Earlier, we came up with a list of some of the best Machine Learning books you should consider going through. In this article, we have come up with yet another list of the recommended books for Data Science.

### Foundations of Data Science

Written by Blum, Hopcroft and Kannan, this book for data science is a great blend of lectures in the modern theoretical course in data science.

### UFLDL Tutorial

This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning.

### Python Data Science Handbook

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.

### Hands-On Machine Learning and Big Data

This book by Kareem Alkaseer is a great source of learning the concepts of Machine Learning and Big Data.

### Think Stats

Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. This one of the most recommended books for data science.

### Think Bayes

Think Bayes is an introduction to Bayesian statistics using computational methods. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics.

### EE263: Introduction to Linear Dynamical Systems

This compilation by Professor Sanjay emphasises on applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Link to previous years’ course notes by professor Boyd can be found here.

### Convex Optimization – Boyd and Vandenberghe

This book provides a comprehensive introduction to the subject and shows in detail how such problems can be solved numerically with great efficiency.

### Essentials of Metaheuristics

This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts.

### CIML

CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

These are the books for data science we highly recommend. If we missed out something, let us know. Comment below and share your list of favorite books for data science.

Great list. Just wanted to add that the Hopcroft and Kannan book has been updated since the 2014 edition you linked to here, to this 2016 version, with an additional author (Blum): https://www.cs.cornell.edu/jeh/book2016June9.pdf