100 books like People Skills for Analytical Thinkers

By Gilbert Eijkelenboom,

Here are 100 books that People Skills for Analytical Thinkers fans have personally recommended if you like People Skills for Analytical Thinkers. 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 Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed

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?

Not everybody needs to be a data scientist, but everybody does need to be data literate. Without an intentional focus on evangelism and building a strong data culture in your organization it will be an uphill battle to make meaningful change. This book helps individuals and leaders to understand what data literacy is, and how we can build it like any other skill.

By Jordan Morrow,

Why should I read it?

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

What is this book about?

In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With learnings to show…


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 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 Effective Pandas

Valliappa Lakshmanan Author Of Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning

From my list on if you want to become a data scientist.

Who am I?

I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.

Valliappa's book list on if you want to become a data scientist

Valliappa Lakshmanan Why did Valliappa love this book?

Even if you are ultimately going to be working with terabytes of data, you’ll start out doing exploratory data analysis. The tool that you’ll use for that is most likely going to be Pandas. One of the best investments that you can make when becoming a data scientist is to become a Pandas expert, and there is no better book than Harrison’s to help you get there. Plus, many of the interview questions you will face during the hiring process will probably involve Pandas. Blow your interviewers out of the water by showing them corners of the Pandas library they didn’t even know!

By Matt Harrison,

Why should I read it?

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

What is this book about?

Best practices for manipulating data with Pandas. This book will arm you with years of knowledge and experience that are condensed into an easy to follow format. Rather than taking months reading blogs and websites and searching mailing lists and groups, this book will teach you how to write good Pandas code.

It covers: Series manipulation Creating columns Summary statistics Grouping, pivoting, and cross-tabulation Time series data Visualization Chaining Debugging code and more...


Book cover of The Golem: What You Should Know about Science

Aubrey Clayton Author Of Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science

From my list on for data scientists trying to be ethical people.

Who am I?

I studied statistics and data science for years before anyone ever suggested to me that these topics might have an ethical dimension, or that my numerical tools were products of human beings with motivations specific to their time and place. I’ve since written about the history and philosophy of mathematical probability and statistics, and I’ve come to understand just how important that historical background is and how critically important it is that the next generation of data scientists understand where these ideas come from and their potential to do harm. I hope anyone who reads these books avoids getting blinkered by the ideas that data = objectivity and that science is morally neutral.

Aubrey's book list on for data scientists trying to be ethical people

Aubrey Clayton Why did Aubrey love this book?

The thing you should know about science is that it’s a human enterprise. As a result, it’s dependent on human factors like social consensus and prejudice. In this series of case studies of famously expensive and difficult-to-replicate experiments probing the limits of scientific understanding from biology to theoretical physics, Collins and Pinch show how scientific knowledge gathering is rarely straightforward because there are always alternative explanations available for the data. Was the phenomenon real or was the experiment set up badly? We can never know for sure, but we decide collectively what we believe. Scientists are experts participating in human culture, they argue, not mysterious clergy issuing declarations of absolute truth.

By Harry M. Collins, Trevor Pinch,

Why should I read it?

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

What is this book about?

Harry Collins and Trevor Pinch liken science to the Golem, a creature from Jewish mythology, powerful yet potentially dangerous, a gentle, helpful creature that may yet run amok at any moment. Through a series of intriguing case studies the authors debunk the traditional view that science is the straightforward result of competent theorisation, observation and experimentation. The very well-received first edition generated much debate, reflected in a substantial new Afterword in this second edition, which seeks to place the book in what have become known as 'the science wars'.


Book cover of Social Sciences as Sorcery

Aubrey Clayton Author Of Bernoulli's Fallacy: Statistical Illogic and the Crisis of Modern Science

From my list on for data scientists trying to be ethical people.

Who am I?

I studied statistics and data science for years before anyone ever suggested to me that these topics might have an ethical dimension, or that my numerical tools were products of human beings with motivations specific to their time and place. I’ve since written about the history and philosophy of mathematical probability and statistics, and I’ve come to understand just how important that historical background is and how critically important it is that the next generation of data scientists understand where these ideas come from and their potential to do harm. I hope anyone who reads these books avoids getting blinkered by the ideas that data = objectivity and that science is morally neutral.

Aubrey's book list on for data scientists trying to be ethical people

Aubrey Clayton Why did Aubrey love this book?

This book is now 50 years old, but its message is as relevant and important now as when it was written. In a series of witty essays that border on rants, Andreski attacks much of social science as fluff obscured by technical jargon and methodology. In particular, he laments the growth of quantitative methods as an attempt to add objectivity to social science and make it appear “harder.” True objectivity is about more than mechanical number-crunching, he says; it’s about a commitment to fairness and resisting the temptations of wishful thinking – a challenge anyone who works with data concerning people and their lives should take seriously.

By Stanislav Andreski,

Why should I read it?

1 author picked Social Sciences as Sorcery as one of their favorite books, and they share why you should read it.

What is this book about?

"Seldom have the social sciences been subject to quite so comprehensive, yet non-partisan, attack. There can be little doubt SOCIAL SCIENCES AS SORCERY is an uncomfortably important and embarassingly comprehensive book." -- Times Literary Supplement "Liberating!" -- Harpers "Andreski has written a new book that is certain to enrage his colleagues ... He documents his charges and spares few of the luminaries of social science in the process." -- TIME Magazine


Book cover of The Black Swan

Roger W. Hoerl Author Of Statistical Thinking: Improving Business Performance

From my list on AI and data science that are actually readable.

Who am I?

As a professional statistician, I am naturally interested in AI and data science. However, in our current information age, everyone, in all segments of society, needs to understand the basics of AI and data science. These basics include such things as what these disciplines are, what they can contribute to society, and perhaps most importantly, what can go wrong. However, I have found that much of the literature on these topics is highly technical and beyond the reach of most readers. These books are specifically selected because they are readable by virtually everyone, and yet convey the key concepts needed to be data-literate in the 21st century. Enjoy!

Roger's book list on AI and data science that are actually readable

Roger W. Hoerl Why did Roger love this book?

A “Black Swan” is a highly unlikely event that occurs with massive consequences. Think of 9/11 or the astonishing success of Google or Amazon.

The main issue relative to Black Swans, as explained by Talib, is that after the fact people are drawn to concocting detailed explanations that make them seem less random, and more predictable. In other words, people develop causal explanations that are completely wrong, but sound reasonable, and will then use them to predict the future.

In the words of Nate Silver, they invent a “signal” to explain what is in reality “noise.” These explanations also create a false sense of security about our ability to predict future events. In short, we fool ourselves into thinking that we know more than we actually do.

By Nassim Nicholas Taleb,

Why should I read it?

7 authors picked The Black Swan as one of their favorite books, and they share why you should read it.

What is this book about?

The most influential book of the past seventy-five years: a groundbreaking exploration of everything we know about what we don’t know, now with a new section called “On Robustness and Fragility.”

A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions…


Book cover of Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools

Naomi R. Ceder Author Of The Quick Python Book

From my list on to level up your Python skills.

Who am I?

I’ve been teaching and writing Python code (and managing others while they write Python code) for over 20 years. After all that time Python is still my tool of choice, and many times Python is the key part of how I explore and think about problems. My experience as a teacher also has prompted me to dig in and look for the simplest way of understanding and explaining the elegant way that Python features fit together. 

Naomi's book list on to level up your Python skills

Naomi R. Ceder Why did Naomi love this book?

I like this book not just because it’s a complete guide to the many ins and outs of data cleaning with Python, but also because David lays out the types of problems and the issues behind them. There are always trade-offs in data cleaning and this book lays out those trade-offs better than any other I’ve seen. This is one of the few books that as I go through it, I struggle to think of anything that could have been said better. 

By David Mertz,

Why should I read it?

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

What is this book about?

Think about your data intelligently and ask the right questions

Key Features Master data cleaning techniques necessary to perform real-world data science and machine learning tasks Spot common problems with dirty data and develop flexible solutions from first principles Test and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description

Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the…


Book cover of Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

Valliappa Lakshmanan Author Of Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning

From my list on if you want to become a data scientist.

Who am I?

I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.

Valliappa's book list on if you want to become a data scientist

Valliappa Lakshmanan Why did Valliappa love this book?

It is not enough for a data scientist to be able to analyze data and build ML models. You have to be able to communicate the insights to decision-makers concisely and accurately. This book shows you bad and good visualizations — you’ll be surprised by how often you would have defaulted to the bad way without the guidance provided by this book!

By Claus O. Wilke,

Why should I read it?

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

What is this book about?

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.

This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke…


5 book lists we think you will like!

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