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"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.

Table of Contents

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