25+ Datasets for Data Science Projects: From Clean to Big Data
By
Liz Fujiwara
•
Aug 22, 2025
Finding the right data is often one of the biggest challenges when working on data science projects. Whether you’re a beginner looking to practice fundamental techniques or an experienced practitioner tackling complex analyses, having access to high-quality datasets is essential. This article highlights 25+ essential datasets for data science, carefully selected to accommodate a range of skill levels and project goals. From popular sources like Kaggle to government and open-data platforms, you’ll discover reliable datasets that allow you to practice your skills, explore real-world problems, and develop a strong portfolio. By knowing where to source the right data, you can accelerate your learning, improve your project outcomes, and excel in the competitive field of data science.
Key Takeaways
Access to diverse datasets is essential for data science projects. Popular sources such as Kaggle, the UCI Machine Learning Repository, and Google Dataset Search provide valuable resources for a wide range of projects.
Publicly available big datasets from platforms are particularly useful for large-scale data analysis, offering robust tools for data exploration and visualization.
Specialized datasets for machine learning and personal data projects present unique opportunities for analysis. Sources such as FiveThirtyEight and Google Takeout facilitate individual insights and model optimization, allowing data scientists to explore nuanced problems and refine their models.
Essential Datasets for Data Science Projects
Access to a diverse range of datasets is fundamental for excelling in data science. They can be manipulated and analyzed to gain insights and practice various skills. Engaging with different project types is one of the best ways to learn and grow in this field. However, sourcing the right dataset can often be a significant challenge, especially for beginners.
Public datasets are ideal resources for creating interesting analyses and projects. Here are some of the most essential datasets available, which can serve as the backbone of your data science work and help you design compelling data visualizations.
Kaggle Datasets
Kaggle is a goldmine for data scientists, offering a wide variety of datasets, including those used in machine learning competitions as well as user-contributed datasets. One of the most well-known datasets on Kaggle is the Titanic dataset, commonly used to practice machine learning techniques. These datasets are typically formatted as .csv files, making them compatible with many data analysis tools.
To access Kaggle datasets, users must:
Sign up and accept the competition terms
Download the desired datasets once registered
Participate in competitions that challenge them to solve real-world problems using the provided data
Kaggle’s community also allows users to share results and exchange knowledge, fostering a collaborative learning environment for data scientists at all levels.
UCI Machine Learning Repository
The UCI Machine Learning Repository is one of the oldest sources for user-contributed datasets, established in 1987. Most datasets in the repository are clean and ready for machine learning, making them an excellent starting point for beginners. Users can download datasets directly from the UCI Repository without needing to create an account.
A modernized beta version of the UCI Repository is currently being tested to enhance the user experience. It remains a reliable source for a wide range of datasets used in education, research, and data projects.
Google Dataset Search
Google Dataset Search is a powerful tool for discovering datasets using keyword inputs. It allows users to locate datasets simply by entering relevant keywords, similar to standard Google searches. The search process is intuitive, making it accessible to both beginners and experienced data scientists.
Google Dataset Search indexes datasets’ metadata rather than their content. This approach supports a variety of file formats, including PDF, CSV, JPG, and TXT, providing a versatile tool for finding the data you need for your projects.
Publicly Available Big Data Sets
Publicly available big datasets are invaluable for tackling large-scale data projects that require robust analysis tools. Amazon and Google are recommended cloud-hosting providers for accessing datasets used in data analysis and visualization projects.
Big datasets are particularly well-suited for projects involving tools like Spark or Hadoop to process large volumes of data. Some of the best sources for big data include AWS Public Datasets, Google Cloud Public Datasets, and Wikipedia Data Dumps.
AWS Public Datasets
Amazon Web Services (AWS) offers a wide range of large public datasets available for download. Some examples include ENSEMBL Annotated Genome data, US Census data, UniGene, and the Freebase dump. Users can upload, download, and analyze these datasets either on their own computers or in the cloud using tools like EC2 and Hadoop, with additional resources available for mobile applications.
AWS provides several features to support data exploration:
A Registry of Open Data for discovering and sharing datasets
Free data transfer within Amazon’s ecosystem when within the same zone
A free access tier for new AWS accounts, making it a cost-effective option for exploring big data
Google Cloud Public Datasets
Google Cloud offers powerful tools for exploring large datasets, with BigQuery being the primary tool. Key features include:
The first 1TB of queries is free, allowing users to explore datasets without initial costs
Access to various datasets, including historical weather data from NOAA
Availability of these datasets through Google Public Datasets
The Google Cloud Platform provides access to large datasets suitable for data science projects, helping users analyze and visualize data effectively.
Wikipedia Data Dumps
Wikipedia offers comprehensive datasets, including article content, edit history, and activity logs, which are ideal for large-scale data projects. These datasets are available in multiple formats, such as XML and SQL. Users can download Wikipedia’s datasets and use scripts to reformat the data as needed, making them versatile for a variety of analysis tasks.
Health and Social Impact Data
Health and social impact data provide critical insights into trends affecting populations and help inform public health policies. These datasets are essential for data analysis and creating impactful data visualizations.
World Health Organization (WHO)
The World Health Organization (WHO) maintains the Global Health Observatory dataset, a valuable resource for data scientists. WHO provides extensive health-related data, including mortality rates, disease prevalence, and vaccination coverage. These datasets are essential for understanding global health trends and informing public health strategies.
WHO’s datasets also include information on antimicrobial resistance, adding depth and context to health data analysis.
Pew Research Center
The Pew Research Center offers datasets on US politics, journalism, media, internet and technology, and science and society. These datasets are valuable for understanding social impacts and conducting research in the social sciences.
Pew’s datasets also cover religion and public life, providing a comprehensive view of societal trends and behaviors.
Climate and Environmental Data
Climate and environmental data are essential for tracking trends related to climate change and informing policy recommendations. These datasets help researchers understand the impacts of climate change and support a wide range of environmental studies.
NOAA Climate Data Online
The National Oceanic and Atmospheric Administration (NOAA) provides access to historical weather data and climate records. NOAA’s Climate Data Online offers a searchable catalog of various climate-related datasets for research and analysis. These datasets include daily, monthly, and yearly climate summaries, making them valuable for studying long-term trends and patterns.
NASA Earth Science Data
NASA offers a wide range of datasets, including Earth science and space datasets. These resources are crucial for visualizing changes in Earth’s climate through satellite imagery and atmospheric data. NASA’s comprehensive datasets from various Earth observation projects further enhance environmental research and analysis.
OpenStreetMap
OpenStreetMap is a collaborative platform for creating and sharing geographic data. The data generated by users is freely accessible and can be utilized for a variety of spatial analysis tasks. OpenStreetMap’s datasets are valuable for urban planning, navigation, and research on geographic phenomena.
Government and Economic Data
Government and economic data are essential for conducting research, informing policy decisions, and understanding social and economic trends within populations.
US Census Bureau
The US Census Bureau provides extensive demographic and economic data that are crucial for researchers and policymakers. Data.census.gov centralizes access to this information, making it easier to find and use. Tools are available to explore a wide range of demographic and economic metrics.
World Bank Open Data
The World Bank provides datasets on global development and project costs. These datasets cover various aspects of economic development, enabling thorough analysis of worldwide issues. World Bank datasets are accessible without registration and are regularly updated to reflect current global economic trends.
Data.gov
Data.gov is a platform that provides access to datasets from multiple U.S. government agencies. No registration is required to browse datasets, which include information on government budgets, school performance scores, and chronic disease indicators. Data.gov is an excellent resource for accessing a wide range of public data and valuable research materials.
Specialized Datasets for Machine Learning Projects
For those specifically interested in machine learning, finding the right datasets is critical for training and optimizing algorithms. Specialized datasets can greatly enhance the accuracy and performance of machine learning models by providing relevant and context-specific data.
FiveThirtyEight
FiveThirtyEight is an interactive news and sports site focused on data journalism and unique subjects. Its datasets cover a variety of topics, including sports, politics, and science, making them valuable for diverse machine learning projects.
For example, the “Study Drugs” dataset provides open data on Adderall use. Accessing these datasets is straightforward, with users able to sign up with their email to receive the newsletter.
OpenML
OpenML is an open platform that allows users to share machine learning datasets and models across various domains. It hosts datasets for image classification, natural language processing, and social sciences, all contributed by the community.
OpenML also offers features to replicate experiments, compare models, and contribute to projects, making it a valuable resource for machine learning enthusiasts.
Academic Torrents
Academic Torrents shares datasets from scientific papers, providing high-quality data for research. A torrent client is required to download the datasets. This platform ensures that researchers can access large datasets that are often difficult to find and download due to their size and complexity.
Personal Data Projects
Personal data projects offer unique insights into individual habits and preferences. Many people use their personal data to uncover trends in their behavior, creating distinctive data science projects that can help predict future actions.
Facebook Activity Data
Facebook allows users to download their personal activity data for analysis. This comprehensive archive includes posts, comments, likes, and more, enabling users to evaluate their interactions and engagement patterns on the platform. Analyzing this data provides a deeper understanding of social media behavior.
Amazon Order History
Users can access their complete Amazon purchase history to evaluate spending patterns and identify shopping trends. This data allows users to review their purchase history and examine spending habits over time, providing valuable insights into personal consumption patterns.
Tools for Finding and Managing Datasets
Finding and managing datasets can be challenging, but several tools simplify the process. These tools provide advanced search capabilities, collaborative features, and powerful data analysis functionalities to help users locate and work with data efficiently.
DataHub
DataHub is a SaaS data-publishing platform for browsing public datasets. It provides easy access to and management of datasets, along with helpful documentation and tutorials for creating visualizations.
DataHub’s dataset management features make it a valuable resource for data scientists looking to organize and analyze data efficiently.
data.world
Data.world is a collaborative platform for finding, sharing, and analyzing datasets. It offers advanced filtering for more precise dataset searches and provides powerful tools for exploring and working with data, making it a versatile resource for data scientists.
GitHub
GitHub provides an opportunity to build a data science portfolio by hosting a variety of datasets for analysis. Users can filter results by programming language and keywords, making it easier to find relevant datasets. GitHub also offers an API for accessing repository activity and code, enabling efficient data retrieval.
Summary
Access to a diverse array of datasets is essential for data science projects. From essential datasets on platforms like Kaggle and the UCI Machine Learning Repository to publicly available big data sets from AWS and Google Cloud, this article has provided a variety of valuable data sources. We also explored specialized datasets for machine learning, health and social impact, climate and environmental data, and government and economic data, ensuring you have strong tools for your projects.
By utilizing these datasets, you can create data visualizations, conduct insightful analyses, and develop innovative solutions to real-world problems. Whether you are a beginner or an experienced data scientist, these resources will help you grow your skills and achieve your project goals.
