📘 10 Free Data Science Books (and what you’ll learn 👇) 🚀
A free collection of books to learn data science from theory to practice. Includes concepts, programming (Python and R), statistics, and useful tools.
📚 Books, downloads, and what each contains#
🌟 Veridical Data Science — https://vdsbook.com/
Contents: Introduction to the data science project lifecycle, data exploration, and prediction.📊 Data Science: Theories, Models, Algorithms, and Analytics — https://srdas.github.io/MLBook/index.html
Contents: Core concepts, visualization, data handling, statistics, machine learning, and advanced applications.🐍 Think Python (3E) — https://allendowney.github.io/ThinkPython/
Contents: Python programming fundamentals, control flow, data structures, and object-oriented programming.🐍 Python Data Science Handbook — https://github.com/jakevdp/PythonDataScienceHandbook
Contents: Key Python tools for data science: NumPy, Pandas, plotting with Matplotlib, and basic machine learning.📈 R for Data Science — https://r4ds.hadley.nz/
Contents: Using R for analysis, visualization, data manipulation, and transformation.📉 Think Stats (3E) — https://allendowney.github.io/ThinkStats/
Contents: Practical statistics for data science: exploratory analysis, probability, regression, and statistical models.📊 Statistics and Prediction Algorithms Through Case Studies — https://rafalab.dfci.harvard.edu/dsbook-part-2/
Contents: Applied statistics and predictive algorithms with examples (useful even if you don’t use R).📡 Probabilistic Programming & Bayesian Methods for Hackers — https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
Contents: Bayesian methods and probabilistic programming with PyMC and practical examples.🔢 Think Bayes (2E) — https://allendowney.github.io/ThinkBayes2/
Contents: Practical approach to Bayesian statistics with Python code and real-world applications.💻 Data Science at the Command Line — https://jeroenjanssens.com/dsatcl/
Contents: How to use the command line (shell/UNIX) to manipulate, clean, explore, and automate data tasks.
💡 Quick summary#
If you’re starting in data science:
- 🧠 Concepts and theory: the first books explain what data science is and how models work.
- 🐍 Python programming: learn to write code to analyze data, from basics to popular libraries.
- 📊 Statistics: understanding numbers, probability, and predictive models is key for data-driven decisions.
- 📈 R and visualization: some guides focus on R, another widely used language for analysis.
- 💻 Command-line tools and workflows: learn tools that speed up daily data science work.
👉 All of these resources are free and accessible online.
More information at the link 👇
