# 13 Best Data Science Books That Boost Your Career in 2019

**Data Science has gained popularity over the past few years**. While many people wish to start learning Data Science, it is a challenge to know where to start. In this article, we will walk you through some of the useful best Data Science books that you can read to **master Data Science**. Since Statistics and Programming are the two important ingredients for Data Science, we will divide this article into these two sections. We will then address each section with their corresponding books and help you to take your first step in Data Science.

To understand the tools and concepts in details, we will go through some important books required for Data Science.

## Best Data Science Books

Here you will see the details of the best data science books in two parts. First, we will see the statistics and probability books and second is books on programming languages and **tools for Data Science**. So, without talking much, let’s start exploring the best data science books –

### a. Statistics & Probability Books for Data Science

Statistics and Probability are the two most important concepts required to craft our Data Science recipes. In order to be proficient in these fields, one must have a beginner’s approach and apply the knowledge acquired in practical scenarios.

Data Science is all about practicality in the end. Given a problem, you need to solve it using all means necessary. However, more than the result, it is the methodology that makes Data Science so special. In order to focus on these methods, one must be able to understand and evaluate various underlying procedures required to solve the problems. With this, let us start by having a look at some of the popular books on Statistics & Probability.

**Learn How Can You become a Data Science **

#### 1. Head First Statistics: A Brain-Friendly Guide

*-by Dawn Griffiths*

If you need a **quick dive into Statistics** while also being a total newbie, I will recommend you this best book for Data Science. If you are from a programming background, you must be familiar with many of the *Head First Series on programming languages*. What sets apart this book from other books on Statistics is its user-friendly approach and lucid explanations of some tough concepts.

Considering that you are an aspiring Data Scientist with a non-statistics background, it can be a bit overwhelming. This book gives several examples required to understand statistics without dwelling too much on mathematical jargons.

#### 2. Naked Statistics

*-by Charles Wheelan*

Best suited for beginners, this book serves as a starter guide to statistics. It uses an example based approach to help the readers gain an in basic insight about Statistics. It uses easy language to assist the readers in understanding statistical formulae without dwelling on their complexities. This book covers basic topics of** Descriptive Statistics** like mean, median, and mode, basics of probability, correlation, regression analysis etc. After reading this book, you can expect to have a basic understanding of Statistics and develop a thought process about its background and working.

#### 3. Introduction to Statistical Learning

*-by Gareth James*

After grasping the basic concepts of Statistics, you are ready to learn Statistics in its raw, true form. While many books provide standalone statistics, this book aligns Statistics with **Machine Learning** using R. It will provide you with the** knowledge of various statistical methods used in Machine Learning**. In this way, it bridges the theoretical concepts of Statistics with real-world applications. It explains a wide variety of topics like regression, classification, support vector machines, clustering etc. This book will not only strengthen your basics in Statistics but will also allow you to implement them in practical scenarios using R.

#### 4. Practical Statistics for Data Scientists

*-by Peter Bruce*

This book is for aspiring Data Scientists with no formal training in Statistics. This book cuts down on the overdose of the statistics and provides only the concepts necessary for Data Scientists. Furthermore, this book is for people who have studied the basic knowledge of Statistics. Furthermore, this book provides examples of statistical procedures using R. This will allow you to practice the required concepts and also** sharpen your R skills**. The structure of this book is in accordance with * real-world applications of Data Science*.

#### 5. Introduction to Probability

*-by Charles M. Grinstead*

This data science book is best suited for beginners who wish to learn the probability from scratch. This book explains various concepts of probability that are useful in Data Science. It gives detailed knowledge about Discrete and Continuous Probability, Conditional Probability, Combinatorics, Central Limit Theorem, Markov Chains etc. Therefore, you will be able to learn the essential concepts of Probability and use them in solving the problems. Moreover, this book is easily accessible and can be read online for free.

### b. Books on Programming Languages & Tools for Data Science

As mentioned earlier, programming languages and tools are necessary ingredients required for solving Data Science problems. * A Data Scientist uses a variety of tools* and languages like R, Python, SQL, Hadoop, Scala etc. An aspiring Data Scientist must read the following books to gain expertise over many of the programming languages and tools.

#### 6. Python Crash Course

*-by Eric Matthes*

This book is for absolute beginners in Python. While Python is easier to learn, it is difficult to master. This book is meant for people who want to quickly learn Python in order to jump into Data Science. This book in divided into two parts: The first part teaches you **Python through various concepts** like conditions, loops, dictionaries, lists etc. The second part focuses on building various projects using Python.

With this book, you will not only** learn Python** but also learn how to solve problems with Python. You will also learn about various Python libraries used in the analysis, visualization and web-application development. Overall, this book is ideal for people who wish to learn Python in one go and implement their knowledge in real-world scenarios.

#### 7. Introduction to Machine Learning with Python: A Guide for Data Scientists

*-by Andreas Muller*

For Python beginners looking forward to applying Python in **real-world applications of Machine Learning**, this book will give them everything they need. This book focuses on teaching Python to users in order to help them build their machine learning solutions.

This book will teach you popular **Machine Learning algorithms** and will teach you the essentials of the scikit-learn library which is most popular in Python. It will not only teach you Python but also the fundamental of Machine Learning in order for you to grow into a skilled Data Scientist. You will learn how to evaluate your model and provide you with suggestions to improve yourself as a Data Scientist.

#### 8. Hands-On Programming with R

*-by Garrett Gorlemund*

R is the most statistically oriented programming language for Data Science. This book will provide you with your first * lessons of R*. The authors have tailored this book keeping in mind non-programmers. This book will walk you through some of the most basic concepts of R like objects, notations, environment, and packages.

You will learn R through a basic walkthrough and** learn to apply R in real-life problems**. You will learn to write your custom built functions that you can use for solving problems. This book is designed to keep in mind the tasks of a Data Scientist using R and covers topics like loading data, writing functions, navigate through the R environment etc. Furthermore, the book is fully available online as an interactive book that you can read as you practice coding in R.

### 9. R for Data Science

*-by Hadley Wickham and Garrett Grolemund*

This book uses R for teaching Data Science. It teaches you all the** skills required to be a Data Scientist** like data cleaning, visualization, wrangling and also introduces you to RStudio. It makes you familiar with important packages of R like the tidy verse that are helpful in Data Science.

This book is for people who have read the previous book “Hands on Programming with R”. This book is specifically designed for the tasks that a Data Scientist must perform in his everyday routine. It will help you to utilize the cognitive resources through **R’s packages** to wrangle and visualize the data. Just like the book “Hands on Programming with R”, the book is freely available online.

#### 10. Practical Data Science with R

*-by Nina Zumel*

As the name suggests, this book teaches R in a very pragmatic manner through its applications in Data Science. This book takes examples from business intelligence, A/B Testing, Decision Support to give you real-life information about Data Science. The authors of this book know the various underlying tools of Data Science and have combined them all together to give the reader a holistic view of Data Science.

If you are a **Data Scientist who wants to learn all the essential skills** in one place, this book will serve as an ideal option. This book will help you explore the depths of Data Science without letting you dwell in the complex jargons. This book does away with all the unnecessaries and will help you to learn only what’s important to chisel you into a Data Scientist.

#### 11. Learning SQL

–*by Alan Beaulieu*

This book serves as an **introductory guide to SQL**. It will help you to understand various SQL queries and apply them in real-world situations quickly. It will teach you basic SQL queries that will help you to retrieve, manipulate and create database objects like tables. This book will also teach you more advanced concepts like grouping and database transactions. Overall, it is best suited for people who want a basic-advanced understanding of SQL in one book.

You can read the full book for free.

#### 12. SQL Cookbook

*-by Anthony Molinaro*

This book is for people who have the rudimentary knowledge of SQL but want to explore more advanced concepts. This book will teach you powerful **SQL queries** and functions that you can use in your database. You will learn the Window Function, Hierarchical Queries, advanced searching techniques etc. This book will be best suited for people who have the zeal to explore the deepest parts of SQL and be proficient at it.

#### 13. Hadoop: The Definitive Guide

*-by Tom White*

**Hadoop is a Big Data tool** used for handling and storing huge loads of data. A Data Scientist must know Hadoop in order to deal with a large amount of data. This book will help the readers to build and maintain reliable distributed systems using Hadoop. You will also learn the real-life usage of Hadoop in healthcare and genomic processing. You will also learn the functioning of Hadoop packages like Hive, Pig, HBase etc.

## Summary – Best Data Science Books

While **Data Science** is an immensely vast field. One must know the right resources to learn the width and depths of this field. We divided our article into two parts: Statistics & Probability and Programming Languages for Data Science. While we need Statistics as the main recipe for cooking our Data Science problems, the various programming languages and tools are the essential ingredients. In the end, we conclude that in order to master Data Science, one must read the above books to gain expertise in both Statistics and Programming.

Hope the information helped you. Please tell us your views on the “Best Data Science Books” article via comments.

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