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

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).

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Our picks:

  • 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.

Aggregators:

  • 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.

Our picks:

Aggregators:

  • 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.

Our picks:

  • 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.

Aggregators:

  • 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.
YouTube-8M Dataset
YouTube-8M Dataset

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.

Our picks:

  • 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.

Aggregators:

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.

Our picks:

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.

Our picks:

  • 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.

Aggregators:

  • 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.
Zillow Real Estate Data
Zillow Real Estate Data

Datasets for Recommender Systems

Recommender systems have taken the entertainment and e-commerce industries by storm. Amazon, Netflix, and Spotify are great examples.

Our picks:

  • 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.

Aggregators:

  • 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:

Aggregators:

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.

Our picks:

  • 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.

Aggregators:

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.

Our picks:

ToScrape Bookstore
ToScrape Bookstore

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.

Aggregators:

  • 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.

Next Steps

If you’d like to learn how to actually use these datasets for data science and practical ML applications, check our other free resources: