Ten years ago, it use be years ago quite difficult to find good datasets for data science and machine learning projects. Today, we have the opposite problem.
We’ve been flooded with lists and lists of datasets. The problem nowadays is not finding datasets, but rather sifting through them to keep the relevant ones.
Well, we’ve done that for you right here.
Below, you’ll find a curated list of free datasets for data science and machine learning, organized by their use case. You’ll find both hand-picked datasets and our favorite aggregators.
Table of Contents
- Exploratory Analysis
- General Machine Learning
- Deep Learning
- Natural Language Processing
- Cloud-Based Machine Learning
- Time Series Analysis
- Recommender Systems
- Specific Industries
- Streaming Data
- Web Scraping
- Current Events
Datasets for Exploratory Analysis
Exploratory analysis is your first step in most data science exercises. The best datasets for data science & exploratory analysis should be fun, interesting, and non-trivial (i.e. require you to dig a little to uncover all the insights).
All links open in a new tab.
- Game of Thrones – Game of Thrones is a popular TV series based on George R.R. Martin’s A Song of Fire and Ice book series. With this dataset, you can explore its political landscape, characters, and battles.
- World University Rankings – Ranking universities can be difficult and controversial. There are hundreds of ranking systems, and they rarely reach a consensus. This dataset contains three global university rankings.
- IMDB 5000 Movie Dataset – This dataset explores the question of whether we can anticipate a movie’s popularity before it’s even released.
- Kaggle Datasets – Open datasets contributed by the Kaggle community. Here, you’ll find a grab bag of topics. Plus, you can learn from the short tutorials and scripts that accompany the datasets.
- r/datasets – Open datasets contributed by the Reddit community. This is another source of interesting and quirky datasets, but the datasets tend to less refined.
Datasets for General Machine Learning
In this context, we refer to “general” machine learning as Regression, Classification, and Clustering with relational (i.e. table-format) data. These are the most common ML tasks.
- Wine Quality (Regression) – Properties of red and white vinho verde wine samples from the north of Portugal. The goal is to model wine quality based on physicochemical tests. (We also have a tutorial.)
- Credit Card Default (Classification) – Predicting credit card default is a valuable and common use for machine learning. This rich dataset includes demographics, payment history, credit, and default data.
- US Census Data (Clustering) – Clustering based on demographics is a tried and true way to perform market research and segmentation.
- UCI Machine Learning Repository – The UCI ML repository is an old and popular aggregator for machine learning datasets. Tip: Most of their datasets have linked academic papers that you can use for benchmarks.
Datasets for Deep Learning
While not appropriate for general-purpose machine learning, deep learning has been dominating certain niches, especially those that use image, text, or audio data. From our experience, the best way to get started with deep learning is to practice on image data because of the wealth of tutorials available.
- MNIST – MNIST contains images for handwritten digit classification. It’s considered a great entry dataset for deep learning because it’s complex enough to warrant neural networks, while still being manageable on a single CPU. (We also have a tutorial.)
- CIFAR – The next step up in difficulty is the CIFAR-10 dataset, which contains 60,000 images broken into 10 different classes. For a bigger challenge, you can try the CIFAR-100 dataset, which has 100 different classes.
- ImageNet – ImageNet held an annual computer vision competition for many years, and was considered to be the benchmark for modern performance. The image dataset has 1000 different classes.
- YouTube 8M – Ready to tackle videos, but can’t spare terabytes of storage? This dataset contains millions of YouTube video ID’s and billions of audio and visual features that were pre-extracted using the latest deep learning models.
- Wikipedia List – Datasets for machine learning research that have been cited in peer-reviewed journals. Tip: Check out the original papers for benchmarks.
- PapersWithCode.com – Community contributed database of open deep learning datasets.
Datasets for Natural Language Processing
Natural Language Processing (N.L.P.) is about text data. And for messy data like text, it’s especially important for the datasets to have real-world applications so that you can perform easy sanity checks.
- Enron Dataset – Email data from the senior management of Enron, organized into folders. This dataset was originally made public and posted to the web by the Federal Energy Regulatory Commission during its investigation.
- Amazon Reviews – Contains ~35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.
- Newsgroup Classification – Collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. Great for practicing text classification and topic modeling.
- nlp-datasets (Github) – Alphabetical list of free/public domain datasets with text data for use in NLP.
- Quora Answer – List of annotated corpora for NLP.
Datasets for Cloud Machine Learning
Technically, any dataset can also be used for cloud-based machine learning if you just upload it to the cloud. However, if you’re just starting out and evaluating a platform, you may wish to skip all the data piping.
Fortunately, all major cloud computing services provide public datasets that you can easily import. Their datasets are all comparable.
Datasets for Time Series Analysis
Time series analysis requires observations marked with a timestamp. In other words, each subject and/or feature is tracked across time.
- MarketStack – Free (with limits) API for real-time, intraday, and historical market data.
- Zillow Real Estate Research – Home prices and rents by size, type, and tier, sliced by zip code, neighborhood, city, metro area, county and state.
- Global Education Statistics – Over 4,000 internationally comparable indicators for education access, progression, completion, literacy, teachers, population, and expenditures.
- Nasdaq Data Link – Nasdaq Data Link (formerly Quandl) contains free and premium time series datasets for financial analysis.
- The World Bank – Contains global macroeconomic time series and searchable by country or indicator.
Datasets for Recommender Systems
Recommender systems have taken the entertainment and e-commerce industries by storm. Amazon, Netflix, and Spotify are great examples.
- MovieLens – Rating data sets from the MovieLens web site. Perfect for getting started thanks to the various dataset sizes available.
- Jester – Ideal for building a simple collaborative filter. Contains 4.1 Million continuous ratings (-10.00 to +10.00) of 100 jokes from 73,421 users.
- Million Song Dataset – Large, rich dataset for music recommendations. You can start with a pure collaborative filter and then expand it with other methods such as content-based models or web scraping.
- entaroadun (Github) – Collection of datasets for recommender systems. Tip: Check the comments section for recent datasets.
Datasets for Specific Industries
In this compendium, we’ve organized datasets by their use case. This is helpful if you need to practice a certain skill, such as deep learning or time series analysis.
However, you may also wish to search by a specific industry, such as datasets for neuroscience, weather, or manufacturing. Here are a couple options:
- Awesome Public Datasets – High quality datasets for data science, separated by industry.
- Data.gov – Curated government data separated by industry.
Datasets for Streaming
Streaming datasets are used for building real-time applications, such as data visualization, trend tracking, or updatable (i.e. “online”) machine learning models.
- Twitter API – The twitter API is a classic source for streaming data. You can track tweets, hashtags, and more.
- StockTwits API – StockTwits is like a twitter for traders and investors. You can expand this dataset in many interesting ways by joining it to time series datasets using the timestamp and ticker symbol.
- OpenWeatherMap.org – A reliable weather API with global coverage. Features a free tier and paid options for scaling up.
- Awesome Public Streaming Datasets – Complement to Awesome Public Datasets: list of datasets that provide a streaming interface via API.
Datasets for Web Scraping
Web scraping is a common part of data science research, but you must be careful of violating websites’ terms of services. Fortunately, there’s a whole site that’s designed to be freely scraped.
- ToScrape.com – Web scraping sandbox with two subdomains. You can practice scraping a fictional bookstore or a site that lists quotes from famous people.
Datasets for Current Events
Finding datasets for current events can be tricky. Fortunately, some publications have started releasing the datasets they use in their articles.
- FiveThirtyEight – FiveThirtyEight is a news and sports site with data-driven articles. They make their datasets openly available on Github.
- BuzzFeedNews – BuzzFeed became (in)famous for their listicles and superficial pieces, but they’ve since expanded into investigative journalism. Their datasets are available on Github.
If you’d like to learn how to actually use these datasets for data science and practical ML applications, check our other free resources:
- How to Learn Python for Data Science – Python is our preferred language for data science.
- Python Seaborn Tutorial – Our favorite library for exploratory analysis.
- Python Scikit-Learn Tutorial – Our favorite library for general purpose machine learning.
- Python Keras Tutorial – Our favorite library for deep learning.
- Modern Machine Learning Algorithms – Overview of machine learning algorithms, including each one’s strengths and weaknesses.