Data science is an interdisciplinary field that contains methods and techniques from fields like statistics, machine learning, Bayesian, etc. Today we're gonna sort out something like Huge List of Free Artificial Intelligence, Machine Learning, Data Science & Python E-Books. We will list out free data science books that might help you in starting your data science career or improving yourself as a data scientist. All data science books are available in the PDF or HTML format. Download it and enjoy learning.

Starting with...

1.

Python Data Science Handbook explains the application of various Data Science concepts in Python. Probably the best book to learn Data Science in Python, this book is also to download. So you can learn without spending any money.

Download Link: Python Data Science Handbook Ebook

2.

With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.

Download Link: Think Bayes Ebook

3.

Applied Data Science by Langmore and Krasner is a book that covers a practical approach to teach Data Science. From using Git, teaching Basic Python, the book goes on to build fundamentals of various algorithms that are used frequently in the field of Data Science.

Download Link: Applied Data Science Ebook

4.

The Statistical inference for data science book is for the students who are numerically and computationally literate, who would like to put those skills to use in Data Science or Statistics.

Download Link: Statistical Inference for Data Science Ebook

5.

This self-contained textbook bridges the gap between mathematical and machine learning texts, injecting the mathematical concepts with a minimum of necessities.

Download Link: Mathematics for Machine Learning Ebook

6.

This book gives a summary of Data Science. Data Science is a very large Bumbershoot term and this book is good for anyone trying to get their feet wet in the field for the first time. Read it to understand what Data Science is, what are some general tasks and algorithms, and some general tips and tricks.

Download Link: The Element of Data Analytic Style Ebook

7.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counter intuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing.

Download Link: Foundations of Data Science Ebook

8.

Starting with...

1.

**Python Data Science Handbook**Download Link: Python Data Science Handbook Ebook

2.

**Think Bayes**Download Link: Think Bayes Ebook

3.

**Applied Data Science**Download Link: Applied Data Science Ebook

4.

**Statistical Inference for Data Science**The Statistical inference for data science book is for the students who are numerically and computationally literate, who would like to put those skills to use in Data Science or Statistics.

Download Link: Statistical Inference for Data Science Ebook

5.

**Mathematics for Machine Learning**Download Link: Mathematics for Machine Learning Ebook

6.

**The Element of Data Analytic Style**Download Link: The Element of Data Analytic Style Ebook

7.

**Foundations of Data Science**Download Link: Foundations of Data Science Ebook

8.

**Convex Optimization**
The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

Download Link: Convex Optimization Ebook

9.

**Computer Age Statistical Inference: Algorithms, Evidence, and Data Science**
This book begins with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of some great topics: survival analysis, logistic regression, random forests, empirical Bayes, Markov chain Monte Carlo, & more.

10.

**Causal Inference by James Robins**
The application of causal inference methods is growing exponentially in fields that deal with observational data. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Download Link: Casual Inference Ebook

11.

**Algebra, Topology, Differential Calculus and Optimization Theory for Computer Science and Machine Learning**
The book tries to explain various mathematical domains required in Computer Science and Machine Learning. Quite Mathematical and a good resource for those who want to come into Data Science from Maths Perspective

Download Link: Algebra, Topology, Differential Calculus and Optimization Theory for Computer Science and Machine Learning

12.

**Data Mining and Analysis**
This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main focus of this book is on different topics which includes exploratory data analysis, pattern mining, clustering, and classification.

Download Link: Data Mining and Analysis Ebook

13.

**Computational and Inferential Thinking by Ani Adhikari and John DeNero**
It dives into various aspects of Data Science from programming in Python, Causality, Tables, Visualization and basic statistics. As this book is from a basic course at UC Berkeley, so a good resource for beginners.

Download Link: Computational and Inferential Thinking Ebook

14.

**Information Theory for Intelligent People**
Information theory is 1 of the 4 mathematical theories you will find in Data Science along with Linear Algebra, Convex Optimization, and Statistics. This is an intro guide that will help you to understand the theory. The amazing thing about this tutorial is it is for beginners and intermediates.

Download Link: Information Theory for Intelligent People Ebook

15.

**Scipy Lecture Notes**
If you are going to work in Data Science, you will need to study the scientific Python stack. This ebook is one of the best to learn Numpy, Scipy, Scikit-Learn, Scikit-Image, and all the libraries you need.

Download Link: Scipy Lecture Notes

16.

**Pandas: Powerful Python Data Analysis Toolkit/ (Pandas Mega Tutorial)**
This huge tutorial is by the Pandas development team to learn and understand the library.

Download Link: Pandas: Powerful Python Data Analysis Toolkit Ebook

17.

**Advanced Data analysis From an Elementary Point of View**

A quick guide to different concepts of Data Science including causal models, regression models, factor models, and so on. The sample programs are in R.

18.

**Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language**
Python Data Analytics Book will help you to winch the world of data acquisition & analysis using the Python Programming language.

Download Link: Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language Ebook

19.

**A Genetic Algorithm Tutorial**
Genetic Algorithms are tools that all Data Scientists need to use sometime in their life. This tutorial helps beginners understand how Genetic Algorithms work.

Download Link: A Genetic Algorithm Tutorial Ebook

20.

**Data Science: Theories, Models, Algorithms, and Analytics**
This book provides a bucket full of information regarding Data Science. The book covers a wide variety of sections by giving access to theories, data science algorithms, tools and analytics. Some highlighting contents of the book are Open Source: Modelling in R to Bayes Theorem.

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