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Top 18 Must-Read Data Science Books You Need On Your Desk

The top 18 data science books by datapine

“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert

We live in a world saturated with data. At present, around 2.7 Zettabytes of data are floating around in our digital universe, just waiting to be analyzed and explored, according to AnalyticsWeek.

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your organization flourish.

The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale. By acquiring a deep working understanding of data science and its many business intelligence branches, you stand to gain an all-important competitive edge that will help to position your organization as a leader in its field. It is also wise to clearly make a difference between data science and data analytics in a business context so that the exploration of the fields brings extra value for interested parties.

That being said, here, we explore 18 of the best data science books in the world today, highlighting the very features, topics, and insights that make each of these institutional data-centric bibles crucial for the success of your career and business.

So, what makes the best book for data science? Read on and find out.

Why You Need To Read Data Science Books

Before we tell you why each of our entries makes the best books on data science, it’s important to give you a little context on this most exciting of modern fields.

In 2013, less than 0.5% of all available data was analyzed, used, and understood. Even now, there are colossal streams of data yet to be explored – a level of insight that could prove groundbreaking if used in the right way. For savvy data scientists, the potential that comes with unlocking this seemingly infinite ocean of information is enormous.

Data science, also known as data-driven science, covers an incredibly broad spectrum. This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining.

By understanding all of the key elements of data science and being able to apply these methods to every aspect of your business, both internal and external, you will reap a wide range of long-term results, ensuring you remain relevant as well as competitive in the process.

We gave you a curated list of our top 19 data analytics books, top 20 data visualization books, and top 20 SQL books – and, as promised, we’re going to tell you all about the world’s best books on data science.

If you'd like to acquire a sound practical understanding of data science or take your existing skills to exciting new heights, these are must-reads.

Without further ado, here are our top data science books.

1) "Deep Learning" by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Deep learning by Goodfellow, Bengio, Courville

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Best for: This best book to learn data science is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field.

Authored by a collective of prolific data science experts (Ian Goodfellow, Yoshua Bengio, and Aaron Courville) Deep Learning offers a wealth of insight into a broad range of subjects, and its this scope that makes it one of the planet's best books on data science.

From the fundamental practical aspects of data science, right to complex networks and the application of machine learning in business and beyond, this data science book is as comprehensive as it is intriguing.

Every chapter is broken down into digestible sections, and if you’re looking to gain an extensive base knowledge of the most cutting-edge elements of the field – this is the book for you.

2) "Advanced R" by Hadley Wickham

Data science book: Advanced R by Hadley Wickham

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Best for: Budding 'R' users and those looking to improve their overall programming talents and analytical skills as well as peruse the intricate nuances of this invaluable data-driven language.

For those embarking on a journey to master the art of the ‘R’ language – a statistical computing program and framework for increased BI-based success – Advanced R is intuitive, easy to follow, and will give you a well-rounded overview of this invaluable area of data science.

The author, Hadley Wickham, wrote chapters that are logical and clear, the jargon is kept to a minimum, and the book’s practical nature is a recipe for success – an essential addition to your bookshelf.

3) “The Art of Statistics: How to Learn from Data” by David Spiegelhalter

Best books for data science: “The Art of Statistics: How to Learn from Data” by David Spiegelhalter

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Best for: Anyone looking to understand statistical thinking in the real world. 

No data science process would be successful without the use of statistics. Advanced machine learning technologies utilize statistics to turn the data into actionable insights that will help draw conclusions to minimize errors and ensure success. Taking that into account, our list wouldn't be complete without the best statistics book for data science.

Under the premise that statistical literacy is more important than ever in today’s context, renowned statistician David Spiegelhalter wrote “The Art Of Statistics: How To Learn With Data”. 

This piece serves as a perfect introduction to the world of statistics without getting lost in the mathematical aspect of it. Drawing from multiple real-world examples, the author explains how experts in the field have been using data to answer multiple questions and teaches readers how they can do the same by being able to understand the numbers, ask the right questions, and manage expectations and assumptions, among other useful knowledge. 

Reviewers of this book have defined it as the perfect transition for anyone looking to get into the field of data science that needs to previously develop their statistical thinking. Regardless, the piece also works perfectly for any layperson that wants to understand statistics as a whole.  

4) "Machine Learning Yearning" by Andrew Ng

Data science book: Machine Learning Yearning

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Best for: Someone who has become all too aware of the machine learning and artificial intelligence craze but needs to get a grip on the subject. Amongst the best books for data science if you’re looking to hit the ground running with autonomous technologies.

Driven by the acquisition and processing of complex information, machine learning is an area of data science that has emerged monumentally in recent years. In fact, 20% of C-level executives worldwide are already using machine learning to make it a core part of their business.

With artificial intelligence changing the face of both our personal and professional lives, understanding the concept of machine learning and how silos of big data can be used to create autonomous, self-evolving machine-learning systems is essential if you want to grasp the importance of data and how it’s used in the modern world.

Written by renowned computer scientist Andrew Ng, this gripping read not only offers an accessible introduction to machine learning and big data, but also proves an excellent resource for collecting data, utilizing the power of deep end-to-end learning, and facilitating the sharing of key insights with a machine learning system.

5) "The Signal And The Noise: Why So Many Predictions Fail – But Some Don’t" by Nate Silver

best data science book: The Signal and the Noise by Nate Silver

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Best for: The CEO, Chief Digital Officer, Chief Information Officer, or a company owner looking to seriously enhance their predictive analytics skills, both practically and theoretically.

A New York Times Best Seller – and for good reason – The Signal and the Noise is a masterclass in using the power of big data analytics to make valuable predictions in an informed and potent way. It’s also amongst the best books on data science around.

Crafted by American statistician Nate Silver, a spokesperson famed for successfully predicting the 2012 US Presidential election results, this book uncovers the genuine art and science of making predictions from data. Peppered with real-world case studies, interesting big data examples in the realm of data prediction, and citations of epic data-based failures, this book shows the reader how to filter out the noise and hone in on the right insights to make projections that not only matter, but also ensure sustainable levels of success. A top data science book for making sense of the overwhelming rafts of data that form the beating heart of our data-driven age.

6) “Introduction to Machine Learning with Python: A Guide for Data Scientists” by Andreas C. Müller and Sarah Guido

Data science book: “Introduction to Machine Learning with Python: A Guide for Data Scientists” by Andreas C. Müller and Sarah Guido

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Best for: Developers with medium to advanced programming knowledge looking to dive into machine learning and apply it to their work. 

With the wide range of tools available to us today, building Machine Learning applications doesn’t need to be a task segregated only to companies with huge resources. According to our next book, all you need to build efficient ML applications is programming knowledge and the right tools. 

Written by authors Andreas C. Muller and Sarah Guido, “Introduction To Machine Learning With Python” aims to teach developers the practical knowledge they need to build exciting ML applications using Scikit-lear, Pandas, Numpy, and Matplotlib. In doing so, the publication also covers important Machine Learning concepts and techniques without getting lost in the “math behind them” serving as the perfect introduction to ML for developers that want to use it in their daily work but don’t want to dive deep into the topic.  

Paired with that, the book also provides knowledge of the whole workflow of an ML project from processing to implementation. This information is particularly useful when a certain business process needs to be improved using ML technologies. It is important to note, that this book requires readers to have a certain amount of programming knowledge.  As recommended by the authors themselves, having previous knowledge or familiarity with NumPy and matplotlib libraries can help readers extract the maximum potential out of the information contained in this book. 

7) "Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython" by Wes McKinney

data science book : Python for Data Analysis

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Best for: Someone with a sound working knowledge of Python who wants to understand how to use the language to enhance their data insights. Deservedly on our list of the top books for data science.

As one of the world’s most revered and widely used high-level programming languages, Python is a robust and versatile tool, particularly in the modern age.

The brainchild of American statistician and data scientist Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython takes the reader deep into the realms of the language and its enormous potential for manipulating, processing, cleaning, and crunching data in Python.

If you’re looking to use Python as an effective means of solving a broad set of data analytics problems that will enhance the intelligence and productivity of your business, this book boasts a host of actionable tips and thought-provoking takeaways.

Its 3rd edition which was recently published in September 2022, is updated for Python 3.10 and pandas 1.4. It also includes practical case studies on "how to solve a broad set of data analysis problems effectively". A top data science book for anyone wrestling with Python.

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8) "Storytelling With Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic

best data science book: Storytelling With Data by Cole Nussbaumer Knaflic

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Best for: Budding data visualists and those looking to create inspiring narratives with their data for a wide range of audiences and outcomes. An essential data science book for your reading list.

Created by storytelling expert Cole Nussbaumer Knaflic, this methodical handbook is not only entertaining, but it also provides deep-rooted insights into a branch of data science that is often overlooked: the art of storytelling through metrics.

One of the best books for data science you’re likely to read this decade, Cole explains approaches to getting rid of unnecessary data that obscures clear communication and using these insights to build an effective narrative that connects with users on an undeniably personal level. The modern age content writer’s dream ticket. A must-explore book for data science that is as intriguing as it is rewarding.

While you’re waiting to get your hands on your copy, take a look at our dashboard storytelling tips to find out how to tell a great story after learning how to make a dashboard in nine simple steps.

9) "Python Data Science Handbook" by Jake VanderPlas

Best data science book: Python data science handbook by Jake VanderPlas

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Best for: Anyone looking to sharpen, extend, and upgrade their existing Python skills and gain an edge on the competition by acquiring a deeper, more relevant insight into this crucial subject.

Written by software engineer Jake VanderPlas, this best book on data science is a gem for anyone that uses Python as an everyday part of their job role or business strategy.

This most extensive, practical, and rewarding data science book of its kind will let you uncover a plethora of new methodologies while building on your core knowledge of Python in a BI context. In its second edition being released on January 2023, the book offers insights into all Python-related libraries such as IPython, NumPy, pandas, Matplotlib, scikit-learn, and others related.

If you want to become an Ipython legend, this is one of the best books on data science on offer at the moment. And the great thing about the Python Data Science Handbook is the fact that you can use it for quick reference while you’re tackling important tasks or projects. A data science book that just keeps on giving long after you finish it.

10) "Artificial Intelligence in Practice" by Bernard Marr

One of the best data science books: Artificial intelligence in practice by Bernard Marr

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Best for: Those looking for a practical means of understanding how artificial intelligence serves to enhance data science and use this knowledge to improve their data analytics strategies.

As the brainchild of data, legend Bernard Marr creates Artificial Intelligence in Practice as a masterclass in using data science for supreme business intelligence.

Based on 50 real-life business intelligence examples and case studies, this book is wonderfully crafted, incredibly entertaining, insightful, enlightening, intriguing, and result-driven.

Amongst Bernard Marr’s greatest offerings to date, this truly is one of the best books for data science in existence today – a must-read in today’s world.

11) "Data Science For Dummies" by Lillian Pierson

Data science for dummies by Lillian Pierson

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Best for: Anyone looking for a fun and understandable yet comprehensive introduction to data science in practice. This book covers all the basics – and a little more!

If you’re relatively new to data science and looking to gain a sound working knowledge of the subject, Data Science For Dummies is the resource you need on your desk at all times.

The author, Lillian Pierson, created a workbook with a focus on business cases, that will help you hone your skills in areas including big data analytics, Hadoop, MapReduce, Spark, MPP platforms, and NoSQL, and machine learning (ML) or artificial intelligence (AI) good practices. It is one of the greatest in the biz for the ambitious novice looking for a quick yet meaningful data science fix.

The book is already in its 3rd edition, which includes more advanced information regarding data science strategies and monetization. The introductory information from editions 1 and 2 complemented with the newest knowledge from the 3rd edition will help readers in successfully transforming massive amounts of company data into valuable insights. 

12) "Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level" by Tom White

data science book Hadoop, the Definitive Guide by Tom White

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Best for: The wide-eyed, budding Apache Hadoop warrior with an unquenchable thirst for creating scalable systems from data.

In one of the best books on data science regarding processing language, Tom White takes his readers on a data-based journey to help them understand the importance of Hadoop and how, if used wisely, it can do a multitude of incredible things.

These incredible things include the ability to build and manage scalable systems with Hadoop and successfully run large Hadoop clusters. As it’s so well-formatted and digestible, dipping in and out of the various chapters of the book is as simple as it gets.

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

Best books on data science: "Data Science from Scratch: First Principles with Python" by Joel Grus

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Best for: Intermediate programmers that want to dive into the world of data science and machine learning. 

Next on our list, we have a data science with Python book written by author Joel Grus. This book serves as an introduction to the world of ML and DS and it aims to teach programmers how to build their own algorithms from scratch using Python. 

Readers of this book can expect to learn the basics of Python and data science and how common techniques can be applied to different scenarios, math, probability, and statistics concepts needed in the field of DS and ML, as well as other technologies such as NLP, network analysis, MapReduce, and more.  

The book recently published its second edition updated for Python 3.6 and includes new material on deep learning, statistics, and natural language processing. While this piece is recommended for programmers, previous knowledge in Python or ML is not mandatory. The publication includes crash courses on these topics to get the reader up to speed. However, a medium to high understanding of math and statistics can make the reading experience much better and more productive.

14) “Machine Learning Simplified” by Andrew Wolf

Data science book: machine learning simplified by Andrew Wolf

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Best for: Developers, students, lecturers, or anyone looking to learn the inner workings of ML.

Recently published in 2022, our next book written by Andrew Wolf is the perfect introduction to the inner workings of machine learning. It provides a simplified view of this technology and how it can help solve multiple problems across several industries. 

Readers of this best data science book for beginners can expect to find a well-constructed introduction containing key concepts, such as statistics, algorithms, and more, that will help make sense of the information contained later on. The body of the book contains a useful set of examples of machine learning to explain complex concepts that go into the process in a way that is simple and understandable. Paired to this, Wolf provides various hyperlinks that readers can visit to experience ML by themselves, making the reading experience much more fulfilling. 

Since its recent publication, the book has already been highly praised by readers, reaching almost 5/5 stars rating on popular platforms. What reviewers enjoy the most is the fact that it is well-written and provides the necessary knowledge to get started on the topic of ML without the need for previous technical knowledge. Definitely, amongst the best data science books for beginners!  

If you want to get your hands on this book ASAP, a free PDF version is offered on the book's website.

15) "Business Skills For Data Scientists: Practical Guidance in Six Key Topics" by David Stephenson, PhD

Data science books: "Business Skills For Data Scientists: Practical Guidance in Six Key Topics" by David Stephenson, PhD

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Best for: Data scientists looking to deepen their business skills. 

It is no secret that data scientists are amongst the most sought-after professionals by businesses today. That said, many scientists fail because they lack the necessary business skills to thrive at an organizational level. With that in mind, experienced data scientist David Stephenson wrote his latest book “Business Skills For Data Scientists”. 

The author put together more than 20 years of experience in the field into 304 pages that aim to provide data scientists with the necessary skills to successfully use their technical knowledge in a business context. The book is divided into 6 topics that are presented in the potential order in which a data scientist will have to face them within a company. The topics include: 

  1. Company: Expectations about the role of a data scientist at an organizational level and how technical skills can be turned into business value. 
  2. Colleagues: Challenges on how to relate to different individuals within an organization, and how to handle misunderstandings and cultural differences. 
  3. Storytelling: How to efficiently communicate your projects through efficient oral and visual presentations (avoid cluttering and use the right charts and graphs). 
  4. Expectations: How to maintain the trust of stakeholders during the completion of a project and set expectations regarding the final product. 
  5. Results: Teaches the principles and techniques needed to choose the right data science projects and how to get them to successful completion. 
  6. Careers: Common questions data scientists ask the author such as how to choose the top job opportunities, how to efficiently build their CVs, and more. 

Don't miss this amazing data science business book!

16) "R For Data Science" by Hadley Wickham and Garrett Grolemund

Data science book: R for data science by Hadley Wickham and Garret Grolemund

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Best for: Anyone looking to delve deeper into data science and learn to organize digital insights more effectively while extracting even greater value from the information available at their fingertips. One of the most thought-provoking best books for data science on our list.

The authors, Garrett Grolemund and Hadley Wickham created one of the top data science books for the digital age. Offering a host of unique insights based on many-core avenues of the field, R for Data Science will tell you all you need to know to transform, transpose, adapt, and structure your data for success.

By learning how to manage your data more efficiently and strategically, you’ll become empowered to make your insights more valuable, more impactful, and exponentially more potent. And this data science R book will help you get there, step by step.

Don’t miss out – it is one of the world’s best books on data science, after all.

17) "Automate This: How Algorithms Came To Rule Our World" by Christopher Steiner

data science book: Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

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Best for: The technically-minded wizard or digital tech enthusiast looking to bridge the gap between big data analytics, complex algorithms, and the way these elements will shape our future lives. One of the best books for data science if you’re obsessed with the inner workings of algorithms.

Data science is largely about predictions, but a significant part of this ever-expanding discipline also boils down to sophisticated algorithms.

In this thought-provoking and, in many ways, timeless work of data science prose, author, and prolific programmer Christopher Steiner explains how algorithms are increasingly being used to take on high-level pursuits that were once tackled only by human beings with niche training – areas including medical diagnosis and foreign policy analysis.

Once you pick it up, Automate This: How Algorithms Came to Rule Our World is nigh on impossible to put down, gripping you from start to finish with its intuitive style and host of stunning observations on how, in today’s world, algorithms have far exceeded the expectations of their creators. A must for any budding data scientist’s home library. An inspiring addition to this list.

18) "Inflection Point: How the Convergence of Cloud, Mobility, Apps, and Data Will Shape the Future of Business" by Scott Stawski

data science books: Inflection Point, by Scott Stawski

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Best for: The budding data manager or data miner with a desire to make sense of information in the modern age and beyond. Books for data science don’t get any better than this.

As far as books on data science go, this is perhaps one of the most forward-thinking ones in existence.

Penned by Scott Stawski, a data management leader at Hewlett Packard, Inflection Point focuses on how swift changes in cloud computing, big data, mobile devices, and apps are morphing the way businesses compete. With mind-blowing observations, astute predictions, and valuable takeaways, this data science book is a must-read for anyone trying to sift through silos of information and get ahead in today’s – and tomorrow’s – world. All future data science books should, well, take a leaf from this book.

For a quick glance at our 18 best books on data science, here’s a summarized list of these incredible resources:

  1. "Deep Learning" by Ian Goodfellow and Yoshua Bengio and Aaron Courville
  2. "Advanced R" by Hadley Wickham
  3. "The Art Of Statistics: How To Learn From Data" by David Spiegelhalter
  4. "Machine Learning Yearning" by Andrew Ng
  5. "The Signal And The Noise: Why So Many Predictions Fail – But Some Don’t" by Nate Silver
  6. "Introduction To Machine Learning With Python: A Guide For Data Scientists" by Andreas C. Müller and Sarah Guido
  7. "Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython" by Wes McKinney
  8. "Storytelling With Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic
  9. "Python Data Science Handbook" by Jake VanderPlas
  10. "Artificial Intelligence in Practice" by Bernard Marr
  11. "Data Science For Dummies" by Lillian Pierson
  12. "Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level" by Tom White
  13. "Data Science For Scratch: First Principles With Python" by Joel Grus
  14. "Machine Learning Simplified" by Andrew Wolf
  15. "Business Skills For Data Scientists: Practical Guidance in Six Key Topics" by David Stephenson, PhD
  16. "Automate This: How Algorithms Came To Rule Our World" by Christopher Steiner
  17. "Inflection Point: How the Convergence of Cloud, Mobility, Apps, and Data Will Shape the Future of Business" by Scott Stawski
  18. "R For Data Science" by Hadley Wickham and Garrett Grolemund

“Information is the oil of the 21st century, and analytics is the combustion engine” – Peter Sondergaard

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Kickstart Your Data Science Journey Today!

When it comes to data science, there is an incredible amount to learn. In our opinion, these 18 best data science books will help you gain the knowledge you need to get started on your long, winding, and incredibly rewarding journey toward data-driven enlightenment.

Armed with your newfound understanding of data analytics, these publications will bestow you with the power to tap into the potential of data for business intelligence, creating a wealth of strategic advantages for your business, complemented by cutting-edge online BI tools.

If you’re looking to make your company smarter, savvier, more sustainable, and more productive, these top 16 business intelligence books will make a great start.

And if you want to tackle deeper into the practical realm of data science, try our business intelligence software for a 14-day trial, completely free!