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Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage
You've heard the hype around data—now get the facts.
In Becoming a Data How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.
You'll learn how
Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.
You've heard the hype around data—now get the facts.
In Becoming a Data How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.
You'll learn how
Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.
240 pages, Kindle Edition
Published April 13, 2021
302 people are currently reading
1375 people want to read
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Displaying 1 - 30 of 50 reviews
May 27, 2023
This book targets anyone needing to communicate through data, whether working with it directly or indirectly. It touches upon concepts within AI and statistics, and the commonly faced hurdles in this increasingly popular field. It’s a good place to start for anyone who want to learn about the possibilities and limitations of data in the real world.
It’s a digestible read for non-technical people, who would like to better understand and speak the “language” of data. The authors provide concrete examples for the real world, and do a good job demystifying some of the more math-heavy words like linear regression and p-values.
I like the fact that it moves between different levels; from explaining what different machine learning models can do to how factors like different roles, biases and types of projects can influence how we need to work with data.
As a data person, I’m a bit biased, as a lot of the topics touched in the book felt quite familiar, so explanations were at times simple rather than pure deep dives into the topics at hand. I acknowledge that this is not the scope of the book. It can be very useful even for anyone with a data background wishing to brush up on their knowledge or simply wanting to get inspiration on how to translate their work to their non-technical peers or to a business context.
I give it a 4-star rating, because the book does a great job providing a holistic understanding of the most important skills, opportunities and challenges present in the growing field of data. It’s a good introductory book for anyone striving to enhance their data literacy.
It’s a digestible read for non-technical people, who would like to better understand and speak the “language” of data. The authors provide concrete examples for the real world, and do a good job demystifying some of the more math-heavy words like linear regression and p-values.
I like the fact that it moves between different levels; from explaining what different machine learning models can do to how factors like different roles, biases and types of projects can influence how we need to work with data.
As a data person, I’m a bit biased, as a lot of the topics touched in the book felt quite familiar, so explanations were at times simple rather than pure deep dives into the topics at hand. I acknowledge that this is not the scope of the book. It can be very useful even for anyone with a data background wishing to brush up on their knowledge or simply wanting to get inspiration on how to translate their work to their non-technical peers or to a business context.
I give it a 4-star rating, because the book does a great job providing a holistic understanding of the most important skills, opportunities and challenges present in the growing field of data. It’s a good introductory book for anyone striving to enhance their data literacy.
November 16, 2022
An excellent introduction to data science, hats off!
The book doesn't focus on the technical details of machine learning algorithms; instead it focuses on how data science projects should be executed from start to end. Find your data, analyze it, clean it, approach it assumption-free, apply machine learning algorithms, make sure to use the right evaluation techniques.
I would recommend to anyone who is having trouble seeing the big picture in data science. Not recommended for people who are willing to do technical deep dives.
The book doesn't focus on the technical details of machine learning algorithms; instead it focuses on how data science projects should be executed from start to end. Find your data, analyze it, clean it, approach it assumption-free, apply machine learning algorithms, make sure to use the right evaluation techniques.
I would recommend to anyone who is having trouble seeing the big picture in data science. Not recommended for people who are willing to do technical deep dives.
October 9, 2024
Fantastic book which, as it was stated several times throughout its chapters, “scrapes the surface” while giving a good overall understanding of Data Science (DS), Machine Learning (ML), Probability and Statistics, Deep Learning, and Artificial Intelligence.
Despite not diving too much into complex technical details of DS/ML, the book can still serve as a great reference for getting acquainted with and leading data projects and pitfalls to watch out for from beginning to the end.
Highly recommended!
Despite not diving too much into complex technical details of DS/ML, the book can still serve as a great reference for getting acquainted with and leading data projects and pitfalls to watch out for from beginning to the end.
Highly recommended!
March 27, 2022
Very useful guide for anyone who is working with data science functions and has zero idea what their work entails. Less useful for experienced DS practitioners
February 3, 2023
If you are interested in data and want to understand a bit more how to use it, question it and decide when it’s the right time to apply it, this book is for you.
This is a great text for those in business interested to understand data more and those who want to deep dive in concepts like structured and unstructured data, machine learning and deep learning. It will give you a sense of that they do and explain logically how they work.
The authors hit the right spot in my opinion. It’s not so high level that only scratches the surface nor is it so nuanced and detailed that will make you lose interest if you are not a data analyst.
Verdict:If you are serious about understanding data more, this book is for you.
Watch out for: Some formulas that you don’t have to learn but are handy to understand what they are talking about.
Format: Paperback
Favourite quote: “Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful.”
This is a great text for those in business interested to understand data more and those who want to deep dive in concepts like structured and unstructured data, machine learning and deep learning. It will give you a sense of that they do and explain logically how they work.
The authors hit the right spot in my opinion. It’s not so high level that only scratches the surface nor is it so nuanced and detailed that will make you lose interest if you are not a data analyst.
Verdict:If you are serious about understanding data more, this book is for you.
Watch out for: Some formulas that you don’t have to learn but are handy to understand what they are talking about.
Format: Paperback
Favourite quote: “Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful.”
August 14, 2023
Four stars because they managed to explain concepts that my professors couldn’t be bothered to teach in my statistics module 😐. Overall I felt it is geared more towards people that have little understanding of data and provides a broader and less technical overview. In that sense I would recommend it to anyone who is interested in learning about statistics, data science and machine learning.
April 3, 2025
Really helpful as a supplement to my studies right now. Gave a lot of context to some theories I've been studying in a really digestible manner.
May 19, 2025
Everything you wish your non-technical colleagues understood about your work—if you're a data scientist. Complex concepts are explained clearly, with a well-defined target audience.
July 22, 2021
#місяцьпоганихкнижок не закінчується. Тут стільки води, що в мене тепер є запасний резервуар якщо припинять подачу води
August 18, 2021
Great book with an overview of the Data Science field. Good to read for those new to the field or recent graduates, but also great for those seasoned and experienced in the field to get reminders of important points for project planning and execution.
May 28, 2024
I've finished reading the book, and here are my summarized comments. It's an excellent book focused on explaining the fundamental aspects you need to be aware of regarding data if you aim to become a data head. It starts from the basics, firstly defining the problem you want to solve with data, explaining the types of data available, structured and unstructured. It briefly reviews descriptive statistics. It also dedicates a section to data sources and exploring them using techniques like simple linear regression, basic probability, Bayes' theorem, hypothesis testing, and then diving into supervised and unsupervised machine learning models. The overview of these models is just a skim on how they work and the most important aspects to know about each of them. The book delves a bit into text analysis, neural networks, and deep learning. I particularly liked how it closes with warnings about the main "pitfalls" we can fall into with data and models. Some examples include survivorship bias, regression to the mean, Simpson's paradox, confirmation bias, effort bias (aka the sunk cost fallacy), algorithmic bias, uncategorized bias. Finally, it ends with communication breakdowns which I find formidable: the postmortem, storytime, the telephone game, into the weeds, the reality check, the takeover, and the blowhard.
March 4, 2022
As someone with who has taken what felt like survey classes on many different data concepts, Becoming a DataHead was able to solidify my understanding on several topics that were introduced to me in school. I bought this book to brush up on the relatively unfamiliar world of data science, and upon completion I feel confident in my knowledge that I’ll be bringing to my role on the data team. The authors succeed in their goal of tailoring this book for anyone who wants to know more about data science, no matter if you’re a C-Suite professional who wants to know more about the cutting edge technologies his or her team is using to make decisions, or if you work with data everyday and want to know how to be a more effective team member. Throughout the book, the authors stress communication between team members, the business side, and the data side to ensure everyone’s expectations about data projects are in line. I anticipate keeping this book nearby as I continue to navigate down the path of becoming a data head.
February 27, 2022
A great book, providing advice and guidance for incorporating data based decisions into the business environment. The authors, data scientists Alex J. Gutman and Jordan Goldmeier, give an easy to understand and to-the-point explanation for all things data science. Their target audience are the managers and executives who want better use of data in their enterprises but are conscious of its pitfalls. Gutman and Goldmeier walk the reader through the whole analysis process. Topics covered include molding questions to available data, the importance of understanding variability, the various models data scientists use, and the interpretation of results. The authors give advice on what questions to ask and what guidance to give. The goal being a manager who can have a healthy two-way conversation with the data scientists working a project. A great book for those wanting to learn the terminology and methods of data science. Highly recommended for anyone who faces data-based decisions.
March 3, 2025
This book is about:
Argue with data: asking right questions.
What is the problem. Who will affect? What is DOD? Who collected data? Working on problems that matter
Challenger failed flight
Probability and Statistics methods
Probability calculation (assuming dependence)
Statistical moving to average
P-values vs significant level plus confidence intervals
Correlation is not causation
ML: Unsupervised and Supervised
Clustering and PCA component analysis
Creating composite features
K-means clustering
Linear regression
Classification and logistic regression
Decision trees
Random forests (trees in parallel) and Gradient boosting (step by step) as Ensemble methods
Test analysis: big bag of words, n-grams, word embeddings
Naive bayes, Sentiment analysis
Deep learning (neurons forming 2+)
Common pitfalls: multicollinears, leakage, wrong provement
Entrepreneuring progress and cynicism and heading
Argue with data: asking right questions.
What is the problem. Who will affect? What is DOD? Who collected data? Working on problems that matter
Challenger failed flight
Probability and Statistics methods
Probability calculation (assuming dependence)
Statistical moving to average
P-values vs significant level plus confidence intervals
Correlation is not causation
ML: Unsupervised and Supervised
Clustering and PCA component analysis
Creating composite features
K-means clustering
Linear regression
Classification and logistic regression
Decision trees
Random forests (trees in parallel) and Gradient boosting (step by step) as Ensemble methods
Test analysis: big bag of words, n-grams, word embeddings
Naive bayes, Sentiment analysis
Deep learning (neurons forming 2+)
Common pitfalls: multicollinears, leakage, wrong provement
Entrepreneuring progress and cynicism and heading
This entire review has been hidden because of spoilers.
November 11, 2024
It was a wonderful introduction to data science for any senior leader looking to build their vocabulary around the practice. The section on probabilistic thinking in particular was excellent. At some point every practitioner has had to act as a statistical apologist and struggled with explaining concepts like Baye’s Theorem in an approachable way. That section and the rest of the book is a treasure trove of allegory and anecdotes from clearly experienced authors.
The final section hit close to my heart with the observation that “the failures within each scene show a lack of empathy and respect.” Data alone won’t solve complex business problems problems - human insight is essential.
Great resource for anybody in a leadership position seeking to better understand data science and its application in decision-making.
The final section hit close to my heart with the observation that “the failures within each scene show a lack of empathy and respect.” Data alone won’t solve complex business problems problems - human insight is essential.
Great resource for anybody in a leadership position seeking to better understand data science and its application in decision-making.
October 30, 2023
The book is meant for business guys working with data scientists who wanna be able to communicate with them and have a basic understanding of what they are doing. So it's not a technical book although I (a data scientist) find it very helpful. Precisely the parts that aim to evaluate the business aspects of the data science projects starting from how to formulate the problem and goes all the way to communicating the final results. I also enjoyed the attempts to explain complicated topics in statistics, probability, and machine learning to non-technical readers. It offers a great intuition, and I think it will be quite helpful next time I try to communicate technical results to nonspecialists. Overall it's a great book and I highly recommend it
May 8, 2025
I really enjoyed this book. I had recently finished an Artificial Intelligence course in my graduate program and I have to say this book described many of the topics discussed in that course and more. Often times the discussions in this book were much better.
The book is about data but ultimately it has deep and broad coverage of all things around machine learning. Whether you want to say A.I. or deep learning or discuss neural networks it is all machine learning. This book breaks down many topics in the fields around data/big data/machine learning.
I highly recommend this book to anyone that wants a good understand of A.I/Machine Learning/Deep learning.
The book is about data but ultimately it has deep and broad coverage of all things around machine learning. Whether you want to say A.I. or deep learning or discuss neural networks it is all machine learning. This book breaks down many topics in the fields around data/big data/machine learning.
I highly recommend this book to anyone that wants a good understand of A.I/Machine Learning/Deep learning.
August 1, 2023
Essentially, Gutman is presenting an introductory statistics textbook for laymen. He does a nice job explaining concepts and the book is worth the read even for statistically sophisticated audiences.
The big gap for me with this presentation is that I don't think there is much original material here. Literally, it felt like a first-year college statstics class translated for laymen.
And I certainly didn't learn anything new about machine learning --- including that buzzword in the title is a bit misleading, in my opinion.
The big gap for me with this presentation is that I don't think there is much original material here. Literally, it felt like a first-year college statstics class translated for laymen.
And I certainly didn't learn anything new about machine learning --- including that buzzword in the title is a bit misleading, in my opinion.
November 16, 2024
I recently read this book for my Data Science course, and I thoroughly enjoyed its layout. One of the standout features is the chapter summaries at the end of each chapter, which effectively reinforce the key points. Additionally, the book excels at breaking down data analysis into simple, digestible tasks, making complex concepts, AI, machine learning, text analytics, deep learning and more to be accessible and easier to grasp.
January 2, 2022
I will definitely re-read this book. Statistics, data science and ML shown in a context of the way of thinking that every Data Head should have.
I'm part of finance team right now, so for the future development I truly believe that this is the way I should go. To become a Data Head.
Definitely to re-read.
I'm part of finance team right now, so for the future development I truly believe that this is the way I should go. To become a Data Head.
Definitely to re-read.
January 3, 2023
Amazing book for anyone who wants to be the head of data in their organization.
It gives deep foundations of each branch related to data, Machine Learning, Statistics, and Data Science, as well as the practical implementation of each algorithm.
I also learned about neural networks and their layers. I found it fascinating how they work together (GNN, GANN, DNN).
It gives deep foundations of each branch related to data, Machine Learning, Statistics, and Data Science, as well as the practical implementation of each algorithm.
I also learned about neural networks and their layers. I found it fascinating how they work together (GNN, GANN, DNN).
February 3, 2025
Amazing book for anyone who wants to be the head of data in their organization.
It gives deep foundations of each branch related to data, Machine Learning, Statistics, and Data Science, as well as the practical implementation of each algorithm.
I also learned about neural networks and their layers. I found it fascinating how they work together (GNN, GANN, DNN).
It gives deep foundations of each branch related to data, Machine Learning, Statistics, and Data Science, as well as the practical implementation of each algorithm.
I also learned about neural networks and their layers. I found it fascinating how they work together (GNN, GANN, DNN).
April 11, 2025
This book helped reinforce the statistical knowledge I already had while tying it into how I can bring it into the workplace to help strengthen decisions made with data. Truthfully a great introduction into working with data that is easy to follow for individuals with little to no expertise in the subject field.
May 7, 2025
Ideas from the book:
* Statistics is more about understanding the story the data is trying to tell, rather than just calculating numbers
* The power of data lies not just in its collection, but in its thoughtful analysis and interpretation.
* Data literacy isn't about doing data science; it's about understanding it well enough to ask the right questions and interpret the answers effectively.
* Statistics is more about understanding the story the data is trying to tell, rather than just calculating numbers
* The power of data lies not just in its collection, but in its thoughtful analysis and interpretation.
* Data literacy isn't about doing data science; it's about understanding it well enough to ask the right questions and interpret the answers effectively.
May 10, 2025
“If you mine the data hard enough, you can find messages from God.” - Dilbert
“The first principle is that you must not fool yourself, and you are the easiest person to fool.” - Feynman
“A Machine Learning algorithm walks into a bar. The bartender asks, ‘What’ll you have?’ The algorithm says, ‘What’s everyone else having?’” - Hasse
“The first principle is that you must not fool yourself, and you are the easiest person to fool.” - Feynman
“A Machine Learning algorithm walks into a bar. The bartender asks, ‘What’ll you have?’ The algorithm says, ‘What’s everyone else having?’” - Hasse
October 25, 2021
Excellent High-Level Journey
This book provides an understandable, concise review of the key topics, nuances, and challenges associated with either being in a data-centric role or in a role that consumes data analytics.
This book provides an understandable, concise review of the key topics, nuances, and challenges associated with either being in a data-centric role or in a role that consumes data analytics.
April 25, 2022
Amazing overview of ML algorithms and their use cases for those with no experience whatsoever. First part of the book is a wonderful review of ML-related statistical methods and principles of running data projects
June 1, 2024
Easy to digest, can be understood by non technical readers.
No code examples in the book.
The authors were able to explain complex topics (i.e. algorithms) in a digestible manner.
Great intro book for those looking to understand how data can be used. No prior knowledge needed.
No code examples in the book.
The authors were able to explain complex topics (i.e. algorithms) in a digestible manner.
Great intro book for those looking to understand how data can be used. No prior knowledge needed.
July 25, 2024
Not exactly what I was looking for. This was much more technical and into the weeds that I was expecting. So my rating is more of a reflection of what I will remember/use from this book, and not a reflection of its quality
December 8, 2024
Curious about how data is used to solve problems all around us? This book explains key data science concepts in a clear and easy-to-understand way. You'll also gain a good understanding of how machine learning and AI work, leaving you feeling more informed about the data-driven world we live in.
Displaying 1 - 30 of 50 reviews