100 books like Be Data Literate

By Jordan Morrow,

Here are 100 books that Be Data Literate fans have personally recommended if you like Be Data Literate. Shepherd is a community of 9,000+ authors and super readers sharing their favorite books with the world.

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Book cover of Competing on Analytics: The New Science of Winning

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Who am I?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.

By Thomas H. Davenport, Jeanne G. Harris,

Why should I read it?

1 author picked Competing on Analytics as one of their favorite books, and they share why you should read it.

What is this book about?

You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new…


Book cover of Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Who am I?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

Data scientists and analytics specialists are great at building models and algorithms, but often wrap them in a presentation or dashboard that diminishes their value and reduces the likelihood of their work being adopted. This book encourages practitioners to always consider the last mile and to pay as much attention to presentation and aesthetics as we do to the model itself. 

By Brent Dykes,

Why should I read it?

1 author picked Effective Data Storytelling as one of their favorite books, and they share why you should read it.

What is this book about?

Master the art and science of data storytelling-with frameworks and techniques to help you craft compelling stories with data.

The ability to effectively communicate with data is no longer a luxury in today's economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative-to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories.

Narratives are more powerful than raw statistics, more enduring than pretty charts. When…


Book cover of People Skills for Analytical Thinkers

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Who am I?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

Since data science is, at its core, people helping people make decisions, it is essential that we can establish productive relationships with our stakeholders. This is a skill that needs to be given the same level of effort as we give to coding or statistics. Gilbert’s book is a great resource to help technically oriented people to advance their people skills.

By Gilbert Eijkelenboom,

Why should I read it?

1 author picked People Skills for Analytical Thinkers as one of their favorite books, and they share why you should read it.

What is this book about?

"For the engineer, scientist, or technology professional seeking to communicate better in the business world, this is the book you've been craving your entire career!" ”
— Douglas Laney, Innovation Fellow, West Monroe, and best-selling author of "Infonomics"

Your analytical skills are incredibly valuable. However, rational thinking alone isn’t enough.

Have you ever: Presented an idea, but then no one seemed to care? Explained your analysis, only to leave your colleague confused? Struggled to work with people who are less analytical and more emotional?

In these situations, people skills make the difference, and research shows these skills are becoming increasingly…


Book cover of The Practice of Management

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Who am I?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

Management as a skill is typically established and honed by osmosis, mimicry, and corporate crash courses. Data scientists pursuing management roles need to understand management from base principles to create meaningful change and establish productive team conventions. After almost 70 years, Drucker’s book still stands up as a foundational piece of reading.

By Peter F. Drucker,

Why should I read it?

1 author picked The Practice of Management as one of their favorite books, and they share why you should read it.

What is this book about?

A classic since its publication in 1954, The Practice of Management was the first book to look at management as a whole and being a manager as a separate responsibility. The Practice of Management created the discipline of modern management practices. Readable, fundamental, and basic, it remains an essential book for students, aspiring managers, and seasoned professionals.


Book cover of Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Tomasz Lelek Author Of Software Mistakes and Tradeoffs: How to make good programming decisions

From my list on big data processing ecosystem.

Who am I?

I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.

Tomasz's book list on big data processing ecosystem

Tomasz Lelek Why did Tomasz love this book?

Apache Spark has a very high point of entry for newcomers to the Big Data ecosystem.

However, it is a key tool that almost everyone is using for running distributed processing. I recommend everyone to read this book before delving into production solutions based on Apache Spark.

This book will allow you to alleviate many spark problems, such as serialization, memory utilization, and parallelization of processing.

By Sandy Ryza, Uri Laserson, Sean Owen , Josh Wills

Why should I read it?

1 author picked Advanced Analytics with Spark as one of their favorite books, and they share why you should read it.

What is this book about?

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for…


Book cover of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Who am I?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

For those intending to use R with an eye on the popular 'Tidyverse' suite of packages – which facilitate the handling, manipulation, and visualisation of data setsit's hard to go past this book. From the founding contributors of the RStudio/Tidyverse worlds, this is a great way to learn about this dialect of R against the overarching backdrop of statistical data analysis and data science.

By Hadley Wickham, Garrett Grolemund,

Why should I read it?

1 author picked R for Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along…


Book cover of The AI Factor: How to Apply Artificial Intelligence and Use Big Data to Grow Your Business Exponentially

Tim Vandehey Author Of Swipe: The Science Behind Why We Don't Finish What We Start

From my list on how technology is changing how we live.

Who am I?

I’m a journalist and a tinkerer. I’m fascinated not only by how things work but by how small levers can move mountains. Growing up in the workshop of my grandfather, an old Boston boatwright, I was mesmerized by the idea that a small rudder could maneuver a huge vessel. In college, I fell in love with how a small idea or expression could redirect a course of research or a country. As a self-taught maker of things, I appreciate how technologies empower us. I’ve chosen these books because they’re examples of how small ideas become things, lines of research, or patterns of thinking that shift human progress in unknowable ways.

Tim's book list on how technology is changing how we live

Tim Vandehey Why did Tim love this book?

I love gutsy books by outsiders, and Ms. Saxena, as a woman of color working in the Ivy League and the worlds of artificial intelligence and Big Data, is very much an outsider.

That makes her deep knowledge and insights into how AI and Big Data are changing business even that much more interesting. Plus, this is one of the only books I’ve read that explains how artificial intelligence works in a clear, direct way that doesn’t assume the reader already knows about things like machine learning and neural nets. 

By Asha Saxena,

Why should I read it?

1 author picked The AI Factor as one of their favorite books, and they share why you should read it.

What is this book about?

Have you heard about artificial intelligence (AI) and big data but felt they are technologies too big or too complicated for you or your business? Do you imagine AI as a Hollywood science fiction stereotype or something in the far and distant future?

Take heart. AI is none of those things. It's part of our everyday lives, and it has the power to transform your business.

This book will put AI, big data, the cloud, robotics, and smart devices in context. It will reveal how these technologies can dramatically multiply any businesses-including yours-by strategically using your data's latent, transformative potential.…


Book cover of Introduction to Machine Learning with Python: A Guide for Data Scientists

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

From my list on machine learning for beginners.

Who am I?

I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.  

Yuxi's book list on machine learning for beginners

Yuxi (Hayden) Liu Why did Yuxi love this book?

This book is more advanced than the first book I recommended. It presents ML theoretical and practical aspects step-by-step from the bottom up. Each chapter elaborates at length on a core building block in the ML life cycle. For example, feature engineering, supervised learning, and model evaluation have their own separate chapters, with intuitive discussions of how they work. Most of the concept is taught through the simple yet powerful Python Module Scikit-Learn so it won’t overburden you with heavy programming. This book will be perfect for practitioners with some understanding of statistics and linear algebra.

By Andreas C. Müller, Sarah Guido,

Why should I read it?

1 author picked Introduction to Machine Learning with Python as one of their favorite books, and they share why you should read it.

What is this book about?

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…


Book cover of Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

Peter J. Bentley Author Of Artificial Intelligence and Robotics: Ten Short Lessons

From my list on no hype and no nonsense artificial intelligence.

Who am I?

I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?

Peter's book list on no hype and no nonsense artificial intelligence

Peter J. Bentley Why did Peter love this book?

OK, I’m biased here because Rob is an old friend of mine. We first met at academic conferences and had several heated debates (arguments). But after spending a little time together at a workshop we realised each probably knew what they were talking about after all. Robert Elliott Smith, I should make clear it's not the Rob Smith who writes about “Artificial Superintelligence”. Those books definitely do not make this list.

Our Rob is a coherent, grounded scientist with bags of real-world experience, and he brings his knowledge to this title with gusto, telling us about how AI is affecting our lives in ways you never thought possible – and often not in a good way. If you want to understand what can go wrong with AI and what we should be doing to stop it, don’t read about singularities or other such nonsense, read this.

By Robert Elliott Smith,

Why should I read it?

1 author picked Rage Inside the Machine as one of their favorite books, and they share why you should read it.

What is this book about?

Shortlisted for the 2020 Business Book Awards

We live in a world increasingly ruled by technology; we seem as governed by technology as we do by laws and regulations. Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it.

But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era.

Having worked in the field…


Book cover of Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

From my list on machine learning for beginners.

Who am I?

I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.  

Yuxi's book list on machine learning for beginners

Yuxi (Hayden) Liu Why did Yuxi love this book?

Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.

By John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

Why should I read it?

1 author picked Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition as one of their favorite books, and they share why you should read it.

What is this book about?

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application…


5 book lists we think you will like!

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