Guide · Open source
How to use awesome-datascience: a guided list of data science resources
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-datascience is a community-maintained list of links about data science, published on GitHub under the MIT licence. It is a starting path for people who want to learn what data science is and what to study. After this guide, you will know how to get a local copy of the list, find the sections that match your goal, and judge which linked resources to try first.
awesome-datascienceacademic/awesome-datascience · ★ 30.3k · MIT Install options & downloads Quick start
- 1Clone the repository. Run the git clone command from the README to copy the list to your computer.
- 2Open the README. Read the introduction to see what data science is and how the list is organized.
- 3Pick a section. Use the table of contents to choose the group that fits your goal, such as Training Resources or Books.
- 4Start with the introductory links. Work through the What is Data Science table, which links to beginner-level articles and a curriculum.
- 5Verify each resource. Open each link and check its level, content and availability before you commit time to it.
What is awesome-datascience?
The repository is a curated index of other resources rather than a software program. Its README answers the question "What is Data Science, and what should I study to learn Data Science?" by pointing to articles, courses, books and tools.
The README opens with a short explanation of data science and a table of introductory links, including a Microsoft curriculum called Data Science For Beginners and articles from O'Reilly, Quora, Harvard Business Review and Wikipedia.
Get a local copy of the repository
The source gives one install command: clone the repository with git. This copies the README and its files to your computer so you can read them offline or browse the folder structure.
Because the list is only links, there is nothing to configure or run after cloning. You can also open the README directly on GitHub without cloning.
git clone https://github.com/academic/awesome-datascienceFind the section that matches your goal
The README has a table of contents with the main groups. Use it to jump to the part you need instead of reading every link.
Training Resources lists tutorials, free courses, MOOCs, intensive programs and colleges. The Data Science Toolbox covers algorithms, general machine learning packages, deep learning packages (PyTorch, TensorFlow and Keras ecosystems), visualization tools and miscellaneous tools.
Literature and Media covers books, journals, newsletters, bloggers, presentations, podcasts and YouTube channels. Socialize lists communities such as Telegram channels, Slack communities and GitHub groups. Fun includes infographics, datasets and comics.
- New to the field: start with What is Data Science? and Where do I Start?
- Want free courses: go to Free Courses and Massively Open Online Courses.
- Want an overview of tools: go to The Data Science Toolbox.
- Want community channels or podcasts: go to Socialize and Podcasts.
Choose resources and check them yourself
Each entry is a link with a short description or none at all. Open the linked page to confirm what it covers, its level and whether it is still available.
The README does not describe how entries are reviewed for currency, so treat any listed course, book or link as a starting point to verify rather than a guaranteed current resource.
Contribute or report a problem
The README says contributions are welcome and points to CONTRIBUTING.md in the repository for how to take part. Use that file as the guide for proposing changes.
What people use it for
- A beginner choosing a first set of articles about what data scientists do.
- A student collecting free courses and tutorials to explore before buying textbooks.
- Someone scanning the Data Science Toolbox to see which machine learning and deep learning packages exist.
- A reader looking for podcasts, newsletters or community channels on data science.
Frequently asked questions
How do I install awesome-datascience?
The README gives one command: git clone https://github.com/academic/awesome-datascience. This downloads the repository to your computer. There is no separate installer because the project is a list of links.
What are the requirements for awesome-datascience?
The README lists no prerequisites. You need git only if you want to clone the repository; otherwise you can read the list on GitHub in a browser.
Is awesome-datascience free and what is its licence?
The project is listed under the MIT licence. The linked resources have their own terms, so check each one before use.
Is awesome-datascience a software tool I can run?
No. The README is a curated index of other resources rather than a software program, so there is nothing to run. It is useful for finding what to learn and which tools to explore.
What are alternatives to awesome-datascience?
The source names Awesome Machine Learning and Data-Science-For-Beginners as comparable projects. Data-Science-For-Beginners is a 20-lesson curriculum, while awesome-datascience is a broader list of links across courses, books, tools and media.
Are the courses and links in awesome-datascience up to date?
The README does not explain how entries are reviewed for currency. Open each link to confirm it still works and matches what you need.
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Our verdict
awesome-datascience suits beginners and students who want a guided set of free links to start learning data science and to see which tools and communities exist. It is not suitable if you need a single program to install, verified and current course quality, or advanced technical material, so pick a dedicated course or tool for those needs.
Install options & downloads