Data Science Books

Top 10 Data Science Books [Updated 2021]

Data science is a field that involves scientific methods, processes, algorithms, and systems to extract knowledge and insights from raw data in various forms, both structured and unstructured data. Below is the list of books on data science that you must read in 2021 –

  1. Python Data Science Handbook ( Get this book ) 
  2. Data Science (MIT Press Essential Knowledge series) ( Get this book ) 
  3. R for Data Science ( Get this book ) 
  4. Storytelling with Data ( Get this book ) 
  5. Data Science from Scratch ( Get this book ) 
  6. Data Science for Business ( Get this book ) 
  7. Data Smart  ( Get this book ) 
  8. Practical Statistics for Data Scientists ( Get this book ) 
  9. Numsense! Data Science for the Layman ( Get this book ) 
  10. Practical Data Science with R ( Get this book ) 

Let us discuss each of the data science books in detail along with its key takeaways and reviews.

Data Science Books

#1 – Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Python Data Science Handbook: Essential Tools for Working with Data

 

Book Review:

The book is ideally suited to those that already know the basics of the Python language or already know how to program in another language like R or Julia and want to learn how to use Python for data science. It explains all the needs of the entire Data Science process from getting data, exploring data, and communicating and visualizing the results.

Key Takeaways
  • Data Manipulation.
  • Python data techniques.
  • Machine Learning.
<< Get this book >>

 

#2 – Data Science (MIT Press Essential Knowledge series)

Author: John D. Kelleher and Brendan Tierney 

Data Science (The MIT Press Essential Knowledge series)

 

Book Review:

The main aim of this book is to improve decision making through analysis of data. This introduces the basics of machine learning and discusses how to link machine learning expertise with real-world problems.

Key Takeaways:
  • Ethical and legal issues and developments in data regulation.
  • Success Principles.
  • Future impact of data science.
<< Get this book >>

 

#3 – R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Author: Hadley Wickham and Garrett Grolemund 

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

 

Book Review:

This book will give a clear understanding of discovering natural laws in the structure of data. This will tell you how to use the R Programming language for data analysis. This also tells how to clean the data draw plots and how to use the grammar of graphics, literate programming, and reproducible research to save time and also many other things.

Key Takeaways:
  • Data Wrangling.
  • Data Visualization.
  • Exploratory Data Analysis
<< Get this book >>

 

#4 – Storytelling with Data:  A Data Visualization Guide for Business Professionals

Author: Cole Nussbaumer Knaflic

Storytelling with Data: A Data Visualization Guide for Business Professionals

 

Book Review:

This book mainly explains the fundamentals of data visualization and how to communicate effectively with data. Through this book, you will be able to find out which is the crucial point for your data. This tells how to go beyond the conventional tools to reach the root of your data and how to create an informative and compelling story.

Key Takeaways:
  • Understanding the situation and audience.
  • Identifying the important point of the data.
  • Concepts of design in data visualization.
  • The power of storytelling to help your message resonate with your audience.
<< Get this book >>

 

#5 – Data Science from Scratch: First Principles with Python

Author: Joel Grus

Data Science from Scratch: First Principles with Python

 

Book Review:

The author has clearly explained the important data science tools and the algorithms and how they can be implemented from scratch. This book contains the actual algorithms for those machine learning models, along with the theory and mathematics in it.

Key Takeaways:
  • Collect, Explore, Clean, and manipulate data.
  • Neural networks.
  • Easy understanding of algorithms.
  • Fundamentals of machine learning.
<< Get this book >>

 

#6 – Data Science for Business

What You Need to Know about Data Mining and Data-Analytic Thinking

Author: Foster Provost and Tom Fawcett 

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

 

Book Review:

It explains the fundamental principles of data science, and also the data-analytic thinking which is required for obtaining important knowledge and business information from the data. This also helps in the understanding of the techniques which are applied nowadays. This book also provides examples of real-world business problems to explain the principles.

Key Takeaways:
  • Data mining.
  • Data science Principles.
  • Understanding of the Data science how it suits the organization.
<< Get this book >>

 

#7 – Data Smart: Using Data Science to Transform Information into Insight

Author: John W Foreman

Data Smart: Using Data Science to Transform Information into Insight

 

Book Review:

The author clearly explains how to convert raw data into actionable insight. The author also explained how to do it with the Spreadsheet. This will also help you in learning the analytical techniques, mathematics, and the magic behind the big data. Each chapter in the book will cover a different technique in a spreadsheet-like mathematical optimization, data mining in graphs, moving from spreadsheets to R programming language, and many other things.

Key Takeaways:
  • Mathematics in data science.
  • Artificial Intelligence.
<< Get this book >>

 

#8 – Practical Statistics for Data Scientists: 50 Essential Concepts

Author: Peter Bruce

Practical Statistics for Data Scientists: 50 Essential Concepts

 

Book Review:

Statistics also plays an important role in Data Science. In this book, the author has clearly explained how to apply various statistical methods to data science in the present and also how to avoid them, which are in wrong use and gives you output on what’s important and what’s not. If you’re good with the R programming language and have some knowledge of statistics, this quick reference builds the gap to a larger extent in the readable format.

Key Takeaways:
  • Key classification techniques.
  • Statics concepts.
  • Unsupervised learning methods for extracting meaning from unlabeled data.
<< Get this book >>

 

#9 – Numsense! Data Science for the Layman: No Math Added

Author: Annalyn Ng and Kenneth Soo 

Numsense! Data Science for the Layman: No Math Added

 

Book Review:

This book gives a clear understanding of the data science and the algorithms which are used. Every algorithm is clearly explained. There are many concepts that all are covered like Neural Networks, Social Network Analysis, Decision Trees and Random Forests, Clustering, and also many more.

Key Takeaways:
  • Real-world applications to illustrate each algorithm.
  • Practical understanding.
  • Key concepts.
<< Get this book >>

 

#10 – Practical Data Science with R

Author: Nina Zumel and John Mount 

Practical Data Science with R

 

Book Review:

It clearly explains the practical examples and fundamental principles of the data science with the programming language R. This will help in applying the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels.

Key Takeaways:
  • Decision Support.
  • Practical Examples.
  • Modeling methods.
<< Get this book >>

 

Recommended Books

This has been a guide to Data Science Books. Here we provide a list of the top 10 books to understand the new concepts and applications of data science. You may refer to the following books to learn more –

 

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