# 10 Best Books to Become a Data Scientist in 2024

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. You’ll learn IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related 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. With this book, you'll learn how:

IPython and Jupyter provide computational environments for scientists using Python

NumPy includes the ndarray for efficient storage and manipulation of dense data arrays

Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data

Matplotlib includes capabilities for a flexible range of data visualizations

Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

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 William Vance gives a detailed overview of Data Science and the skills that one needs to become a data scientist.

The book will help you learn the following:

What is data science, and how it has emerged

What are the responsibilities of a data scientist and the fundamentals of data science

Overall process with the life cycle of data science

How data science tools, like statistics, probability, etc.

Help to draw insights from data

Basic concept about data modeling, and featurization

How to work with data variables and data science tools

How to visualize the data

How to work with machine learning algorithms and Artificial Neural Networks

Concepts of decision trees and cloud computing

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 techniques using pandas, scikit-learn, and XGBoost.

The book will take you through the end-to-end process of exploring data and delivering machine learning models. This edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world. After reading the book, you will be able to:

Load, explore, and process data using the pandas Python package

Use Matplotlib to create compelling data visualizations

Implement predictive machine learning models with scikit-learn

Use lasso and ridge regression to reduce model overfitting

Evaluate random forest and logistic regression model performance

Deliver business insights by presenting clear, convincing conclusions

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

Gradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values

Test Set Analysis, Financial Insights, and Delivery to the 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.

### 8. Best Book for Data-Driven Solutions: **Dive Into Data Science**

Dive Into Data Science by Bradford Tuckfield is a practical introduction on how to use data science and Python to solve everyday business problems. The book shows you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges.

Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms. You’ll also learn how to forecast consumer demand, optimize marketing campaigns, reduce customer attrition, predict website traffic, and build recommendation systems

The book is divided into ten chapters and includes the following topics:

Chapter 1: Exploratory Data Analysis

Chapter 2: Forecasting

Chapter 3: Group Comparisons

Chapter 4: A/B Testing

Chapter 5: Binary Classification

Chapter 6: Supervised Learning

Chapter 7: Unsupervised Learning

Chapter 8: Web Scraping

Chapter 9: Recommendation Systems

Chapter 10: Natural Language Processing

The book strikes a nice balance of explaining fundamental data science concepts and theories, while also equipping readers with hands-on practice with Python. I recommend this to anybody looking for a solid introduction to data science.

### 9. Best Data Analytics Book using R: R for Data Science

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Mine Çetinkaya-Rundel teaches you how to do data science with R and RStudio. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details.

You'll understand how to:

Visualize: Create plots for data exploration and communication of results

Transform: Discover variable types and the tools to work with them

Import: Get data into R and in a form convenient for analysis

Program: Learn R tools for solving data problems with greater clarity and ease

Communicate: Integrate prose, code, and results with Quarto

There are exercises that help you practice what you've learned. The book gives you a solid foundation in the most important tools and enough knowledge to find the resources to learn more when necessary. I highly recommend if you use R already and want to learn more.

### 10. Best Book on Maths Behind Data Science: Essential Math for Data Science

Essential Math for Data Science by Thomas Nield teaches you the math needed to excel in data science, machine learning, and statistics. The book guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science.

This book gives a simple and methodical background to the use of math in DS. Here’s what you’ll get from the book:

Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning

Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon

Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance

Manipulate vectors and matrices and perform matrix decomposition

Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks

Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

It is the definitive handbook on data that every data scientist, analyst, business manager should understand before working with data. This book makes you appreciate the theories behind DS and ML.

## 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:

**Udemy:**The Data Science Course 2021: Complete Data Science Bootcamp is a high-rated course that provides the entire toolbox you need to become a data scientist. This 28.5 hours course is divided into 63 sections and includes 90 articles.

**Coursera:**You can launch your career in data science with Introduction to Data Science Specialization. There are four courses in this specialization including

Course 1: What is Data Science?

Course 2: Tools for Data Science

Course 3: Data Science Methodology

Data Science: Statistics and Machine Learning Specialization is a series of courses that covers statistical inference, regression models, machine learning, and the development of data products.

**Codecademy:**Codecademy Pro has an extensive Data Scientist Career Path designed to take you from zero to professional ready to interview. . The course teaches how to analyze data, communicate findings, and draw predictions using machine learning. For more on Codecademy Pro, see my Codecademy Pro review.

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!👍