"Python for Data Science Handbook" by Jake VanderPlas:– This book focuses on using Python for various aspects of data science, including data manipulation, visualization, and machine learning.
"Think Stats" by Allen B. Downey:– A great resource for understanding statistical concepts in the context of data science. It uses Python for practical examples.
"R for Data Science" by Hadley Wickham & Garrett Grolemund:– If you're interested in using R for data science, this book is a comprehensive guide covering data manipulation, visualization, and modeling.
"An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani:– While not entirely free, this book is available online for free and provides a solid introduction to statistical learning techniques using R.
"Introduction to Data Science" by Jeffrey Stanton:– This book covers the basics of data science, including data exploration, visualization, and statistical analysis.
"Data Science at the Command Line" by Jeroen Janssens:– A unique book that teaches data science using the command line. It covers practical tools and techniques for handling data.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville:– This comprehensive book is available for free online and covers deep learning concepts. It's a bit more advanced but a valuable resource.
"Data Science for Business" by Foster Provost and Tom Fawcett:– While not entirely free, the authors have made the slides and materials freely available online. It's a great resource for understanding the business applications of data science.
"Bayesian Methods for Hackers" by Cameron Davidson-Pilon:– This book introduces Bayesian statistical methods using practical examples and Python code.