Data Science

Cleaning, analyzing, and communicating data with Python or R.

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16 picks · 10 free · 13 providers · curated by Rowland Kuru

Editor's intro

Editor's intro

Data science is less about fancy models than most course marketing suggests. Day to day, it's getting messy data into shape, asking a sharp question, answering it honestly, and explaining the answer to people who don't read code. This feed weights its picks accordingly: data wrangling and communication get as much space as machine learning.

Pick a language first — Python if you also want to build software or do machine learning, R if you're headed toward research or statistics-heavy work. Learn one data library deeply, then practise on real datasets every week. The certificates here are useful for career changers; the free books are what working data scientists actually reread.

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Python for Data Analysis, 3rd Edition

Wes McKinney Book Free Free to read online

Python for Data Analysis is written by Wes McKinney, the creator of pandas, and it's free to read online in its third edition. It teaches the tools you'll use every single day — NumPy arrays, pandas DataFrames, grouping, reshaping, time series, and basic plotting — with realistic examples rather than toy ones. Read it alongside a Jupyter notebook and retype the examples on a dataset you care about. It's not a statistics book, so pair it with the Statistics feed once you're comfortable.

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Beginner · 7

Loading, cleaning, summarizing, and charting data. Choose Python or R and stick with it for now.

Intermediate · 5

Full analysis projects, communicating results, and the statistics that keep conclusions honest.

Advanced · 3

Causal inference, experimentation, and the data engineering that feeds everything else.

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