# Must-Read Free Books for Data Science

# Must-Read Free Books for Data Science

### Want to learn about Data Science but don't know where to start? No worries! Here's a list of free books and resources form experts on the topic.

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Earlier, we came up with a list of some of the best Machine Learning books that you should consider reading 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, *Foundations of Data Science* is a great blend of lectures in the modern theoretical course in data science.

## UFLDL Tutorial

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

## Python Data Science Handbook

The *Python Data Science Handbook* 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

*Hands-On Machine Learning and Big Data* by Kareem Alkaseer is a great source for 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

*EE263: Introduction to Linear Dynamical Systems *by Professor Sanjay emphasizes applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. A link to previous years’ course notes by Professor Boyd can be found here.

## Convex Optimization: Boyd and Vandenberghe

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

## Essentials of Metaheuristics

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

## A Course in Machine Learning

A Course in Machine Learning (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 that highly recommend. If we missed out something, let us know. Comment below and share your list of favorite books for data science.

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