13 BEST Data Science Books (2021 Update)


Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.

Here is a curated list of Top 17 Data Science Books that should be part of any beginner to advanced Data Science Learners library.

1) Data Science from Scratch: First Principles with Python

Data Science from Scratch is a book written by Joel Gurus. This book helps you to learn math and statistics that is at the core of data science. You will also learn hacking skills you need to get started as a data scientist.

The books include topics like implement k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, and clustering models. You will be also able to explore natural language processing, network analysis, etc.

2) Data Science For Dummies

Data Science For Dummies is a book written by Lillian Pierson. This book is ideal for IT professionals and students who want a quick primer covering all areas of the expansive data science space.

The book covers topics like big data, data science, and data engineering, and how all of these areas are combined to which offers great value. You will also learn about technologies, programming languages, and mathematical methods.

3) Big Data: A Revolution That Will Transform How We Live, Work, and Think

Big Data is a book written by Viktor Mayer-Schonberger and Kenneth Cukier. The book talks about the optimistic and practical look at the Big Data revolution. The authors of this book also talk about how Big data technology able to change our lives and what we can do to protect ourselves from its hazards.

4) Storytelling with Data: A Data Visualization Guide for Business Professionals

Storytelling with data is a book written by Cole Nussbaumer Knaflic. In this book, you will learn the fundamentals of data visualization and how to communicate effectively with data. The lessons in this book are mostly in theory and offer many real-world examples ready for immediate application to your next graph or presentation.

This book also teaches the reader about how they can go beyond predictable tools to reach the root of your data. It also includes a topic of how to use your data to create an engaging and informative story.

5) Designing Data-Intensive Applications

Designing Data-Intensive Applications is a book written by Martin Kleppmann. This book helps learn the benefits and drawbacks of various technologies for processing and storing data. This book also helps software engineers and architects to learn about how to make full use of data in modern applications.

The book helps you to make informed decisions by identifying the strengths and weaknesses of different tools and navigate the trade-offs around consistency, scalability, fault tolerance, and complexity.

6) Practical Statistics for Data Scientists: 50 Essential Concepts

Practical Statistics for Data Scientists is a book written by Peter Bruce (Author), Andrew Bruce. This book explains how to apply various statistical methods to data science, and gives you advice on what's important and what's not.

This book is an easy-to-use data science reference book if you're familiar with the R programming and have some knowledge of statistics.

7) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data

Data Science and Big Data Analytics is a book published by EMC education service. This book covers the breadth of activities and methods and tools that data scientists use. The book focuses on concepts, principles, and practical applications.

It applies to any industry and technology environment, and the learning. It is supported and explained with examples that you can replicate using open-source software.

8) Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Data Science for business is a book written by known data science experts Foster Provost and Tom Fawcett. This Data science study book introduces the fundamental principles of data science. This study book helps you understand the many data-mining techniques in use today.

You'll also learn how to improve communication between business stakeholders and data scientists. It also helps you understand the data-analytical process and how data science methods able to support business decision-making.

9) Head First Statistics: A Brain-Friendly Guide

Head First Statistics is a book written by Dawn Griffiths. The writer brings this typically dry subject to life, teaching you everything you want and need to know about statistics through a material that is full of puzzles, stories, quizzes, and real-world examples.This book helps you to learn statistics so you can understand key points and use them. The book also covers how to present data visually with charts and plots. Lastly, the book also teaches how you can calculate probability and expectation, etc.

10) R for data science: Import, Tidy, Transform, Visualize, And Model Data

R for Data Science is a book written by Hadley Wickham. It is designed to get you doing data science as quickly as possible.

The book guides you through the steps of importing, exploring, and modeling your data and communicating the results.

In this book, you will get a complete, big-picture understanding of the data science cycle. Apart from the basic tools, you need to manage the details. Each section of this book is paired with exercises to help you practice what you've learned along the way.

11) Hands-On Machine Learning

Hands-On Machine Learning is a Data Science book written by Aurélien Géron. The book helps you learn the concepts and tools for building intelligent systems. You'll learn also learn various techniques, like simple linear regression and progressing to deep neural networks. Each chapter of this book helps you apply what you've learned; all you need is programming experience.

12) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis is a book written by Wes McKinney. This reference book is full of case studies showing how to solve many commonly faced data analysis problems. In this Data science book, you will learn the latest versions of pandas, NumPy, IPython, and Jupyter.

This reference book is a practical, modern introduction to data science tools in Python. It's an ideal book for analysts new to Python and Python programmers.

13) Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning with Python is a book written by Andreas C. Müller (Author), Sarah Guido (Author). In this book, you will learn the steps necessary to create a successful machine-learning application with Python and the sci-kit-learn library.

In this book, you will learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. This study material also introduces you to NumPy and matplotlib libraries.

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