In Becoming a Data Head: 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.

Becoming a Data Head

Preface

Many companies rush to try the “next big thing” in data without ever pausing to ask the right business questions. Or learn basic data terminology. Or learn how to look at the world through a statistical lens.

What Is the Problem?

“If we have data, let's look at data. If all we have are opinions, let's go with mine.” —Jim Barksdale, former Netscape CEO 

What Is Data?

“Statistical thinking is a different way of thinking that is part detective, skeptical, and involves alternate takes on a problem.” 1 —Frank Harrell, statistician and professor 

Prepare to Think Statistically

Chapters 4-7, continues the previous chapter’s charge to think statistically and question everything. In fact, chapters 4-7 gives you questions to ask and things to think about whether you're viewing someone else's data project or doing the work yourself. Many of the forthcoming section headers will be named for the very same questions you'll need to ask. Consider it your reference guide for asking tough questions.

Argue with the Data

If you tell a data scientist to go on a fishing expedition … then you deserve what you get, which is a bad analysis. 1 Thomas C. Redman, “the Data Doc” and Harvard Business Review contributor 

Explore the Data

Many people's notion of probability is so impoverished that it admits [one] of only two values: 50-50 and 99%, tossup or essentially certain.” —John Allen Paulos, mathematician and author of “Innumeracy: Mathematical Illiteracy and Its Consequences” 1 

Examine the Probabilities

Kent Brockman: Mr. Simpson, how do you respond to the charge that petty vandalism such as graffiti is down 80% while heavy sack beatings are up a shocking 900%? Homer: Oh, people can come up with statistics to prove anything, Kent. Forty percent of all people know that. —The Simpsons 

Challenge the Statistics

You know the terms: machine learning, artificial intelligence, and deep learning. They're probably what led you to this book. But now we're going to demystify them.

Search for Hidden Groups

“Regression analysis is like one of those fancy power tools. It is relatively easy to use, but hard to use well—and potentially dangerous when used improperly.” Charles Wheelan in Naked Statistics 1 

Understand the Regression Model

A Machine Learning algorithm walks into a bar. The bartender asks, “What'll you have?” The algorithm says, “What's everyone else having?” Chet Hasse (@chethaase) 

Understand the Classification Model

“Seek success but prepare for vegetables.” InspireBot™, an AI bot “dedicated to generating unlimited amounts of unique inspirational quotes.” 1 

Understand Text Analytics

“AI is sometimes heralded as the new industrial revolution. If deep learning is the steam engine of this revolution, then data is its coal: the raw material that powers our intelligent machines, without which nothing would be possible.” —François Chollet, AI researcher and author 1 

Conceptualize Deep Learning

In chapters 13-15, you'll discover how to make the most out of your Data Head journey by learning from others’ mistakes, both technical and human.

Watch Out for Pitfalls

“People worry that computers will get too smart and take over the world, but the real problem is that they're too stupid and they've already taken over the world.” 1 —Pedro Domingos, AI Researcher 

Know the People and Personalities

“One learns from books and example only that certain things can be done. Actual learning requires that you do those things.” —Frank Herbert, American author 

What's Next?

A A/B tests/experiments, 17 absolute value, 121 accuracy, misunderstanding, 147–150 adapting questions, 201 affordability metric, 179 AGI (artificial general intelligence), 187 AI (artificial intelligence) about, 187–188 big tech and, 188–189 algorithmic bias, 198 alternative hypothesis, 85–86 Amazon, 168 Ames Housing Data, 53 ANI (artificial narrow intelligence), 187 Apple, 168 applications of deep learning, 178–184 of supervised learning, 119 arguing, with data about, 37–38 missing data, 47–48 origin of data, 43–45 representativeness of data, 45–47 size of data, 48–49 "what would you do" scenario, 38–43 artificial general intelligence (AGI), 187 artificial intelligence (AI) about, 187–188 big tech and, 188–189 artificial narrow intelligence (ANI), 187 asking questions, 22–23 assuming causality, 96–97 equivalence, 93 assumptions, underlying in statistics, 32 attributes. See columns audience, knowing your, 14 average, 18 

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