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Becoming a Data Head
How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
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ISBN-13:
9781119741749
Veröffentl:
2021
Erscheinungsdatum:
24.06.2021
Seiten:
272
Autor:
Alex J. Gutman
Gewicht:
368 g
Format:
224x151x14 mm
Sprache:
Deutsch
Beschreibung:
ALEX J. GUTMAN, PhD, is a Data Scientist, Corporate Trainer, and Accredited Professional Statistician. His professional focus is on statistical and machine learning and he has extensive experience working as a Data Scientist for the Department of Defense and two Fortune 50 companies.
JORDAN GOLDMEIER is a Data Scientist, author, speaker, and community leader. He is a seven-time recipient of the Microsoft Most Valuable Professional Award and he has taught analytics to members of the Pentagon and Fortune 500 companies.
JORDAN GOLDMEIER is a Data Scientist, author, speaker, and community leader. He is a seven-time recipient of the Microsoft Most Valuable Professional Award and he has taught analytics to members of the Pentagon and Fortune 500 companies.
Acknowledgments xiii
Foreword xxiii
Introduction xxvii
Part One Thinking Like a Data Head
Chapter 1 What Is the Problem? 3
Questions a Data Head Should Ask 4
Why Is This Problem Important? 4
Who Does This Problem Affect? 6
What If We Don't Have the Right Data? 6
When Is the Project Over? 7
What If We Don't Like the Results? 7
Understanding Why Data Projects Fail 8
Customer Perception 8
Discussion 10
Working on Problems That Matter 11
Chapter Summary 11
Chapter 2 What Is Data? 13
Data vs. Information 13
An Example Dataset 14
Data Types 15
How Data Is Collected and Structured 16
Observational vs. Experimental Data 16
Structured vs. Unstructured Data 17
Basic Summary Statistics 18
Chapter Summary 19
Chapter 3 Prepare to Think Statistically 21
Ask Questions 22
There Is Variation in All Things 23
Scenario: Customer Perception (The Sequel) 24
Case Study: Kidney-Cancer Rates 26
Probabilities and Statistics 28
Probability vs. Intuition 29
Discovery with Statistics 31
Chapter Summary 33
Part Two Speaking Like a Data Head
Chapter 4 Argue with the Data 37
What Would You Do? 38
Missing Data Disaster 39
Tell Me the Data Origin Story 43
Who Collected the Data? 44
How Was the Data Collected? 44
Is the Data Representative? 45
Is There Sampling Bias? 46
What Did You Do with Outliers? 46
What Data Am I Not Seeing? 47
How Did You Deal with Missing Values? 47
Can the Data Measure What You Want It to Measure? 48
Argue with Data of All Sizes 48
Chapter Summary 49
Chapter 5 Explore the Data 51
Exploratory Data Analysis and You 52
Embracing the Exploratory Mindset 52
Questions to Guide You 53
The Setup 53
Can the Data Answer the Question? 54
Set Expectations and Use Common Sense 54
Do the Values Make Intuitive Sense? 54
Watch Out: Outliers and Missing Values 58
Did You Discover Any Relationships? 59
Understanding Correlation 59
Watch Out: Misinterpreting Correlation 60
Watch Out: Correlation Does Not Imply Causation 62
Did You Find New Opportunities in the Data? 63
Chapter Summary 63
Chapter 6 Examine the Probabilities 65
Take a Guess 66
The Rules of the Game 66
Notation 67
Conditional Probability and Independent Events 69
The Probability of Multiple Events 69
Two Things That Happen Together 69
One Thing or the Other 70
Probability Thought Exercise 72
Next Steps 73
Be Careful Assuming Independence 74
Don't Fall for the Gambler's Fallacy 74
All Probabilities Are Conditional 75
Don't Swap Dependencies 76
Bayes' Theorem 76
Ensure the Probabilities Have Meaning 79
Calibration 80
Rare Events Can, and Do, Happen 80
Chapter Summary 81
Chapter 7 Challenge the Statistics 83
Quick Lessons on Inference 83
Give Yourself Some Wiggle Room 84
More Data, More Evidence 84
Challenge the Status Quo 85
Evidence to the Contrary 86
Balance Decision Errors 88
The Process of Statistical Inference 89
The Questions
Foreword xxiii
Introduction xxvii
Part One Thinking Like a Data Head
Chapter 1 What Is the Problem? 3
Questions a Data Head Should Ask 4
Why Is This Problem Important? 4
Who Does This Problem Affect? 6
What If We Don't Have the Right Data? 6
When Is the Project Over? 7
What If We Don't Like the Results? 7
Understanding Why Data Projects Fail 8
Customer Perception 8
Discussion 10
Working on Problems That Matter 11
Chapter Summary 11
Chapter 2 What Is Data? 13
Data vs. Information 13
An Example Dataset 14
Data Types 15
How Data Is Collected and Structured 16
Observational vs. Experimental Data 16
Structured vs. Unstructured Data 17
Basic Summary Statistics 18
Chapter Summary 19
Chapter 3 Prepare to Think Statistically 21
Ask Questions 22
There Is Variation in All Things 23
Scenario: Customer Perception (The Sequel) 24
Case Study: Kidney-Cancer Rates 26
Probabilities and Statistics 28
Probability vs. Intuition 29
Discovery with Statistics 31
Chapter Summary 33
Part Two Speaking Like a Data Head
Chapter 4 Argue with the Data 37
What Would You Do? 38
Missing Data Disaster 39
Tell Me the Data Origin Story 43
Who Collected the Data? 44
How Was the Data Collected? 44
Is the Data Representative? 45
Is There Sampling Bias? 46
What Did You Do with Outliers? 46
What Data Am I Not Seeing? 47
How Did You Deal with Missing Values? 47
Can the Data Measure What You Want It to Measure? 48
Argue with Data of All Sizes 48
Chapter Summary 49
Chapter 5 Explore the Data 51
Exploratory Data Analysis and You 52
Embracing the Exploratory Mindset 52
Questions to Guide You 53
The Setup 53
Can the Data Answer the Question? 54
Set Expectations and Use Common Sense 54
Do the Values Make Intuitive Sense? 54
Watch Out: Outliers and Missing Values 58
Did You Discover Any Relationships? 59
Understanding Correlation 59
Watch Out: Misinterpreting Correlation 60
Watch Out: Correlation Does Not Imply Causation 62
Did You Find New Opportunities in the Data? 63
Chapter Summary 63
Chapter 6 Examine the Probabilities 65
Take a Guess 66
The Rules of the Game 66
Notation 67
Conditional Probability and Independent Events 69
The Probability of Multiple Events 69
Two Things That Happen Together 69
One Thing or the Other 70
Probability Thought Exercise 72
Next Steps 73
Be Careful Assuming Independence 74
Don't Fall for the Gambler's Fallacy 74
All Probabilities Are Conditional 75
Don't Swap Dependencies 76
Bayes' Theorem 76
Ensure the Probabilities Have Meaning 79
Calibration 80
Rare Events Can, and Do, Happen 80
Chapter Summary 81
Chapter 7 Challenge the Statistics 83
Quick Lessons on Inference 83
Give Yourself Some Wiggle Room 84
More Data, More Evidence 84
Challenge the Status Quo 85
Evidence to the Contrary 86
Balance Decision Errors 88
The Process of Statistical Inference 89
The Questions
"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 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.
You'll learn how to:
* 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 workplace: 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.
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 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.
You'll learn how to:
* 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 workplace: 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.