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Data Science Books to learn for Free and Paid

General By DataGyan Team · July 7, 2022 · 13,497 Comments

As the world gets more data-savvy, the need to make sense of all that has increased many folds. What started as the “Sexiest Job of the 21st century” is now being implemented across verticals and industries, thanks to rapid development in research, tools and technology. If you are new to the field of data science and would like to start somewhere but don’t know where here is a list of data science books to read in order to know what the field is about and perhaps know how you can make a career out of it.

Beginners

If you are a beginner and have not done any hands-on data science before, your first step should be to know what this field is all about. Here are some data science books that you can read to know about the subject. Do remember that the aim here is to know what this field is all about and not get into jargon and technicals right away. We will get to that in the Intermediate and Advanced sections;

Data Science for Dummies

This book touches upon what data science is and as it reads, it’s for people who have little to no knowledge of what this field is about. You can get your hands on it and understand what it is to work on data science problems and build solutions that help companies make data-driven decisions.

Python Data Science Handbook – Free

Although there are tools that can be used to do analysis without programming, for hardcore data science and deeper research you would need to code. If there is one programming language you have to learn and master if you want to do Hands-on data science, it is python. The language is easier compared to other programming languages and comes with plenty of pre-built packages and a supporting developer community. This book by Jake VanderPlas, covers the python you would need for data science work and is a good and broad introduction to the Python data science toolkit. It covers an introduction to the NumPy library including concepts such as arrays, computations on arrays and data types in Python. It also gives a good, beginner-friendly introduction to data analysis with Pandas and machine learning with Scikit-learn. Grab it for free here! And, if you are new to python totally, it is better to read ‘Learn Python the right way‘ before reading the Data science handbook.

ISLR & ESLR – Free

The ISLR & ESLR bundle is considered one of the best books to read if you want to know the fundamentals of Machine learning, statistics and how optimization functions are determined. The book covers the fundamentals needed and is a great read if you want to know the theory behind basic algorithms. There are also coding examples in R for you to code along. You can read the book here.

ESLR on the other hand delves deep into the mathematical foundations with no coding at all. It gives you a strong idea of why many models work the way they work, along with the how. It can be considered as the book that you read once you get a good grip on ISLR. If you do not have a good foundation in Statistics and Linear Algebra, and if you are new to the field of Data Science, reading this directly will be quite challenging and you may drop off. You can read the book here.

Practical Statistics for Data Science

This book by Peter & Andrew bruce talks about statistics as used in the real world by data scientists. Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. You can grab the book here.

Think Bayes

The age-old debate between frequentists vs bayesian’s might continue for a long time, and it only gets bigger every day. Most statistics books cover Machine Learning from a frequentist standpoint and are in fact considered successful compared to Bayesian methods due to computation and algorithmic capabilities. Bayesian methods are used in other areas as well, and if you would like to understand the basics behind them using Python, then this book is a good read.

Mathematics for Machine Learning – Free

Mathematics for Machine learning Data science book

This free book covers the mathematics behind Machine learning and could very well take you back to your school days. What is covered are the fundamental mathematical tools needed to understand machine learning including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. If you are someone who wants to know the math behind algorithms and are keen on research, then this book has to be on your list.

Intermediate

In this section, we will look at books that cover the concepts as well as some code snippets on what the Data Science process and development look like,

Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow

HandsOn Machine Learning with Scikit-learn, Keras & TensorFlow Data science book

This book by Geron is a widely read book by developers, and data scientists who know the basics of analytics and data science but are looking to build data science pipelines and models and put them into production in the real world. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow, this book helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. You will learn how to use scikit-learn to build end-to-end ML models and also use TensorFlow to build neural networks.

Hands-on Unsupervised Learning using Python

Unsupervised learning is a critical subset of machine learning and plays a huge role in the data science field. Not all problems come with labels and some problems need techniques to be implemented that don’t need the targets. Unsupervised methods help you here. This book by Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks scikit-learn and TensorFlow using Keras. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. You will learn about dimensionality reduction, clustering and auto-encoders that you can learn and implement in the real world. Grab the book here.

Machine Learning with Pytorch and Scikit-learn

This book is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you’ll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. You also learn about PyTorch, one of the widely used Deep learning frameworks and also scikit-learn and build ML models and pipelines using them.

Feature Engineering for Machine Learning

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. It is one of the important steps that could differentiate a great model from an average one. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.

Rather than simply teaching these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including NumPy, Pandas, Scikit-Learn, and Matplotlib are used in code examples. This book also covers feature engineering for texts, images and tabular data. Grab the book here.

Advanced

In the section above, we already saw books that covered topics like deep learning, but they were mostly hands-on, or directly tackled a problem. This section is for books that are purely for deep learning theory and applications.

Deep Learning Book – Free

Easily the best Deep learning book out there and it is free. A highly recommended book by the practitioners of the field and including the likes of Elon Musk, who felt this book to be the best book about deep learning. If you want to know the basics behind deep learning and how Neural networks work, grab this book.

Deep Learning with Pytorch

Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away on building a tumour image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. The best part is that this book is written by PyTorch’s creator and key contributor. You will learn the PyTorch Tensor API, loading data in Python, and visualise results, along with implementing modules and loss functions and utilizing pre-trained models from PyTorch Hub.

Machine Learning with Tensorflow Cookbook

Machine Learning using Tensorflow Cookbook

The previous data science books covered concepts in Deep learning using Pytorch but the independent recipes in this book will teach you how to perform complex data computations and gain valuable insights into your data and dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning – each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. With this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.

Deep Learning for Coders with Fastai and Pytorch

Deep Learning for Coders with Fastai & Pytorch Data Science Book

So far in all the data science books we touched upon, you would have noticed that they cover frameworks alongside theory to an extent. The frameworks are also at a lower level compared to Fastai which is what this book is about. Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. You will train models in computer vision, natural language processing, tabular data, and collaborative filtering and also discover how to turn your models into web applications. What are you waiting for? Grab the book and train those models NOW!!

Natural Language Processing with Transformers

Transformers(Not the movie) have changed deep learning and machine learning in a huge way and they have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you’re a data scientist or machine learning engineer, this practical book shows you how to train and scale these large models using HuggingFace Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf use a hands-on approach to teach you how Transformers work and how to integrate them into your applications. You’ll quickly learn a variety of tasks that can help you solve, build, debug, and optimize Transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering. You will also learn how transformers can be used for cross-lingual transfer learning and apply transformers in real-world scenarios where labelled data is scarce. Why wait, grab your copy and be on your way to building those powerful AI applications.

So, these were your list of books to read to understand the field of Data Science and AI, from beginner to advanced. We hope this list was useful. Happy reading!

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