15 High-Quality Datasets Every Data Scientist Should Practice On in 2026

Introduction

As a data scientist, one of the best ways to hone your skills is by working on a diverse array of datasets. The more you expose yourself to data from different domains, the better equipped you‘ll be to tackle real-world data science challenges. Regularly practicing on high-quality datasets is key to becoming a well-rounded data scientist.

In this post, I‘ve curated a list of 15 excellent datasets across machine learning, computer vision, and natural language processing for data scientists to practice on. The datasets in this list are all fairly recent, being released between 2020-2024. I‘ll describe each one, including what data it contains and potential use cases. I‘ll also provide links so you can easily access and download them yourself.

So let‘s dive in and explore some of the best datasets that every data scientist should consider adding to their portfolio!

Machine Learning Datasets

First up, let‘s look at 5 high-quality datasets well-suited for practicing fundamental machine learning techniques like regression, classification, and clustering.

1. Kaggle Competitions

Kaggle is a popular platform that hosts data science competitions. Datasets are regularly added for these contests, so it‘s a great place to find interesting data to work with. A few notable recent competitions include:

• Spaceship Titanic: Predict which passengers were transported in a sci-fi scenario based on personal records. Good for binary classification.
• Predict Future Sales: Forecast monthly sales based on daily historical sales data. Well-suited for time series forecasting.
• HuBMAP – Hacking the Kidney: Identify glomeruli in human kidney tissue images. Great for semantic segmentation.

The datasets and kernels for past Kaggle competitions remain available, so I recommend browsing through and finding ones that interest you. Access them at: https://www.kaggle.com/competitions

2. UCI Machine Learning Repository

The UCI Machine Learning Repository is a collection of over 500 datasets used for ML research. While some are older, new datasets continue to be added. A few interesting recent ones include:

• Beijing Multi-Site Air Quality: Hourly air pollutant data from 12 monitoring sites. Useful for time series analysis and forecasting.
• Facebook Large Page-Page Network: Data on verified Facebook pages and links between them. Can be used for social network analysis and link prediction.
• Tao Te Ching: The complete text of the classic Chinese Taoist book. Interesting for NLP techniques like translation or text generation.

Browse and access all the datasets at: https://archive.ics.uci.edu/ml/index.php

3. Government Open Data Portals

Many governments around the world have open data initiatives and maintain public data portals. These can be great sources for interesting public datasets on a variety of topics. A few examples:

• U.S. Government Open Data: Data.gov has over 200,000 datasets on topics like climate, energy, education, and more from U.S. agencies.
• EU Open Data Portal: This portal provides data from European Union institutions and agencies. Categories include economy, science, environment and more.
• India Open Government Data Platform: Datasets from various Indian government agencies on agriculture, healthcare, education and more.

Do a search for open data portals in your country or region of interest. There is a ton of fascinating public information out there waiting to be analyzed!

Computer Vision Datasets

Next, let‘s explore some high-quality, large-scale computer vision datasets. Whether you want to practice techniques like classification, object detection, or segmentation, you‘ll find great options here.

4. ImageNet

ImageNet is a massive dataset with over 14 million labeled images across more than 20,000 categories. It has been hugely influential in advancing computer vision research. While the full dataset is not available, a smaller subset called ImageNet1K with 1000 categories is publicly available.

Access ImageNet and its subsets at: https://www.image-net.org/

5. COCO (Common Objects in Context)

COCO is a large-scale object detection, segmentation, and captioning dataset. It has 330,000 images with 1.5 million object instances across 80 categories. COCO is widely used to benchmark object detection models.

Annotations include bounding boxes, segmentation masks, and image captions, allowing COCO to be used for multiple computer vision tasks. The dataset keeps being updated with new releases and challenges as well.

Download the dataset and learn more at: https://cocodataset.org/

6. Google Open Images

Open Images is a huge collection of 9 million images annotated with image-level labels, object bounding boxes, segmentation masks, and more. It contains a total of 16 million bounding boxes across 600 categories. The dataset is a product of a collaboration between Google, Carnegie Mellon, and Cornell universities.

This large, diverse dataset is great for benchmarking object detection models at a huge scale. Access it at: https://storage.googleapis.com/openimages/web/index.html

7. BDD100K

The Berkeley Deep Drive dataset contains 100,000 HD video sequences of over 1,100 hours of driving footage. It is the largest and most diverse open driving video dataset. Annotations include image level tagging, road object bounding boxes, drivable area, lane marking, and full-frame segmentation.

This dataset is very useful for training autonomous driving perception models. Access it at: https://www.bdd100k.com/

Natural Language Processing Datasets

Finally, let‘s look at some high-quality text datasets to fuel your NLP projects. Whether you want to build language models, chatbots, sentiment classifiers or more, check out these great options.

8. Wikipedia

Wikipedia is a huge source of text data that is freely available. It contains over 6 million articles in English alone. Wikipedia data has been used to train language models, knowledge bases, and more.

You can access Wikipedia dumps at: https://dumps.wikimedia.org/

9. Project Gutenberg

Project Gutenberg is a library of over 60,000 free eBooks, especially older works for which U.S. copyright has expired. It includes many classic literature and non-fiction works. This dataset is great for training language models or text generation systems.

Access the dataset at: https://www.gutenberg.org/

10. Common Crawl

Common Crawl is a repository of web page data collected over 8 years of web crawling. It contains raw web page HTML data, metadata extracts and text extracts. As of 2021, it contained 280 TB of compressed data!

This huge dataset has been used to train language models like GPT-3. It allows training on a massive amount of diverse web data.

Learn more and access the data at: https://commoncrawl.org/

11. Amazon Reviews

Amazon Customer Reviews is a dataset of over 130 million customer reviews spanning two decades from Amazon.com. Reviews include product and user information, ratings, and a plaintext review.

This dataset is great for sentiment analysis and recommender systems. And with 130 million reviews, it allows training on a truly massive scale.

Access the dataset at: https://registry.opendata.aws/amazon-reviews/

12. Twitter Sentiment140

Sentiment140 is a popular dataset originally collected for a research competition. It contains 1.6 million tweets extracted using the Twitter API. Tweets have been annotated (0 = Negative, 4 = Positive) and the dataset is often used as a benchmark for sentiment analysis.

Download the dataset at: http://help.sentiment140.com/for-students/

13. GLUE Benchmark

The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for evaluating NLP systems on a diverse set of existing NLU tasks. Tasks include question answering, sentiment analysis, textual entailment and more.

GLUE has become a standard for evaluating performance of NLP models. Access the data and learn more at: https://gluebenchmark.com/

Conclusion

In this post, we covered 15 high-quality datasets across computer vision, NLP and general machine learning. These should give you a great foundation to practice a variety of data science techniques.

But don‘t stop here – there are many other excellent datasets out there! Kaggle and Google Dataset Search are great places to find more. The key is to continually challenge yourself with new data.

By frequently working with diverse, real-world datasets, you‘ll greatly accelerate your growth as a data scientist. So pick some of these datasets and start practicing. Before long, you‘ll be ready to solve even the toughest data challenges. Happy learning!