Data Science for Beginners vs 
Applied ML
Data Science for Beginners vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. The data foundations before any ML vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
Data Science for Beginners vs Applied ML at a glance
| Spec | Data Science for Beginners | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Curriculum (10 weeks) | Curated papers & posts |
| License | MIT | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Markdown |
| Ease of use | Beginner | Intermediate |
| Best for | building the foundations ML courses skip | learning from what companies really did |
| GitHub stars | — | 29.9k |
How Data Science for Beginners and Applied ML score
| Criterion | Data Science for Beginners | Applied ML |
|---|---|---|
| Popularity | n/a | 3.5 |
| Maintenance | n/a | 2.0 |
| Ease of use | 5.0 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
Data Science for Beginners
A 10-week Microsoft curriculum on data science fundamentals: statistics, data wrangling, visualisation and ethics — the groundwork most ML courses assume you already have.
- Covers what ML courses assume you know
- Strong on data ethics, rarely taught
- Sketchnotes make concepts stick
Applied ML
Eugene Yan's curated collection of papers and engineering blog posts on how companies actually build and deploy ML systems in production — organised by problem, not by algorithm.
- Real production systems, not toy examples
- Organised by problem, not by algorithm
- Curated by a practising ML engineer
Key differences
Data Science for Beginners is curriculum (10 weeks), while Applied ML is curated papers & posts. Data Science for Beginners leans more beginner-friendly, whereas Applied ML is more suited to intermediate users. In short, Data Science for Beginners fits building the foundations ML courses skip, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose Data Science for Beginners for building the foundations ML courses skip. Choose Applied ML for learning from what companies really did.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is Data Science for Beginners or Applied ML easier to use?
Data Science for Beginners is generally the easier of the two to get started with, while Applied ML rewards more setup with more control.
Are Data Science for Beginners and Applied ML free?
Data Science for Beginners is free and open source (MIT), and Applied ML is free and open source (MIT). Neither charges for the core software.
Can I run Data Science for Beginners and Applied ML locally?
Data Science for Beginners: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Data Science for Beginners vs Applied ML — which should I pick in 2026?
Choose Data Science for Beginners for building the foundations ML courses skip. Choose Applied ML for learning from what companies really did.
People also compare
Explore more open-source AI
Browse thousands of open-source AI tools, models and projects — all curated in one place, updated daily.
Explore the directory →