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21 Data Science Books You Should Read in 2021

An Updated Collection of the Best Data Science Books to Read Right Now

Photo by Christin Hume on Unsplash
Photo by Christin Hume on Unsplash

Data and Artificial Intelligence continue to steal the limelight in LinkedIn’s 2020 Emerging Jobs Report, citing Artificial Intelligence Specialists and Data Scientists as the Top 1 and 3 emerging jobs in the U.S., respectively.

With companies realizing the value of data beyond the hype, we can expect to see Data Science and AI jobs postings and salaries keep rising in 2021.

Regardless of your background or skill level, data science professionals and enthusiasts-alike all need to keep sharpening the saw. This post attempts to collate some of the most helpful Books you can read to increase your data science proficiency.

Disclaimer: There are no affiliate links in this post. This post is for information purposes only.


Data Science Appreciation

These are some books meant for those who don’t have any background in Data Science. Moreover, these are books that are also well-suited for Business Leaders and Managers looking into applying concepts of Data Science in their workplace. The following books provide a high-level view of the Data Science process and some of the many applications in business.

1. The Art of Data Science – A Guide for Anyone Who Works With Data

By Roger D. Peng and Elizabeth Matsui

This book provides an excellent overview of the data analysis workflow. Moreover, it articulates well how despite the presence of many tools, data analysis is fundamentally an art, involving an iterative process where information is learned at every step.

Image from Amazon
Image from Amazon

2. Predictive Analytics – The Power to Predict Who Will Click, Buy, Lie, or Die

By Eric Siegel

This book offers a comprehensive yet accessible resource to anyone who wants to learn how predictive analytics work, dissecting many real-life applications from mortgage risk, terrorism, crime predictions, and politics, to name a few.

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Image from Amazon

3. Data Science for Business – What You Need to Know about Data Mining and Data Analytic-Thinking

By Foster Provost and Tom Fawcett

This is a must-read book for business people who want to have a better understanding of how data science can be used to achieve a competitive advantage.

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Image from Amazon

4. Data Smart – Using Data Science to Transform Information into Insight

By John Foreman

What’s interesting about this book is how it teaches data science concepts using none other than Microsoft Excel. All in all, the book shows a perfect illustration of how data science is inherently tool-agnostic.

It doesn’t matter what language, platform, or software you do your data science on, the fundamentals and math behind the algorithms remain the same.

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Image from Amazon

Math and Statistics

Who says grasping numerical concepts can’t be light and entertaining? Some of these math and statistics books are geared to give you a less intimidating introduction to many of the key concepts required to use data science in business.

5. Naked Statistics – Stripping the Dread from the Data

By Charles Wheelan

Statistics can sometimes be an daunting topic to dive into. Not only that, focusing on the details sometimes obscure the intuition behind the metrics we use at work. In this book, author Charles Wheelan clarifies key concepts like inference, correlation, and regression analysis in a fun and less dreadful way.

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Image from Amazon

6. Practical Statistics for Data Scientist — 50+ Essential Concepts Using R and Python

By Peter Bruce, Andrew Bruce, and Peter Gedeck

This is a practical high-level guide to get you familiarized with statistical methods used by Data Scientists. While it does not provide an in-depth explanation of the mathematical concepts, it is nonetheless an excellent reference that allows you to continue learning statistics elsewhere.

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Image from Amazon

7. The Art of Statistics – How to Learn from Data

By David Spiegelhalter

Written by the well-renowned statistician, David Spiegelhalter, The Art of Statistics shows how we can derive insights from raw data and how we can approach a variety of problems using statistics.

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Image from Amazon

Data Visualization and Storytelling

A key aspect of the data science process is data visualization. Many might settle for bland matplotlib and maybe fancy some seaborn plots every once in a while, but these books will tell you there’s indeed a proper way to do data visualization. Getting the execution scripts right is one thing, but designing charts and dashboards to get the right insights out is another thing.

8. Storytelling with Data – A Data Visualization Guide for Business Professionals

By Cole Nussbaumer Knaflic

This is a must-read book for anyone who wants to get better at presenting information in a clear, concise, and graphical way. This book teaches you the fundamentals of data visualization and how to effectively communicate with data, complete with numerous real-world examples.

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Image from Amazon

9. Fundamentals of Data Visualization – A Primer on Making Informative and Compelling Figures

By Claus O. Wilke

This book presents the basic principles alongside good and bad contrasting examples of data visualization. It is a book that can help you understand the rationale behind an effective visualization and can teach you to design more meaningful plots that get the right message across.

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Image from Amazon

10. Good Charts – The HBR Guide to Making Smarter, More Persuasive Data Visualizations

By Scott Berinato

This book draws insights from research in visual perception and neuroscience and attempts to explore how people perceive good and bad charts differently. It teaches frameworks on how to make persuasive visualizations along with case studies to illustrate them.

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Image from Amazon

11. MakeoverMonday — Improving How We Visualize and Analyze Data, One Chart at a Time

By Andy Kriebel

This book is an extension of the #MakeOverMonday project where members of the data visualization community share their improved take on existing charts and data. It emphasizes that while there’s variability in designing visualizations, there are key techniques that you can follow to make sure your chart makes an impact.

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Image from Amazon

Machine Learning

If you are ready to get your feet wet into making predictions with data, these books will give you an in-depth exposition of Machine Learning concepts with practical application and hands-on examples.

12. Introduction to Machine Learning with Python

By Andreas C Muller and Sarah Guido

This book is an excellent resource that can get you up to speed with the basics of the most widely used machine learning algorithms, including techniques on how to process data, advanced methods for model evaluation and parameter tuning, and principles on creating your modeling workflow. It is beginner-friendly with no assumption that the reader has a heavy programming background. Not to mention, the accompanying GitHub repository is undeniably useful for learning.

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Image from Amazon

13. The Hundred Page Machine Learning Book

By Andriy Burkov

It is a condensed resource for machine learning concepts perfect as a go-to handbook for managers or software developers looking to integrate ML pipelines into their projects.

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Image from Amazon

14. Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow

By Aurelien Geron

Another one of those O’Reilly books that offer a practical guide to learning ML coupled with clear conceptual explanations and code implementations. It helps you build a solid understanding of machine learning through a variety of hands-on exercises implemented with Scikit-Learn and TensorFlow.

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Image from Amazon

15. AI and Machine Learning for Coders — A Programmer’s Guide to Artificial Intelligence

By Laurence Moroney

A must-have book for programmers breaking into the Artificial Intelligence field or for anyone who has a strong technical background that is looking into applying AI in their projects. Primarily based on TensorFlow, author Laurence Moroney walks you through common AI and ML concepts as applied in computer vision, natural language processing, sequence modeling, to name a few.

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Image from Amazon

16. The Elements of Statistical Learning – Data Mining, Inference, and Prediction

By Trevor Hastie, Robert Tibshirani, et al.

Probably, one of the more academic looking books on this list. However, we can’t deny the immense knowledge contained in this book. It is a valuable resource for statisticians or anyone interested in data mining.

Sufficiently technical and can serve as a good lasting reference that you definitely should keep on your shelf.

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Image from Amazon

Deep Learning

Deep Learning is perhaps the hottest aspect of data science nowadays. This subset of machine learning is responsible for many of the high-profile applications we see today from self-driving cars, deep fakes, to image recognition. The following books are excellent resources to get you started on this topic.

17. Deep Learning with Python

By Francois Chollet

Written by the creator of Keras, Deep Learning with Python helps you to build an understanding of deep learning from scratch. It contains detailed examples with practical recommendations and high-level explanations to allow any beginner to start their deep learning project.

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Image from Amazon

18. Foundations of Deep Reinforcement Learning – Theory and Practice in Python

By Laura Graesser and Wah Loon Keng

A rather advanced textbook that explores Deep Reinforcement Learning, where artificial agents learn to solve sequential decision making. A well-written book for anyone who has working knowledge of machine learning and wants to solve problems using Deep RL.

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Image from Amazon

19. Deep Learning Illustrated – A Visual, Interactive Guide to Artificial Intelligence

By John Krohn, Grant Beyleveld, and Aglae Bassens

This is a practical reference that can help you build your intuition on deep learning algorithms. In this visual, interactive guide, you will learn theories together with examples you can run through on the accompanying Jupyter notebooks.

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Image from Amazon

Programming

This section is an exception from the title.

While these books originally came from the software engineering field and were written with examples from languages other than Python and R, concepts here are universal and can be used to level up your programming proficiency.

Many Data Scientists come from non-tech backgrounds. Hence, it is not uncommon to see messy code when reviewing ML notebooks. The remaining two books to complete this list are classic references used by many programmers to rethink and improve the way they code.

20. The Pragmatic Programmer — Your Journey To Mastery

By David Thomas and Andrew Hunt

This is a timeless book that "examines the very essence of software development, independent of any particular language, framework, or methodology". Not only does it discuss techniques to keep your code adaptable and easy to reuse, but it also explores topics on personal responsibility and career development.

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Image from Amazon

21. Clean Code – A Handbook of Agile Software Craftsmanship

By Robert C. Martin

This book explains the principles, and best practices of writing clean code illustrated using several case studies. Important for data professionals working in a collaborative setting, writing clean code is a skill that can prepare you and your team to produce better data products.

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Image from Amazon

Other interesting data science books you might like:


Are there other books you think that should be on this list? 📚

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