Become a Data Scientist: Read these 10 best books for Data Science

best books for data science

Table of Contents

Data plays a crucial role in making complex business decisions. Every company aims at making data-driven decisions to increase the accuracy of predicting factors like market trends, sales, competition and many more. This generates a massive need for people who can handle the data and draw insights.

That’s where the Data Scientist comes in. They evaluate, process, and model data before interpreting the results to develop actionable plans for businesses and other groups.

According to the World Economic Forum, Data Science is going to be the most demanding job in the next 5 years.

Analysts predict that the country will have more than 11 million job openings by 2026. In fact, since 2019, hiring in the Data Science industry has increased by 46%.

Harvard Business School called Data Science “the sexiest job of the 21st century.”

If all those factors above made you curious about this “sexy” field of Data Science, you may want to know more about it and how one can become a Data Scientist.

We have compiled a list of some of the best books to learn Data Science. The list contains Data Science books for beginners, intermediate and advanced levels. These books will surely help you kickstart your Data Science journey.

Key Takeaways

  • Get the list of best books to read for Data Science for all levels: Beginner, Intermediate and Advanced
  • Get to know about all the essential tools and skills required to become a Data Scientist.
  • Books alone won’t make you a good Data Scientist, you need some Hands-on experience and internship training to achieve this goal.

How to Choose the Right book for data science?

There are thousands of books for Data Science, but you can’t just pick any random book and start reading. Many of these books cover advanced topics that may be intimidating to newcomers.

You should select a book based on your level of comprehension.

We have divided the list of the books into 3 parts :

  • Beginners
  • Intermediate
  • Advanced 

This will help you pick a book according to your calibre.


Data Science books for beginners will give you a brief introduction to the various core concepts of Data Science. They cover every important topic and explain everything from the ground up. 


If you have some prior knowledge of programming languages or have finished the introductory books, you can move on to these books. These books to learn Data Science elaborate the core concepts in more depth.


These books can help you to understand all the essential subjects, such as Machine Learning and Deep Learning if you have experience with data crunching and it’s time to upgrade your game.

Check out this step-by-step guide on How to Become a Data Scientist

10 best Data Science books

Here is the list of the 10 best books for Data Science. We have highlighted the crucial topics that these books include and what makes each book different from another.

So without any further delay, let us get started.

Beginner level:

1. Data Analytics Made Accessible by Dr Anil Maheshwari

Data Analytics Made accessible by Dr Anil Maheshwari is a beginner-friendly book that covers almost all the topics required for Data Mining. 

It provides a fairly comprehensive grand tour of all the major topics and considerations on the subject of Data Analysis. This book primarily focuses on Data Privacy, Big Data and Data Mining and gives tutorials for R and Python.

This book includes :

  1. Data Privacy, Big Data, Artificial Intelligence and Data Mining.
  2. Tutorials of R and Python for beginners.
  3. Concepts of Business Intelligence

2. Numsense! Data Science for the Layman: No Math Added

As the name suggests, Numsense! Data Science for the Layman: No Math Added does not contain any complex math and gives a concise introduction to Data Science and its algorithms. This unique feature of this book makes it a great Data Science book for beginners.

The authors of this book have used real-world applications to illustrate each algorithm. You will also find point summaries at the end of each chapter.

If you look into the material on the book’s GitHub repository, you will find all of the details, including the R scripts and data files. As a result, you can replicate the authors’ calculations and statistics. All of these materials will help you build a strong foundation in the field of Data Science.

This book includes:

  1. Decision Trees and Random Forests, Regression and Social Network Analysis.
  1. Reference sheets comparing the pros and cons of algorithms.
  1. Glossary list of commonly-used terms.

3. Data Science from Scratch

Data Science from Scratch is the most gentle introduction to Data Science and Data Analytics.

Everything is laid out for beginners and you don’t even have to know how to code.

It is a wonderful book to understand the details of some Machine Learning methods implementation. It is also a good practice to use Python basics. As the name suggests, every function is constructed from scratch. This might be the best Data Science book for beginners.

This book includes

  1.  KNN, Naive Bayes, linear regression and other fundamental analytic tools.
  1. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability.
  1. You’ll learn how many of the most fundamental Data Science tools and algorithms work by implementing them from scratch.

Intermediate level:

4. R for Data Science

The book, R for Data Science, focuses on R, R Studio, and the Tidyverse, a collection of R packages designed to work together to make Data Science fast, fluent, and fun. Its readers call this book the best book on data science.

Although a little knowledge of programming languages is recommended, this book will cover the basics as well and make more clarity to the crucial topics.

This book includes

  1. R tools for solving data problems with greater clarity.
  1. Transform your datasets into a form convenient for analysis.
  1. R Markdown for integrating prose, code, and results.

5. Data Visualization: A Practical Introduction

Data Visualization: A Practical Introduction is suited for people who have an intermediate level of R skills, and some pre-existing ggplot2 (open-source Data Visualization package) experience. This is one of those books to learn Data Science that focuses on thinking about Data Visualization effectively and powerfully.

Effective graphics are essential to communicating ideas and a great way to better understand data. Hence, you can not miss any Data Visualization book, especially this one.

This book includes:

  1. A hands-on introduction to the principles and practice of Data Visualization using R and ggplot2.
  1. How the “Tidyverse” of Data Analysis tools makes working with R easier and more consistent.

6. Practical Statistics for Data Scientists

If you have some experience with Data Science and want to learn more with a focus on the statistical aspect of the profession, then the Practical Statistics for Data Scientists will help you brush up on your statistical skills while also developing new skills and insights.

This Book by O’Reilly covers over 50 core concepts of statistics using R and Python.

This book includes:

  1. Why exploratory Data Analysis is a key preliminary step in Data Science.
  1. How random sampling can reduce bias and yield a higher quality dataset, even with big data.

Advanced level:

7. Hands-On Machine Learning with Scikit-Learn and TensorFlow

When your system can crawl through your data, identify patterns and draw insights with minimal human intervention, Machine Learning is in action.

Hands-On Machine Learning with Scikit-Learn and TensorFlow can help you build an intelligent system. Dives deep into the practical implementation of Scikit-learn and Tensorflow. Also, dives deep enough into the math side of Machine Learning.

This book includes:

  1. Scikit-Learn : An accessible framework that implements many algorithms efficiently and serves as a great Machine Learning entry point.
  1. TensorFlow : A more complex library for distributed numerical computation, is ideal for training and running very large neural networks.
  1. Logistic Regression, Support Vector Machines (SVMs), Decision Trees and Random Forests, Neural networks.

Check out these 10 Must-Read Machine Learning blogs that you should follow.

8. Deep Learning with Python

Deep Learning technology has several applications in the field of Artificial Intelligence such as image classification, speech recognition, text-to-speech and many more.

Deep Learning with Python is written by the author of Keras, one of Python’s most popular Machine Learning libraries. This book covers the fundamentals of what happens in neural networks.

This book includes:

  1.  Fundamental understanding and mathematical building blocks are needed.
  1.  Computer vision with convolutional neural networks (CNNs).
  1.  Text and sequences (time series) with recurrent neural networks (RNNs).
  1. Generating text and images using variational autoencoders (VANs) and generative adversarial networks (GANs)

9. Artificial intelligence: A guide for thinking Human

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”

Melanie Mitchell

Humans have limitations in their visual abilities, as demonstrated by optical illusions, but Artificial Intelligence (AI) struggles on a much more profound level with identifying what’s going on in images. Artificial intelligence: A guide for thinking Human explores various concerns around Artificial Intelligence and how we should prepare for the future.

This book includes:

  1. Interweaving stories about the science of AI and the people behind it.
  1. Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting.
  1. Provocative modern work in the field, flavored with Mitchell’s humor and personal observations. 

Also Read: Best books on Artificial Intelligence

10. Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning is a holistic book on Machine Learning. It is thorough and explains concepts with examples in a straightforward manner.

It is intended for advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and makes no assumptions about prior knowledge of pattern recognition or Machine Learning concepts.

This book includes:

  1. The practical applicability of Bayesian methods.
  1. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and Machine Learning.


All these books to learn Data Science will certainly help you to establish a deep understanding of both Data Science and Data Analytics and ace your journey as a Data Scientist.

To lay the foundation, start with introductory books and then gradually increase the level to intermediate followed by the advanced level books.

Developing a firm understanding and insight into Data Science is key to a long-term prosperous career.

Frequently Asked Questions (FAQ’s)

Are books sufficient for Data Science?

Books can provide you with the necessary knowledge for the actual industry. But since analytics is a more practical subject than a theoretical one, you’ll need some practical experience beforehand.

Can Data Science make you rich?

Data scientists are paid well. A Data Scientist with a fair amount of experience can make up to US $800K in the US, and in India, nearly 90 lakh rupees per annum

Can I learn Data Science on my own?

You can learn Data Science yourself with interest, discipline and persistence.

But Vincent Granville, Data Science Executive said “One drawback of being self-taught is that you will have “holes” in your knowledge that you are not aware of, but they will show up one day, usually at the worst time.”

Mentorship is, therefore, very helpful in every area, let alone the Data Science field.

What math do you need for Data Science?

The only type of math you need to be intimately familiar with for most Data Science positions is statistics. Aside from that, some intermediate knowledge of calculus and linear algebra is beneficial.

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