Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
“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 End User License Agreement
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