Best Data Science Books


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Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. Or we can say Data science refers to the process of extracting clean information to formulate actionable insights. Using data science one can uncover findings from data.

Here you will find 10 best Data Science books to learn Data Science from beginner level to advance level.

Top 10 Data Science Books



1. An Introduction to Statistical Learning

Author :- Gareth James
Edition :- 2017 Edition
Published by :- Springer

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

2. Data Science from Scratch

Author :- Joel Grus
Edition :- 2015 Edition
Published by :- O′Reilly

In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist.

3. Python for Data Science For Dummies

Author :- Luca Massaron John Paul Mueller
Edition :- 2nd Edition
Published by :- Wiley

This book is designed for beginners to data analysis and covers the basics of Python data analysis programming and statistics.The book covers the Python fundamentals that are necessary to data analysis, including objects, functions, modules, and libraries.
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis Testing, confidence intervals, and building regression models for prediction.

4. Python Data Science Handbook: Essential Tools for Working with Data

Author :- Jake VanderPlas
Edition :- 2017 Edition
Published by :- O'Reilly Media

The book is not meant to be an introduction to Python or to programming in general; The author assume the reader has familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.
Instead, it is meant to help Python users learn to use Python's data science stack — libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools — to effectively store, manipulate, and gain insight from data.

5. Data Science for Business

Author :- Foster Provost, Tom Fawcett
Edition :- 2013 Edition
Published by :- O'Reilly Media

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

6. Doing Data Science

Author :- Rachel Schutt, Cathy O′neil
Edition :- 2013 Edition
Published by :- O′Reilly

This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.
Topics include: Statistical inference, exploratory data analysis, and the data science process, Algorithms, Spam filters, Naive Bayes, and data wrangling, Logistic regression, Financial modeling, Recommendation engines and causality, Data visualization, Social networks and data journalism, Data engineering, MapReduce, Pregel, and Hadoop.

7. Practical Statistics for Data Scientists

Author :- Peter Bruce, Andrew Bruce, Peter Gedeck
Edition :- 2nd Edition
Published by :- O'Reilly Media

This book is aimed at the data scientist with some familiarity with the R and/or Python programming languages.All the methods in this book have some connection — historical or methodological — to the discipline of statistics. Methods that evolved mainly out of computer science, such as neural nets, are not included.

Two goals underlie this book:
1. To lay out, in digestible, navigable, and easily referenced form, key concepts from statistics that are relevant to data science.
2. To explain which concepts are important and useful from a data science perspective, which are less so, and why.

8. An Introduction to Data Science

Author :- Jeffrey S. Saltz, Jeffrey M. Stanton
Edition :- 2018 Edition
Published by :- SAGE Publications, Inc

An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language.
This book began as the key ingredient to one of those massive open online courses, or MOOCs, and was written from the start to welcome people with a wide range of backgrounds into the world of data science. In the years following the MOOC we kept looking for, but never found, a better textbook to help our students learn the fundamentals of data science. Instead, over time, we kept refining and improving the book such that it has now become in integrated part of how we teach data science.

9. Introducing Data Science

Author :- Davy Cielen , Arno D.B. Meysman, Mohamed Ali
Edition :- 2016 Edition
Published by :- Dreamtech Press

Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process.
You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it.

10. R for Data Science

Author :- Hadley Wickham, Garrett Grolemund
Edition :- 2017 Edition
Published by :- O'Reilly Media

The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science and also the book is to give you a solid foundation in the most important tools.You'll use these tools in every data science project, but for most projects. You can tackle about 8o% of every project using the tools that you'll learn in this book, but you'll need other tools to tackle the remaining 20%.
Throughout this book we'll point you to resources where you can learn more. After reading this book, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R.


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