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Top Data Science Books for 2021

If you are interested in learning more about data science, you may be shocked at the sheer volume of titles available. Media platform O’Reilly, an aggregator of books from various publishers, has over 25,000 titles on the topic.

To help narrow the search, we’ve asked a few experts to share their top data science reads.

Introduction to Statistical Learning: With Applications in R by Gareth James, Trevor Hastie, Robert Tibshirani and Daniela Witten

Miguel Lacerda, chief data scientist at Differential Capital believes this book is the best place to start any data science journey. “It provides a more accessible introduction to a broad range of popular statistical learning techniques, starting with the very basics and progressively building on these with clear use cases. Each chapter concludes with a programming lab, so you get to put the theory into practice immediately. A must-have for statisticians and non-statisticians.”

See book here.


Deep Learning by Ian Goodfellow by Yoshua Bengio and Aaron Courville

Miguel also recommends this comprehensive book on deep learning. It is written by three leading researchers in the field and sets the scene with foundational machine learning concepts. The reader is taken on a tour of various techniques as they are currently used by practitioners in industry. The book concludes by providing a glimpse into the future of research on the topic. “This book will be the authoritative reference on deep learning for many years to come,” says Miguel.

See book here.

Information is beautiful by David McCandless

Dave Mullen, consultant at AWA digital recommends Information is Beautiful. “This is a classic that’s still an inspiration today. This book really expanded my horizons on visualising and telling stories with data.” First published in 2012, it has been revised with over 20 updates and new visualisations. It was picked as one of the best science books of the year by the Independent on Sunday. Facts are presented in image form and information is shown in a striking and very effective way. 

See book here.

Data Science from Scratch: First Principles with Python by Joel Grus

This Amazon bestseller is a good place to start for anyone wanting to get a good idea of what data science is all about, says John Barnes, head of analytics at AWA digital. Author Joel Grus makes the reader comfortable with the maths and the statistics at the heart of data science. He also offers insight into the hacking skills needed to get started as a data scientist. A former software engineer at Google, the author has become an expert on machine learning.

See book here.

Python for Data Analysis, 2e: Data Wrangling with Pandas, Numpy, and Ipython by Wed McKinney

John says this book is next in line. Here you will find the application of all of the concepts introduced in Data Science from Scratch. It provides many practical case studies and examples, giving you the opportunity to solve a range of data analysis problems. It is a next-level data science reference for readers ready to immerse themselves in the field. Author Wes McKinney is a former quantitative analyst and graduated from MIT with an S.B. in Mathematics.

See book here.

Data Jujitsu: The Art of Turning Data into Profit by DJ Patil

I don’t think we’re short of people with data science skills, I think we’re short of people who can sit in-between the business world and the data world and get the two working together in a more meaningful way,” says John. He likes this book for the way in which it covers the application of data science. “Too many reports and bits of information end up not going anywhere or never solving the root issues.” He believes this book addresses the gap between the business world and the data science field.

See book here.


Successful Analytics: Gain Business Insights by Managing Google Analytics by Brian Clifton

While this title is not new, John says it very importantly emphasises data quality. He refers to what in computer science is called GIGO or “garbage in, garbage out”, highlighting the need for proper input to ensure useful output. “It is not really data science but this book is relevant to analytics in general,” says John.

It will be of particular interest to those using Google Analytics, and helps in applying analytics data better. This is particularly helpful for organisations wanting to use data to make strategic decisions.

See book here.

Storytelling with Data: A Data Visualisation Guide for Business Professionals by Cole Nussbaumer Knaflic

AWA’s COO Johann van Tonder says, “Many years ago, I read an article in Harvard Business Review explaining that numbers don’t move people to action, stories do. And yet, most decks are just that – slide after slide of numbers and graphs.” While working at Google, Cole Nussbaumer Knaflic realised that some of the most talented analysts didn’t know how to make data come to life. This book packs a punch when it comes to explaining how to use stories for greater impact.

See book here.

The Visual Display of Quantitative Information by Edward R Tufte

This book is compulsory reading, if you can get your hands on a copy, says Johann. After working through this classic, you won’t quite slap together a graph in the same way as before, he says. It will show you how to make meaning jump out of your data visualisations. It contains theory and practice around the design of data graphics with 250 of the best – and worst – statistical graphics.

See book here.


Statistical Methods in Online A/B Testing by Georgi Zdravkov Georgiev

“It seems that most A/B testing practitioners educate themselves about statistical significance and related concepts by reading blog posts and listening to others in the industry,” says Johann. Chances are they’ve been misinformed along the way. On the other hand, most of us battle to read academic texts on this topic. This book sits in the middle, cuts through the jargon, breaks through (the many) misconceptions and builds the reader’s understanding from the ground up.

See book here.


If you’re looking for books covering topics under the umbrella of Conversion Rate Optimisation (CRO) and experimentation, read our blog ‘19 Recommended Books on Conversion Rate Optimisation‘.

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