10 Best Websites to Find Quality Datasets for Your Next Data Science Project

As an aspiring data scientist looking to build an impressive portfolio, finding high-quality datasets to work with is crucial. But datasets aren‘t one-size-fits-all. To build robust and generalizable machine learning models, it‘s important to gain experience working with a variety of data modalities, from classic tabular data to images, text, audio, and time series.

Fortunately, there are a number of excellent websites that curate datasets and make them available for data science projects. Many of these sites offer datasets completely free, while others include both free and paid options. Collectively, they host millions of datasets and boast communities of millions of data scientists.

In this post, we‘ll highlight 10 of the best websites to source datasets for your next machine learning or data analysis project. For each site, we‘ll give an overview of what kind of data you can expect to find, usage statistics, and any special features that make the platform uniquely useful for data science practitioners. Let‘s dive in!

1. Kaggle

Kaggle is the go-to resource for many data scientists looking for interesting datasets. The site hosts datasets, machine learning competitions, tutorials, and a vibrant community of over 5 million data science professionals and enthusiasts.

On Kaggle, you‘ll find over 50,000 public datasets across a wide range of domains, from healthcare to e-commerce to sports. Datasets are neatly categorized and searchable, making it easy to find exactly what you need. Many of the datasets are linked to ongoing or past Kaggle competitions, so you can see examples of how others have worked with the data.

One great feature of Kaggle is the ability to explore datasets and prototype your analysis right in your browser using Kaggle Notebooks, a free hosted Jupyter notebook environment. Notebooks can be run with GPU acceleration, making it easy to train complex deep learning models without any setup.

Some notable datasets on Kaggle:

• Titanic: Machine Learning from Disaster (predict survival on the Titanic)
• Credit Card Fraud Detection (identify fraudulent credit card transactions)
• Dogs vs. Cats (classify images of dogs and cats)

2. UCI Machine Learning Repository

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for empirical analysis of algorithms.

Established in 1987, it‘s one of the oldest sources for machine learning datasets. While not as expansive as some newer sites, the UCI repository contains many classic datasets that have been widely studied and cited in academic literature.

The repository currently hosts 559 datasets, categorized by task (e.g. classification, regression, clustering) and format. Notably, the site maintains careful versioning of datasets, so you can be confident you‘re working with the exact same data as other researchers, making it easy to benchmark your results against published work.

Some notable datasets in the UCI repository:

• Iris (classify types of iris plants based on measurements)
• MNIST handwritten digits (recognize handwritten numerical digits)
• Adult Census Income (predict whether income exceeds $50K/yr)

3. Papers With Code Datasets

Papers With Code is a unique resource that links machine learning research papers with their associated code repositories and datasets. The site‘s Datasets section indexes over 3,000 datasets used in machine learning research.

You can browse datasets by task (e.g. image classification, question answering, speech recognition), domain (e.g. biology, economics, physics), or modality (e.g. images, text, audio). Each dataset page includes a description, links to download the data, and a leaderboard showing the current state-of-the-art results on the dataset.

One standout feature of Papers With Code is the ability to see which research papers have used a given dataset. This provides valuable context on how the dataset has been used and what techniques have been successful.

Some notable datasets on Papers With Code:

• ImageNet (1.4 million images across 1,000 classes)
• Penn Treebank (annotated corpus of English sentences)
• LibriSpeech (1,000 hours of English speech)

4. AWS Open Data Registry

Amazon Web Services hosts a variety of datasets in its cloud platform that anyone can access for free. The AWS Open Data Registry makes it easy to find datasets hosted on AWS.

You‘ll find everything from satellite imagery to genomic data to demographic surveys. Many of the datasets are massive – terabytes or even petabytes in size. AWS provides tools like Amazon Athena and Amazon SageMaker to query and analyze the datasets in the cloud, so you don‘t need to download the entire dataset.

One advantage of the AWS Open Data Registry is that many of the datasets are updated on a regular basis. If you‘re looking for data to power a production application, these datasets can be a good choice. Some datasets are also pre-processed and ready for machine learning, saving significant time on data cleaning and feature engineering.

Some notable datasets on AWS:

• Common Crawl (web archive of over 2 billion web pages)
• Landsat 8 (satellite imagery of Earth‘s surface)
• 1000 Genomes Project (human genome sequences)

5. TensorFlow Datasets

TensorFlow Datasets is a collection of ready-to-use datasets for machine learning, particularly for deep learning applications using TensorFlow or other neural network libraries.

The site hosts over 150 datasets across a range of modalities including images, text, audio, and video. Datasets are provided as TensorFlow Datasets objects, which abstract away details of file formats and make it easy to load data into a model.

TensorFlow Datasets aim to standardize the process of data loading and preprocessing, which can often be a major source of friction in machine learning projects. The datasets are versioned and include detailed provenance information.

Some notable datasets on TensorFlow Datasets:

• COCO (Common Objects in Context – images with object segmentation masks)
• Sentiment140 (1.6 million tweets labeled for sentiment)
• WMT (parallel corpora for machine translation)

6. HuggingFace Datasets

HuggingFace, creators of the popular Transformers library for natural language processing, recently launched a dataset hosting platform to make it easy to share and discover NLP datasets.

The HuggingFace Datasets library provides a simple, unified way to download and access over 1,000 NLP datasets. You can search for datasets by task, language, license, and other attributes.

One nice feature of HuggingFace Datasets is the ability to slice and combine datasets using a simple API. This makes it easy to create custom train/test splits or benchmark a model on multiple related datasets.

Some notable datasets on HuggingFace:

• SQuAD (Stanford Question Answering Dataset)
• GEN (large corpus of news articles)
• ELI5 (long-form question answering dataset)

7. Google Dataset Search

Google Dataset Search is like a Google search engine specifically for finding datasets. It lets you search across a wide range of data repositories using keyword searches.

Results include dataset hosting platforms, academic databases, and other sources. Clicking a result takes you to the dataset landing page on the external site, where you can learn more and download the data.

Google Dataset Search is a great tool for discovering datasets you may not find on the major data science-specific sites. It casts a very wide net, so you‘ll need to do some filtering and exploring of results to find what you need.

8. data.world

data.world is a platform for collaborating on datasets and data projects. The site hosts over 100,000 datasets on a wide range of topics.

One unique feature of data.world is the ability to connect datasets to each other and collaboratively clean, analyze, and visualize data. Users can form teams to work on data projects together.

Data on data.world is organized into "datasets" (raw data), "projects" (datasets plus associated code and analysis), and "insights" (interesting findings from a project). This structure encourages a collaborative, iterative approach to working with data.

9. Quandl

Quandl is a repository of financial and economic datasets. Some datasets are free, while others require payment.

The site focuses on data that is useful for building financial models and conducting economic research. This includes things like stock prices, commodity prices, economic indicators, and company fundamentals.

One nice feature of Quandl is that you can preview and access many of the datasets via API without signing up for an account. Datasets are also well-documented, with information on the primary source, update frequency, and licensing.

10. GitHub

GitHub needs no introduction for most developers, but it‘s also a fantastic resource for finding open datasets. Many organizations and individuals use GitHub to share datasets alongside code.

A simple way to find datasets on GitHub is to use the search bar and filter by file extension (e.g. .csv) or topic (e.g. "dataset"). You can also try more advanced Google searches like "site:github.com [topic] dataset".

Once you find a dataset you like, you can easily preview it right on GitHub. And if you spot any issues or have ideas for improvement, you can open an issue or contribute back with a pull request!

Tips for Finding Quality Datasets

With so many dataset resources available, how do you identify high-quality datasets that are a good fit for your machine learning project? Here are a few tips:

• Consider the source. Is the dataset coming from a reputable repository or institution? Has it been used in published research?
• Check the documentation. Is there a clear description of what the dataset contains and how it was collected? Are there any known issues or limitations?
• Look at sample data. Most repositories allow you to preview a subset of the data. Take a look to get a sense of the data format, cleanliness, and completeness.
• Assess the size and scope. Is the dataset large enough to train a robust model? Is it representative of the problem domain you‘re interested in?
• Consider the license. Make sure you have permission to use the dataset for your intended purpose, whether that‘s research, a hobby project, or a commercial application.

Remember, even the highest quality datasets typically require some preprocessing before they‘re ready for machine learning. Be prepared to invest time in data cleaning, normalization, and feature engineering.

The Importance of Diverse Datasets

Working with a variety of datasets is crucial for building robust machine learning models. Different types of data pose different challenges:

• Tabular data requires careful feature selection and engineering
• Image data is often high-dimensional and benefits from data augmentation
• Text data needs to be tokenized and embedded, and may have issues like misspellings or slang
• Time series data must be split carefully to avoid leakage and often exhibits seasonality and trend

By experimenting with diverse datasets, you‘ll develop a strong intuition for how to preprocess and model each type of data. You‘ll also be prepared for real-world data science projects, which often involve messy, heterogeneous data.

Benchmark datasets in particular have been instrumental in driving progress in machine learning. Large, high-quality datasets like ImageNet (for computer vision) and SQuAD (for question answering) have enabled researchers to develop and compare increasingly sophisticated models. The rise of transfer learning means that models pre-trained on benchmark datasets can often be fine-tuned for related tasks with much less data.

Go Forth and Explore

There‘s never been a better time to be a data scientist. The proliferation of open datasets and data sharing platforms has made it easier than ever to find interesting data to learn from and build models with.

So get out there and start exploring! Try a dataset search engine like Google Dataset Search or Papers With Code to find datasets relevant to your interests. Download a few datasets and dive into the data preparation process. Train a model or two and evaluate your results.

The most important thing is to get hands-on experience with as many different datasets as you can. Over time, you‘ll develop a keen eye for what makes a dataset suitable for machine learning and how to get the most out of even messy, challenging data. That‘s when you know you‘re thinking like a true data scientist.