Becoming a Data Head - How to Think, Speak, Understand Data Science, Statistics, Machine

"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.

Inhaltsverzeichnis

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

Über den Autor / die Autorin

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.

Zusammenfassung

"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.

Bericht

"Big Data, Data Science, Machine Learning, Artificial Intelligence, Neural Networks, Deep Learning...It can be buzzword bingo, but make no mistake, everything is becoming "datafied" and an understanding of data problems and the data science toolset is becoming a requirement for every business person. Alex and Jordan have put together a must read whether you are just starting your journey or already in the thick of it. They made this complex space simple by breaking down the 'data process' into understandable patterns and using everyday examples and events over our history to make the concepts relatable."
- Milen Mahadevan, President of 84.51°
 
"What I love about this book is its remarkable breadth of topics covered, while maintaining a healthy depth in the content presented for each topic. I believe in the pedagogical concept of 'Talking the Walk,' which means being able to explain the hard stuff in terms that broad audiences can grasp. Too many data science books are either too specialized in taking you down the deep paths of mathematics and coding ('Walking the Walk') or too shallow in over-hyping the content with a plethora of shallow buzzwords ('Talking the Talk'). You can take a great walk down the pathways of the data field in Alex and Jordan's without fear of falling off the path. The journey and destination are well worth the trip, and the talk."
- Kirk Borne, Data Scientist and Top Worldwide Influencer in Data Science
 
"The most clear, concise, and practical characterization of working in corporate analytics that I've seen. If you want to be a killer analyst and ask the right questions, this is for you."
- Kristen Kehrer, Data Moves Me, LLC and LinkedIn Top Voices in Data Science & Analytics
 
"THE book that business and technology leaders need to read to fully understand the potential, power, AND limitations of data science."
- Jennifer L. L. Morgan, PhD, Analytical Chemist at Procter and Gamble
 
"You've heard it before: 'We need to be doing more machine learning. Why aren't we doing more sophisticated data science work?' Data science isn't the magic unicorn that will solve all of your company's problems. Becoming a Data Head brings this idea to life by highlighting when data science is (and isn't) the right approach and the common pitfalls to watch out for, explaining it all in a way that a data novice can understand. This book will be my new 'pocket reference' when communicating complicated concepts to non-technically trained leaders."
- Sandy Steiger, Director, Center for Analytics and Data Science at Miami University
 
"Individuals and organizations want to be data driven. They say they are data driven. Becoming a Data Head shows them how to actually become data driven, without the assumption of a statistics or data background. This book is for anyone, or any organization, asking how to bring a data mindset to the whole company, not just those trained in the space."
- Eric Weber, Head of Experimentation & Metrics Research, Yelp
 
"What is keeping data science from reaching its true potential? It is not slow algorithms, lack of data, lack of computing power, or even lack of data scientists. Becoming a Data Head tackles the biggest impediment to data science success, the communication gap between the data scientist and the executive. Gutman and Goldmeier provide creative explanations of data science techniques and how they are used with clear everyday relatable examples. Managers and executives, and anyone wanting to better understand data science will learn a lot from this book. Likewise, data scientists who find it challenging to explain what they are doing will also find great value in Becoming a Data Head."
- Jeffrey D. Camm, PhD, Center for Analytics Impact, Wake Forest University
 
"Becoming a Data Head raises the level of education and knowledge in an industry desperate for clarity in thinking. A must read for those working with and within the growing field of data science and analytics."
- Dr. Stephen Chambal, VP for Corporate Growth at Perduco (DoD Analytics Company)
 
"Gutman and Goldmeier filter through much of the noise to break down complex data and statistical concepts we hear today into basic examples and analogies that stick. Becoming a Data Head has enabled me to translate my team's data needs into more tangible business requirements that make sense for our organization. A great read if you want to communicate your data more effectively to drive your business and data science team forward!"
- Justin Maurer, Engineering and Data Science Manager at Google
 
"As an aerospace engineer with nearly 15 years experience, Becoming a Data Head made me aware of not only what I personally want to learn about data science, but also what I need to know professionally to operate in a data-rich environment. This book further discusses how to filter through often overused terms like artificial intelligence. This is a book for every mid-level program manager learning how to navigate the inevitable future of data science."
- Josh Keener, Aerospace Engineer and Program Manager
 
"A must read for an in-depth understanding of data science for senior executives."
- Cade Saie, Chief Data Officer
 
"Gutman and Goldmeier offer practical advice for asking the right questions, challenging assumptions, and avoiding common pitfalls. They strike a nice balance between thoroughly explaining concepts of data science while not getting lost in the weeds. This book is a useful addition to the toolbox of any analyst, data scientist, manager, executive, or anyone else who wants to become more comfortable with data science."
- Jeff Bialac, Senior Supply Chain Analyst at Kroger
 
"Gutman and Goldmeier have written a book that is as useful for applied statisticians and data scientists as it is for business leaders and technical professionals. In demystifying these complex statistical topics, they have also created a common language that bridges the longstanding communication divide that has -- until now -- separated data work from business value."
- Kathleen Maley, Chief Analytics Officer at datazuum

Produktdetails

• Autoren: Jordan Goldmeier, Goldmeier Jordan, Aj Gutman, Alex Gutman, Alex J Gutman, Alex J. Gutman, Alex J. Goldmeier Gutman
• Verlag: Wiley, John and Sons Ltd
• Sprache: Englisch
• Produktform: Taschenbuch
• Erschienen: 30.06.2021
• EAN: 9781119741749
• ISBN: 978-1-119-74174-9
• Seiten: 272
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Anwendungs-Software; Informatik, Daten, Maschinelles Lernen, computer science, Data Mining & Knowledge Discovery, Data Mining u. Knowledge Discovery