Systems
- Big Data Integration and Processing
- Machine Learning With Big Data
- Graph Analytics for Big Data
Certificate of Completion
You can see the Certificate of Completion and other certificates in my Certificates Repo that contains all my certificates obtained through my journey as a self-made Data Science and better developer.
⚠️ Disclaimer ⚠️
Please, don't fork or copy this repository.
Big Data Specialization from University of California San Diego, is a very easy and straight forward path. You can complete it with a minimal effort.
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- Jupyter Notebook 96.3%
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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.
View the BuzzFeed Datasets. Here are some examples: Federal Surveillance Planes — contains data on planes used for domestic surveillance. Zika Virus — data about the geography of the Zika virus outbreak. Firearm Background Checks — data on background checks of people attempting to buy firearms. 3. NASA.
Where To Get More Ideas Never stop searching! Here are some ways to get more leads, either in the form of project ideas or datasets to use. 1. Academic papers 2. Kaggle Competitions 3. Kaggle...
In this post, we'll highlight the key elements that your data analytics portfolio should demonstrate. We'll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping, exploratory analysis, and data visualization. We'll cover:
Python is a powerful tool for data analysis projects. Whether you're web scraping data - on sites like the New York Times and Craigslist- or you're conducting Exploratory Data Analysis (EDA) on Uber trips, here are three Python data analytics project ideas to try: 1. Enigma Transforming CSV file Take-Home.
An EDA project is an excellent time to take advantage of the wealth of public datasets available online. Here are 10 fun and free datasets to get you started in your explorations. 1. National Centers for Environmental Information: Dig into the world's largest provider of weather and climate data. 2.
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.
Check the topics for the capstone project examples below to pick one. Decide how deeply you will research the topic and define how wide or narrow the sphere of your investigation will be. Cybersecurity: Threats and Elimination Ways Data Mining in Commerce: Its Role and Perspectives Programming Languages Evolution Social Media Usage: How Safe It Is?
The capstone project, entitled "Vehicle Impoundment Records Management System" is designed to record, process and monitor impounded vehicles. The system will electronically manage all impounded vehicle-related information. From impounding up to releasing the vehicles.
27 Free Capstone Project Ideas and Tutorials Here are the lists of capstone project ideas, programming related tutorials and free source code for the month of March 2022. The compilation consists of the following: Conceptual Framework Free Source code Programming Tutorials Entity Relationship Diagram Database Design
Cybersecurity projects can teach vital skills like threat detection and mitigation, identity access and management (IAM) governance, and vulnerability assessment and remediation tactics. Robust cybersecurity bootcamp programs use project-based learning to teach aspiring cybersecurity professionals the skills that they need to get hired.
The best data engineering projects showcase the end-to-end data process, from exploratory data analysis (EDA) and data cleaning to data modeling and visualization. In these projects, make sure that you show evidence of data pipeline best practices. You should be able to spot failure points in data pipelines and build systems that are resistant ...
Capstone projects challenge students to acquire and analyze data to solve real-world problems. Project teams consist of two to four students and a faculty advisor. Teams select their capstone project at the beginning of the year and work on the project over the course of two semesters.
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. 13. Tourist Behavior Analysis. This is one of the most innovative big data project concepts.
120 Capstone Project Ideas for Information Technology Use one of these information technology capstone project examples as your topic or inspiration. Get affordable and high quality legal essay writing service here. Home Surveillance and Automation iPhone SMS Notification Systems Using GSM Technologies for Detecting Theft POS Apps and Their Use
The Big Data Capstone project will give you the chance to demonstrate practically what you have learned in the Big Data MicroMasters program including: How to evaluate, select and apply data science techniques, principles and theory; How to plan and execute a project; Work autonomously using your own initiative;
A capstone project refers to a final or culminating project high school or college seniors need to earn their degrees. It's usually a project that takes several months to complete and should demonstrate students' command over particular subjects within an area of study. It may be similar to master's thesis writing.
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".
Project Idea: Explore what is real-time data processing, the architecture of a big data project, and data flow by working on a sample of big data. Learn how to use various big data tools like Kafka, Zookeeper, Spark, HBase, and Hadoop for real-time data aggregation. Also, explore other alternatives like Apache Hadoop and Spark RDD.
Big Data Specialization from University of California San Diego is an introductory learning path for the Big Data world. This specialization covers: Big Data essential concepts; Hadoop and MapReduce; NoSQL and MongoDB; Graph Databases and Neo4j; Big Data Analytics and Apache Spark, Hive, Pig; Courses in this Program. Introduction to Big Data
Program Details and Capstone Projects. For people who are not aware - Praxis Business School offers a year-long program - PGP in Data Science with ML & AI at both its campuses - Kolkata and Bengaluru. The program is structured in a manner where the first 9 months are spent in the classroom with in-house and industry faculty and the last 3 ...
Answer (1 of 3): Big data is a term used to describe the large volumes of data that are generated by organizations, governments, and individuals on a daily basis. It can come from a variety of sources, including social media, sensor data, mobile devices, and online transactions. The sheer volume ...