Top 5 Statistics Books For A Data Scientist in 2022

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Top 5 Statistics Books For A Data Scientist In 2022

Every data has its own story. Projects fail, and sometimes won’t even start due to lack of data. But, it’s not a lack of data, but a lack of statistical knowledge which is drowning them. I wish to start this article with a quote by Andrew Lang before we go into the list of top 5 statistical books for a data scientist in 2022. With this quote, you will understand how people are using statistics to solve real-world problems. 

Most people use statistics like a drunk man uses a lamppost; more for support than illumination.

-Andrew Lang.

Top 5 Statistics Books For Budding Data Scientists

Let’s look at some of the best books that I’ve come across in my journey as a data scientist. You can choose the one that you like best and to begin with.

1. Practical Statistics for Data Science

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

You can download the PDF version for this book using above download button.

Level: Beginner

Author/s: Perter Bruce and Andrew Bruce

Key points –

  • This book by Peter and Andrew Bruce covers all the major topics of statistics used in Data Science. It is beginner-friendly. Anyone can grab this and start learning.
  • It includes hundreds of coding examples about statistics using R programming. (R lovers should not miss this book :P)
  • Along with reading if you want to practice stats for data science, it is the go-to book for you.
  • – You can find all the coding examples here.
  • For R programmers, this is Gold.

2. Think Stats

Think Stats: Exploratory Data Analysis

Level: Python Beginner

Author/s: Allen B. Downey

Key points –

  • Think stats is an awesome book that I personally prefer for python programmers who want to learn stats for data science.
  • As stated in the book’s introduction, this book is all about “Turning knowledge into Data”.
  • Some of the key concepts covered in this book are statistical thinking, Hypothesis testing, correlation, and data distributions.
  • Since this book entirely focuses on stats for data science, any data scientist will find this easy to follow and implement the concepts.
  • Again, this book also includes many coding examples in Python.
  • It shed enough light on Probability concepts such as the Bayes theorem and Binomial distribution. 
  • You can get all the coding assignments here –

3. Naked Statistics

Naked Statistics: Stripping the Dread from the Data

Level: Advanced

Author/s: Charles Wheelan (Naked Economics)

Key points –

  • This book is crowned as “Statistics come Alive”.
  • Unlike other books which are specifically focused on stats for data science, this book covers advanced statistical topics such as Central limit theorem, Regression analysis, Inference, and Evaluation as well.
  • This is much like a traditional and powerful stats book, which doesn’t include any coding examples.
  • But all the topics are explained with relevant examples and illustrations.
  • You will get to see some of the exciting problem statements and case studies as well.
  • All I can say is – “This never gets old”.

4. Head-First Statistics

Head First Statistics: A Brain-Friendly Guide

Level: Beginner

Author/s: Dawn Griffiths

Key points –

  • One of the first and best things about this book is – All the concepts are explained in a storytelling manner, which is awesome :P.
  • This book is best suited for beginners who are interested in statistics and want to learn creatively.
  • I can promise you one thing – “You won’t get bored with this”. 
  • Some of the key concepts covered in this book are descriptive and inferential stats along with Probabilistic distributions.
  • In this book, all the topics are explained with real-world examples which stand out from others.

5. Statistics Done Wrong

Statistics Done Wrong: The Woefully Complete Guide

Level – Advanced

Author/s – Alex Reinhart

Key points –

  • I would say this is a “Lost Treasure”. 
  • Only a few people know about this book and read it. Unlike other statistics books, this deep dive into core stats concepts such as significance, Models, Errors, and Pseudoreplication.
  • When I was looking for some core stats book on the web, I found this. All the concepts are clearly explained considering this is an advanced-level book.
  • But make sure, you know the basic stats before you start reading it.
  • This book is popular for many unique concepts which you will enjoy a lot. There is one dedicated chapter about modeling, modeling bias, and evaluation. Should get a place in top statistical books.

Statistics books – What’s Next?

This list of top 5 statistics books can really educate you enough to get this done in your domain of work. If you are a data scientist or analyst, nothing can be best than these books. I have curated this list based on people’s opinions, concepts, flexibility, and personal experience as well. I hope you will find this useful and let’s start looking at data with a stats lens. Remember – “No data is short, but misinterpreted”. That’s all for now. Happy Stats!!!

More read: Why learn Statistics?

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