7 Best Data Science Books to Read in 2022 (Ranks and Reviews)

data science books
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Want to get right to the best data science books? My favorite choices are Data Science From Scratch and Naked Statistics.

Data science is an interesting field. You will find that the biggest challenge in learning data science is the fact that the field can get pretty complex at times. Some people even lack a standard definition for it.

Data science has emerged as the most sought-after job skill of today’s hiring managers. The recently published Indeed job market trends report revealed that interest in data science roles has risen by a staggering 300% in 2016 alone.

This rapid increase in demand has created a tremendous shift in the organizations’ view toward data science and its potential to solve their toughest business problems.

And it continues to gain traction. It is no longer a niche subject, and today, more than ever, professionals are being required to learn about this concept if they want to remain relevant in the workforce.

However, what most people don’t know is that you don’t need to get stuck in school for two years to learn data science. You can actually check out a couple of books from Amazon and get started.

Today, I am here to help with this problem by listing out the best data science books which you should check out if you are interested in learning more about data science this year and beyond.

Disclaimer: Please note, I may receive affiliate commissions from Amazon LLC if you decide to purchase books through checkout links available on this page. However, these commissions are at no extra cost to you and my goal is to give you the very best reviews and recommendations. Read more about my disclaimer here.

Let’s get started.

What are the Best Data Science Books?

Here are my top picks for the best data science books to read this year:

1. Data Science From Scratch.

The world of data science awaits. You just need to learn how to use the tools, how to think about the data and problems, and how to handle all the messy bits in between.

Data Science from Scratch teaches you these skills by showing you how many of the most fundamental data science tools (e.g., NumPy) and algorithms (e.g., linear regression) work by implementing them from scratch — all in Python 3, with no library dependencies other than the Python Standard Library.

This second edition of Data Science from Scratch shows you how to implement algorithms and tools used in data science using Python. This book also teaches many principles that underlie these tools, so you not only understand what the algorithms do but why they work.

Along the way, you will create your own functions and libraries, enabling you to learn how data science is typically practiced.

You’ll begin with the Python programming language, get hands-on experience working with real datasets, understand basic machine learning techniques and statistical inference methods, and discover what’s really under the hood of libraries such as Pandas.

Then it’ll move on to solving linear equations, detecting trends, and creating functions that determine whether certain conditions exist or not.

Next, it’ll look at reading from, manipulating, storing data in, and loading data from a database before moving on to logistic regression, which is suitable for predicting continuous outcomes like the performance of a stock market.

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2. Practical Statistics For Data Scientists.

Statistics is a key part of data science, yet few data scientists have formal statistical training.

Courses and books on basic statistics rarely cover the topic from a data science perspective. Many use mathematics to teach simple concepts that can be understood just as well — and sometimes better — without it.

This book tells you how to apply statistical methods to data science, tells you how to avoid their misuse, explains what’s important and what’s not, and gives you advice on structuring your work.

The book offers a hands-on introduction to the concepts and tools statistics and data science students need to know — and those that aren’t statistics or computer science majors will find particularly useful.

The book presents a detailed overview of 50 key statistical methods and features real data examples covering topics such as social network analysis, text mining, machine learning, and big data sets.

The author describes each technique in plain language and provides Python code in kernels to illustrate the method.

He explains how to avoid the errors that result from commonly misapplied techniques and how to interpret what’s important and what’s not.

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3. Naked Statistics.

Former White House Deputy Chief of Staff Karl Rove has called statistics the “lingua franca of the twenty-first century.” But what if you want to read this language?

To speak and understand statistics, you need to be fluent in a particular dialect—one that has historically been as difficult to learn as Mandarin Chinese. (In fact, Mandarin is easier.)

However, once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, the chief economist at Google, has actually called “sexy.”

From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds.

How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism?

As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. Naked Statistics takes a behind-the-scenes look into the fascinating world that is statistics.

With his trademark wit and style, Wheelan paints a clear picture of how basic statistical tools can be used to make our lives better, whether we’re trying to catch cheaters or deciding whether to watch the new movie. Statistics is the haute couture of modern science.

These sophisticated mathematical tools can shed new light on the most important questions in our society, from helping doctors analyze their patients’ health to detecting cheating on standardized tests and uncovering fraud. But too often, this power is hidden behind a thicket of jargon and complex formulas.

Charles Wheelan, who was twice named one of the best young prodigies by Fortune magazine, strips statistics to its bare essentials so you don’t have to be afraid of it again. In clear language, he explains how statisticians think about data and what we should think about when we look at numbers.

Statisticians use numbers every day to solve problems and answer questions, but these questions and answers are usually shrouded in a dense fog that makes people feel like they can’t understand statistics or use it to defend their own views. Wheelan will change that.

You‘ll learn how to sniff out garbage in the form of bad social science or shoddy numbers, and be empowered with enough knowledge so that you can track down your own answers using real statistical methods from top-quality academic articles — not just the sound bites that make headlines and shape opinions.”

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4. Becoming a Data Head.

Becoming a Data Head is the first book you need to read if you want to talk and think critically about data science & statistics. In this book, authors Alex Gutman and Jordan Goldmeier introduce the foundational terminology of data science in the form of a story.

The sloth (a much-loved animal native to South America) wakes up one day to find that all of her friends have been rounded up by evil ape-like creatures called Snarks.

To make matters even more perilous, these Snarks have invented a new language ― one with words like SMART, SIMPLEX, TRANSFORM, LIFTING, INDEPENDENT VARIABLES, and SLACK ― in order to confound the sloths’ ability to deploy their full array of skills for getting out of trouble.

Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics, and Machine Learning is the must-have resource for anyone trying to learn data science.

It demystifies some of the more difficult subjects surrounding statistics, data science, and machine learning and makes it digestible for newcomers. It’s time to become a data head!

This book is infused with the wonderment and personality of its authors, who believe that data science is not a magical art but rather an exacting science.

This book will fill you with their infectious wonder as they teach complex concepts, demystify difficult terms, and articulate advanced statistical analysis.

They also teach valuable lessons in language and critical thinking through many entertaining examples that span from the basics of Excel formulas to current research on artificial intelligence.

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5. Data Science For Business.

Data Science for Business combines the core concepts of statistical modeling and data engineering into a single text that is accessible to every business leader.

Whether you are seeking a primer on data-analytic thinking or information on specific data-mining techniques, Data Science for Business will help you master this essential activity.

Data science isn’t just for large corporations with endless resources. Today, it’s a necessity for any organization that wants to collect, process, and apply data to make smarter business decisions.

Fortunately, the data science revolution has been led by people who are committed to making this valuable field of study accessible to every business.

If you want your business to take advantage of the power of data, Data Science for Business is an excellent place to start.

Since the only constant in the business world is changing, it’s important to have tools at your disposal that can keep up with these changes and give you an edge.

The techniques and statistics in Data Science for Business are the ones most commonly used by businesses worldwide, so it will help you keep pace.

Add this book to your repertoire of resources and data science skills today, and you’ll be one step ahead for tomorrow.

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6. Storytelling with Data.

Storytelling with Data gives you the tools you need to create a powerful impact with your data visualizations. It allows you to transform data into meaningful communication, rather than just dull charts on a page.

It teaches you how to tell an effective story to your users and how to present that story in the best possible way — giving them a glimpse into what your business does, why it matters, and how they can be affected by it.

The old saying “a picture is worth a thousand words” is often true. The business world is competitive, and the more you can do to differentiate yourself from your competition, the more successful your business will be.

One of the most effective ways you can distinguish yourself from other business people is by communicating with data effectively.

This means making data a focal point of communication, rather than an afterthought or just another piece of information that does not relate to the big picture.

The effective communication of data will give organizations a competitive edge, and this book will show you how to do it right!

This book can help you become a better visual storyteller by putting storytelling first and data visualization second. In doing so, the story comes alive, and your audience will engage with the data more effectively.

The authors of this book believe that storytelling is key to effective communication, and their elegant writing style will likely win you over as well.

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7. Ace the Data Science Interview.

This invaluable book should give you every tool you need to ace a data science interview, and it’s one that every aspiring data scientist would be wise to read through — its advice is that good.

If you are looking for a job in Data Analytics and have teetered back and forth about grabbing the book due to how “expensive” it is, I encourage you to look at the title again.

Ace the Interview is an amazing use of $32 and will likely be the last book you will purchase on this subject.

In the end, don’t let these different types of questions scare you. All Data Scientists have to be strong in Statistics and Probability, so knowing how to answer these questions can give you a leg up over other job candidates.

Hopefully, this guide will help you better understand when it’s appropriate to use each method and has help you pick the most effective tools for your next Data Science job interview.

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That’s it for my list of the best data science books.

Today, Data Science has become the buzzword in this field as well as others. Many new terms and concepts are being added to make it more advanced than its prior versions.

The books introduced here will not only help you understand data science but also give you a thorough insight into the top jobs of today’s market.

Data Science is becoming more and more important nowadays. Big Data has generated lots of business opportunities for every discipline and industry so every person should be aware of this new technology.

The books that you find in the list are very helpful in teaching the basics of data science, describing the concepts, and providing great explanations and examples to understand this technology easily.

Here is a final summary of my top picks:

What did you think of this list? Are there any books not mentioned? Let me know in the comment section.

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