7 Best Books to Become a Data Scientist in 2023


Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope.

In this article, let’s cover the best books to build a career or side hustle as a data scientist through a collection of book reviews.

Each book review highlights the taste of the book, the contents covered, and how it can benefit you. 


Why Learn Data Science?

Let us now delve into the top reasons why you should learn Data science:

  • A fuel of 21st century: Data science has critical applications across most industries today. It is rapidly expanding its horizons to places never thought possible.

  • Problem of demand and supply: There is a lack of ‘data-literacy’ in the market. In order to fill this vacuum in supply, you need to learn Data Science and its underlying fields.

  • A lucrative career: The the value of a Data Scientist is very high in the market. It is one of the most in-demand careers in computer science and the job outlook for data scientists is very positive.

  • Offers Quick Growth: Data science is the new engine driving different industries and businesses. Good knowledge and experience in data science ensures a quick career growth. 

  • You can use your knowledge in data science for generating side income: There are so many side income opportunities for data scientists for example freelancing, consultancy, tutoring, teaching, blogging.


What Makes The Best Data Science Books? 

Here are our criteria for selection of the books:

  • The book should contain a variety of instructional materials, including exercises, examples, questions, learning activities, and other features that promote the reader's engagement and learning.

  • It uses clear, precise, and easy-to-understand language.

  • Content must be up-to-date and should thoroughly teach and explain the basic concepts of data science.

  • Contain assignments for practice and hands-on experience

  • The book should have a clear layout and must be friendly toward self-taught programmers.


Best Books for Data Scientists 

We have reviewed the top 7 books for data scientists.

It is important to stay on top of the game and read relevant books to boost your skills. Here’s the list of books you should read as a Data Analyst at any level. These books will jumpstart your career and help you along the way.


1. Best Book for Pragmatic Learners: Data Science from Scratch: First Principles with Python

Data Science from Scratch by Joel Grus is the most gentle introduction to Data Science and Data Analytics. This book will give you a crash course in Python, linear algebra, statistics, and probability. After reading the book, you will be able to:

  • Collect, explore, clean, munge, and manipulate data

  • Dive into the fundamentals of machine learning

  • Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering

  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

The book is divided into twenty seven chapters and includes the following contents:

  • Chapter 1 gives the introduction to data science

  • Chapter 2 is a crash course in Python

  • Chapter 3 talks about 3. Visualizing Data

  • Chapter 4 covers linear algebra

  • Chapter 5 talks about Statistics

  • Chapter 6 covers Probability

  • Chapter 7 talks about Hypothesis and Inference

  • Chapter 8 covers Gradient Descent

  • Chapter 9 guides you on getting data

  • Chapter 10 talks about working with Data

  • Chapter 11 covers Machine Learning

  • Chapter 12 covers k-Nearest Neighbors

  • Chapter 13 talks about Naive Bayes

  • Chapter 14 covers Simple Linear Regression

  • Chapter 15 covers Multiple Regression

  • Chapter 16 covers Logistic Regression

  • Chapter 17 covers Decision Trees

  • Chapter 18 talks about Neural Networks

  • Chapter 19 covers Deep Learning 

  • Chapter 20 talks about clustering 

  • Chapter 21 talks about Natural Language Processing

  • Chapter 22 covers Network Analysis

  • Chapter 23 covers Recommender Systems

  • Chapter 24 covers Databases and SQL

  • Chapter 25 covers MapReduce

  • Chapter 26 talks about data ethics

  • Chapter 27 guides to go forth and do Data Science 

This book will show the reader how to find the gems in today’s messy glut of data.


2. Best book for Data Scientists Computing in Python: Python Data Science Handbook

Python Data Science Handbook by Jake VanderPlas gives an introduction to the Python language, along with how to do machine learning with Python based tools.

The book addresses the needs of the entire Data Science Process, from getting the data, exploring the data, modeling the data and communicating/visualizing the results.

  • Chapter 1 covers IPython: Beyond Normal Python

  • Chapter 2 covers introduction to NumPy

  • Chapter 3 covers Data Manipulation with Pandas

  • Chapter 4 covers Visualization with Matplotlib

  • Chapter 5 covers Machine Learning

Every page is rich in information and provides practical use case examples, optimization tricks and adds new dimensions to your understanding of the topic. This book is the must-have reference for data scientists computing in Python.


3. Best book for the Career-Focused Learner: Build a Career in Data Science

Build a Career in Data Science by Emily Robinson and Jacqueline Nolis guides you to land your first data science job and develop into a valued senior employee. 

The book guides the reader how to create a portfolio of data science projects. The authors discuss assessing and negotiating an offer, leaving gracefully and moving up the ladder. The book also includes some interviews with professional data scientists.

The sixteen chapters are divided into four parts.

Part 1 - Getting Started With Data Science

  • Chapter 1 gives an overview of data science

  • Chapter 2 talks about Data science companies

  • Chapter 3 talks about Getting the skills

  • Chapter 4 helps you in Building a portfolio

Part 2 - Finding Your Data Science Job

  • Chapter 5 talks about the search: Identifying the right job for you

  • Chapter 6 talks about the application: resumes and cover letters

  • Chapter 7 covers the interview: What to expect and how to handle it

  • Chapter 8 covers the offer: Knowing what to accept

Part 3 - Settling Into Data Science

  • Chapter 9 talks about the first months on the job

  • Chapter 10 talks about taking an effective analysis

  • Chapter 11 talks about deploying a model into production

  • Chapter 12 talks about working with stakeholders

Part 4 - Growing In Your Data Science Role

  • Chapter 13 steers the way when your data science project fails

  • Chapter 14 guides you to join the data science community

  • Chapter 15 talks about leaving your job gracefully

  • Chapter 16 guides you about moving up the ladder

This book is ideal for those who want to begin or advance a data science career.


More books you may like:


4. Best book for Serious Learners: Data Science: A Comprehensive Beginner’s Guide to Learn the Realms of Data Science

Data Science: A Comprehensive Beginner’s Guide to Learn the Realms of Data Science by Liam Damien gives a detailed overview of Data Science and the skills that one needs to develop to become a data scientist.

The book will help you learn the following:

  • Big Data and its benefits

  • What is data and how it can be used to obtain insights

  • The different types of data analytics

  • What is Data Visualization

  • How to interpret data

  • What are data mining and the different algorithms used for data mining

  • Some data mining tools and their advantages

  • What is data integration and the process

This book will be the perfect solution to those who are new to the realms of data science.


5. Best Book for Completionists: Data Science (The MIT Press Essential Knowledge series)

Data Science by John D. Kelleher gives a concise introduction to the emerging field of data science. The book talks about its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.

The book introduces fundamental data concepts and describes the stages in a data science project. It is divided into seven chapters and includes the following contents:

  • Chapter 1 gives an introduction to data science

  • Chapter 2 talks about what is data and what is a data set

  • Chapter 3 covers the data science ecosystem

  • Chapter 4 covers machine learning

  • Chapter 5 talks about standard data science tasks

  • Chapter 6 talks about privacy and ethics

  • Chapter 7 talks about future trends and principles of success 

This book covers core concepts in data science in an easy to read manner. Overall, it offers a great non-technical overview of data science. This well-written and easy-to-understand book is for anyone who wishes to enter data science.


6. Best Book for Total Beginners: Data Science For Dummies

Data Science for Dummies by Lillian Pierson gives a broad overview of the discipline to get readers familiar with data science. The book also explores topics like data engineering, programming languages like R and Python, machine learning, algorithms, artificial intelligence, and the evolution of the Internet of Things. The book also explores data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate.

The book is divided into six parts and twenty three chapters.

  • Part 1 helps in getting started with Data Science 

  • Part 2 talks about using data science to extract meaning from your data

  • Part 3 talks about creating data visualizations that clearly communicate meaning

  • Part 4 covers computing for Data Science

  • Part 5 talks about applying domain expertise to solve real-world problems using data science

  • Part 6 includes 10 phenomenal resources for open data and 10 free data science tools and applications

If you have a curiosity about data science, this might be a good place to start.


7. Best book for Hands-On Learners: Data Science Projects with Python

Data Science Projects with Python by Stephen Klosterman is a hands-on introduction to real-world data science. This book will help you gain hands-on experience with industry-standard data analysis and machine learning tools in Python. 

After reading the book, you will be able to:

  • Tackle data science problems by identifying the problem to be solved

  • Illustrate patterns in data using appropriate visualizations

  • Implement suitable machine learning algorithms to gain insights from data

The contents covered in the book are:

  • Data Exploration and Cleaning

  • Introduction to Scikit-Learn and Model Evaluation

  • Details of Logistic Regression and Feature Exploration

  • The Bias-Variance Trade-off

  • Decision Trees and Random Forests

  • Imputation of Missing Data, Financial Analysis, and Delivery to Client

The book is full of practical step-by-step exercises, activities and solutions. The contents are written in a well-structured and easy to understand manner. 

The book is an ideal introduction to data science for those already familiar with foundational Python.


More Ways to Learn to Become a Data Scientist

The data science books featured in this post will help the reader gain insight into this growing field.

I always recommend pairing your book with multiple forms of input, so that you can learn as quickly and effectively as possible.

You can pursue your data science learning plan online. There is a wide range of popular online courses in data science and we have listed a few:

These online courses include lecture videos, live sessions, and opportunities to collaborate with other learners and data scientists from all around the world.

We also suggest here over 70 coding resources that are free online.

Level up your Data Science skills by reading these books and taking these online courses. 

Good luck!👍 

Miranda Limonczenko

Miranda is the founder of Books on Code, with a mission to bring book-lover culture to programmers. Learn more by checking out Miranda on LinkedIn.


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