awesome-datascience vs Data Science for Beginners
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A curated list of links for learning data science, from courses and books to tools and datasets.
Open source · MIT
A free 10-week, 20-lesson beginner data science curriculum from Microsoft, with quizzes and assignments.
The short answer
Choose awesome-datascience if you need…
- Beginners who want a guided set of links to start learning data science
- Students looking for free courses, books, and tutorials to explore
- People who want an overview of data science tools such as machine learning and deep learning packages
- Readers who want to find community channels, newsletters, or podcasts on the subject
Choose Data Science for Beginners if you need…
- Complete beginners who want a guided, week-by-week data science course
- Students looking for free structured coursework
- Learners who prefer hands-on, project-based lessons over videos
- People who want to work through material in English or one of the many listed translations
Side-by-side
| awesome-datascience | Data Science for Beginners | |
|---|---|---|
| Type | Curated list | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 30.3k | 37.9k |
| Setup difficulty | Beginner | Beginner |
| How you get it | See the README | Clone and build from source |
| Main language | — | Jupyter Notebook |
| Last code update | 5 days ago | 16 days ago |
What each one is
awesome-datascience
This is a community-maintained list of links about data science, organized into sections such as courses, books, tools, podcasts, and datasets. It is meant as a starting path for people who want to learn what data science is and what to study. The README is a curated index of other resources rather than a software program.
Data Science for Beginners
This is a structured course in data science for people who are new to the field. It is organized as 20 lessons spread over 10 weeks, and each lesson includes pre- and post-lesson quizzes, written instructions, a solution, and an assignment. The course uses a project-based approach, so you learn by building things.
Best for
awesome-datascience
- Beginners who want a guided set of links to start learning data science
- Students looking for free courses, books, and tutorials to explore
- People who want an overview of data science tools such as machine learning and deep learning packages
- Readers who want to find community channels, newsletters, or podcasts on the subject
Data Science for Beginners
- Complete beginners who want a guided, week-by-week data science course
- Students looking for free structured coursework
- Learners who prefer hands-on, project-based lessons over videos
- People who want to work through material in English or one of the many listed translations
Strengths
awesome-datascience
- Broad coverage of topics in one place, from career background to specific tools
- Open source under the MIT license and accepts contributions via CONTRIBUTING.md
- Organized into a table of contents that makes it easy to jump to a section
Data Science for Beginners
- Free and MIT-licensed, so it can be reused and adapted
- Each lesson pairs instruction with quizzes and assignments
- Available in a large number of languages through the listed translations
Not ideal for
awesome-datascience
- People who need a single tool or program to install and use, since this is only a list of links
- Readers who need verified, up-to-date course or resource quality, because the README does not show how entries are reviewed for currency
- Advanced practitioners looking for in-depth technical material beyond introductory-level pointers
Data Science for Beginners
- Experienced data scientists looking for advanced techniques, since the README describes it as a beginner curriculum
- People who need a live instructor or cohort, since the README only describes self-paced written lessons and community channels
- Anyone expecting a software product to install, since this is a set of lesson materials
Trade-offs
awesome-datascience
- It is a list of external links, so the quality and availability of each linked resource depends on those third parties
- The README is truncated in the provided text, so the full set of sections could not be fully checked here
- Some intro content includes sponsor banners and affiliated book deal links, which readers should weigh
Data Science for Beginners
- The README gives little detail on prerequisites or the exact topics covered in each lesson
- The full repository is large because of the many translations, though sparse checkout is offered to reduce download size
Getting started
awesome-datascience
Clone the repository
git clone https://github.com/academic/awesome-datascienceData Science for Beginners
Clone without translations (Bash / macOS / Linux)
git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.gitEnter the folder and set sparse checkout (Bash / macOS / Linux)
cd Data-Science-For-Beginners && git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'Frequently asked questions
What is the difference between awesome-datascience and Data Science for Beginners?
awesome-datascience: A curated list of links for learning data science, from courses and books to tools and datasets. Data Science for Beginners: A free 10-week, 20-lesson beginner data science curriculum from Microsoft, with quizzes and assignments. awesome-datascience suits beginners who want a guided set of links to start learning data science, while Data Science for Beginners suits complete beginners who want a guided, week-by-week data science course.
Are awesome-datascience and Data Science for Beginners free?
awesome-datascience: Free and open source. Data Science for Beginners: Free and open source.
Is awesome-datascience or Data Science for Beginners more popular?
Data Science for Beginners has more GitHub stars (37.9k) than awesome-datascience (30.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to awesome-datascience and Data Science for Beginners?
Other options people compare them with include Python Data Science Handbook.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.