Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

Length: 272 pages
Edition: 1
Language: English
Publisher: Wiley
Publication Date: 2021-05-11
ISBN-10: 1119741742
ISBN-13: 9781119741749
Sales Rank: #393101 (See Top 100 Books)

“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.

Cover
Table of Contents
Title Page
Copyright
Dedication
About the Authors
About the Technical Editors
Acknowledgments
Foreword
    NOTE
Introduction
    THE DATA SCIENCE INDUSTRIAL COMPLEX
    WHY WE CARE
    DATA IN THE WORKPLACE
    YOU CAN UNDERSTAND THE BIG PICTURE
    WHO THIS BOOK IS WRITTEN FOR
    WHY WE WROTE THIS BOOK
    WHAT YOU'LL LEARN
    HOW THIS BOOK IS ORGANIZED
    ONE LAST THING BEFORE WE BEGIN
    NOTES
PART I: Thinking Like a Data Head
    CHAPTER 1: What Is the Problem?
        QUESTIONS A DATA HEAD SHOULD ASK
        UNDERSTANDING WHY DATA PROJECTS FAIL
        WORKING ON PROBLEMS THAT MATTER
        CHAPTER SUMMARY
        NOTES
    CHAPTER 2: What Is Data?
        DATA VS. INFORMATION
        DATA TYPES
        HOW DATA IS COLLECTED AND STRUCTURED
        BASIC SUMMARY STATISTICS
        CHAPTER SUMMARY
        NOTES
    CHAPTER 3: Prepare to Think Statistically
        ASK QUESTIONS
        THERE IS VARIATION IN ALL THINGS
        PROBABILITIES AND STATISTICS
        CHAPTER SUMMARY
        NOTES
PART II: Speaking Like a Data Head
    CHAPTER 4: Argue with the Data
        WHAT WOULD YOU DO?
        TELL ME THE DATA ORIGIN STORY
        IS THE DATA REPRESENTATIVE?
        WHAT DATA AM I NOT SEEING?
        ARGUE WITH DATA OF ALL SIZES
        CHAPTER SUMMARY
        NOTES
    CHAPTER 5: Explore the Data
        EXPLORATORY DATA ANALYSIS AND YOU
        EMBRACING THE EXPLORATORY MINDSET
        CAN THE DATA ANSWER THE QUESTION?
        DID YOU DISCOVER ANY RELATIONSHIPS?
        DID YOU FIND NEW OPPORTUNITIES IN THE DATA?
        CHAPTER SUMMARY
        NOTES
    CHAPTER 6: Examine the Probabilities
        TAKE A GUESS
        THE RULES OF THE GAME
        PROBABILITY THOUGHT EXERCISE
        BE CAREFUL ASSUMING INDEPENDENCE
        ALL PROBABILITIES ARE CONDITIONAL
        ENSURE THE PROBABILITIES HAVE MEANING
        CHAPTER SUMMARY
        NOTES
    CHAPTER 7: Challenge the Statistics
        QUICK LESSONS ON INFERENCE
        THE PROCESS OF STATISTICAL INFERENCE
        THE QUESTIONS YOU SHOULD ASK TO CHALLENGE THE STATISTICS
        CHAPTER SUMMARY
        NOTES
PART III: Understanding the Data Scientist's Toolbox
    CHAPTER 8: Search for Hidden Groups
        UNSUPERVISED LEARNING
        DIMENSIONALITY REDUCTION
        PRINCIPAL COMPONENT ANALYSIS
        CLUSTERING
        K-MEANS CLUSTERING
        CHAPTER SUMMARY
        NOTES
    CHAPTER 9: Understand the Regression Model
        SUPERVISED LEARNING
        LINEAR REGRESSION: WHAT IT DOES
        LINEAR REGRESSION: WHAT IT GIVES YOU
        LINEAR REGRESSION: WHAT CONFUSION IT CAUSES
        OTHER REGRESSION MODELS
        CHAPTER SUMMARY
        NOTES
    CHAPTER 10: Understand the Classification Model
        INTRODUCTION TO CLASSIFICATION
        LOGISTIC REGRESSION
        DECISION TREES
        ENSEMBLE METHODS
        WATCH OUT FOR PITFALLS
        MISUNDERSTANDING ACCURACY
        CHAPTER SUMMARY
        NOTES
    CHAPTER 11: Understand Text Analytics
        EXPECTATIONS OF TEXT ANALYTICS
        HOW TEXT BECOMES NUMBERS
        TOPIC MODELING
        TEXT CLASSIFICATION
        PRACTICAL CONSIDERATIONS WHEN WORKING WITH TEXT
        CHAPTER SUMMARY
        NOTES
    CHAPTER 12: Conceptualize Deep Learning
        NEURAL NETWORKS
        APPLICATIONS OF DEEP LEARNING
        DEEP LEARNING IN PRACTICE
        ARTIFICIAL INTELLIGENCE AND YOU
        CHAPTER SUMMARY
        NOTES
PART IV: Ensuring Success
    CHAPTER 13: Watch Out for Pitfalls
        BIASES AND WEIRD PHENOMENA IN DATA
        THE BIG LIST OF PITFALLS
        CHAPTER SUMMARY
        NOTES
    CHAPTER 14: Know the People and Personalities
        SEVEN SCENES OF COMMUNICATION BREAKDOWNS
        DATA PERSONALITIES
        CHAPTER SUMMARY
        NOTES
    CHAPTER 15: What's Next?
Index
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