5 Free Books to Master Data Science

#1 “A Whirlwind Tour of Python”

If you are interested in starting to learn Python without taking too much time, this book can be a good match for you. It gives a very short overview of Python’s basic concepts. Together with the 100-page book, there is also a GitHub repository with exercises. 

In particular, you can quickly learn the principal data types of Python: integers, floating-point numbers, strings, Booleans, lists, tuples, dictionaries and sets. At the end of the book, there is a brief overview of Python libraries, NumPy, Pandas, Matplotlib, Scipy.

It covers the following content:

Basic Syntax, Variables, Operators, Principal Data Types, For Loop, While loop, Functions, If-elif-else, Fast overview of Python libraries

#2 “Think Stats: Probability and Statistics for Programmers”

 It can be hard to acquire a good knowledge of probability and statistics without putting into practice what you study.  The beauty of this book is that it’s focused on a few basic concepts and doesn’t only show theory, but there are also practical exercises written with Python. 

The book covers:

Summary Statistics, Data Distribution, Probability Distributions, Bayes’s Theorem, Central limit theorem, Hypothesis testing, Estimation

#3 “Introduction to Linear Algebra for Applied Machine Learning with Python”

When you study Linear Algebra in university, most of the time the professors explain all the theory without any practical application. So, you end up taking the exam, and forget every concept once you are done, because in your head it’s too abstract. 

Luckily, I have found this amazing book that gives you a good introduction of linear algebra’s fundamentals that you’ll meet when you study machine learning models. Every theoretical concept is followed by a practical example written with NumPy, a well-known Python library for scientific computing.

These are the main topics covered: Vectors, Matrices, Projections, Determinant, Eigenvectors and Eigenvalues, Singular Value Decomposition  

#4 “Introduction to Machine Learning with Python”

After studying Python, Statistics and Linear Algebra, it’s time to finally learn everything about Machine Learning models to solve real-world problems. The book is suggested for people getting started and uses scikit-learn for the machine learning applications. 

These are the main machine learning models explained:

Linear Regression , Naïve Bayes, Decision Trees , Ensembles of Decision Trees, Support Vector Machines, Principal Component Analysis, t-SNE, K-Means Clustering, DBSCAN

#5 “Deep Learning with Python”

 This fifth and last book was conceived for people that already have Python programming knowledge and no prior experience with machine learning is required. The author of this book is Francois Chollet, a software engineer and AI researcher at Google, famous for creating Keras, a deep learning library released in 2015.

These are the most important notions:

Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTM, Generative Adversarial Networks

 “Final thoughts”

These recommendations are invaluable for beginners aspiring to enter the realm of data science. Moreover, they prove incredibly helpful for experienced data scientists and researchers aiming to fill gaps in their knowledge and enhance their understanding of certain concepts. I trust you find this list of books valuable and enriching for your learning journey.