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

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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.  Cover Title Page Copyright Page About the Authors About the Technical Editors Acknowledgments Contents 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 Part I Thinking Like a Data Head Chapter 1 What Is the Problem? Questions a Data Head Should Ask Why Is This Problem Important? Who Does This Problem Affect? What If We Don’t Have the Right Data? When Is the Project Over? What If We Don’t Like the Results? Understanding Why Data Projects Fail Customer Perception Discussion Working on Problems That Matter Chapter Summary Chapter 2 What Is Data? Data vs. Information An Example Dataset Data Types How Data Is Collected and Structured Observational vs. Experimental Data Structured vs. Unstructured Data Basic Summary Statistics Chapter Summary Chapter 3 Prepare to Think Statistically Ask Questions There Is Variation in All Things Scenario: Customer Perception (The Sequel) Case Study: Kidney-Cancer Rates Probabilities and Statistics Probability vs. Intuition Discovery with Statistics Chapter Summary Part II Speaking Like a Data Head Chapter 4 Argue with the Data What Would You Do? Missing Data Disaster Tell Me the Data Origin Story Who Collected the Data? How Was the Data Collected? Is the Data Representative? Is There Sampling Bias? What Did You Do with Outliers? What Data Am I Not Seeing? How Did You Deal with Missing Values? Can the Data Measure What You Want It to Measure? Argue with Data of All Sizes Chapter Summary Chapter 5 Explore the Data Exploratory Data Analysis and You Embracing the Exploratory Mindset Questions to Guide You The Setup Can the Data Answer the Question? Set Expectations and Use Common Sense Do the Values Make Intuitive Sense? Watch Out: Outliers and Missing Values Did You Discover Any Relationships? Understanding Correlation Watch Out: Misinterpreting Correlation Watch Out: Correlation Does Not Imply Causation Did You Find New Opportunities in the Data? Chapter Summary Chapter 6 Examine the Probabilities Take a Guess The Rules of the Game Notation Conditional Probability and Independent Events The Probability of Multiple Events Probability Thought Exercise Next Steps Be Careful Assuming Independence Don’t Fall for the Gambler’s Fallacy All Probabilities Are Conditional Don’t Swap Dependencies Bayes’ Theorem Ensure the Probabilities Have Meaning Calibration Rare Events Can, and Do, Happen Chapter Summary Chapter 7 Challenge the Statistics Quick Lessons on Inference Give Yourself Some Wiggle Room More Data, More Evidence Challenge the Status Quo Evidence to the Contrary Balance Decision Errors The Process of Statistical Inference The Questions You Should Ask to Challenge the Statistics What Is the Context for These Statistics? What Is the Sample Size? What Are You Testing? What Is the Null Hypothesis? What Is the Significance Level? How Many Tests Are You Doing? Can I See the Confidence Intervals? Is This Practically Significant? Are You Assuming Causality? Chapter Summary Part III Understanding the Data Scientist’s Toolbox Chapter 8 Search for Hidden Groups Unsupervised Learning Dimensionality Reduction Creating Composite Features Principal Component Analysis Principal Components in Athletic Ability PCA Summary Potential Traps Clustering k-Means Clustering Clustering Retail Locations Potential Traps Chapter Summary Chapter 9 Understand the Regression Model Supervised Learning Linear Regression: What It Does Least Squares Regression: Not Just a Clever Name Linear Regression: What It Gives You Extending to Many Features Linear Regression: What Confusion It Causes Omitted Variables Multicollinearity Data Leakage Extrapolation Failures Many Relationships Aren’t Linear Are You Explaining or Predicting? Regression Performance Other Regression Models Chapter Summary Chapter 10 Understand the Classification Model Introduction to Classification What You’ll Learn Classification Problem Setup Logistic Regression Logistic Regression: So What? Decision Trees Ensemble Methods Random Forests Gradient Boosted Trees Interpretability of Ensemble Models Watch Out for Pitfalls Misapplication of the Problem Data Leakage Not Splitting Your Data Choosing the Right Decision Threshold Misunderstanding Accuracy Confusion Matrices Chapter Summary Chapter 11 Understand Text Analytics Expectations of Text Analytics How Text Becomes Numbers A Big Bag of Words N-Grams Word Embeddings Topic Modeling Text Classification Naïve Bayes Sentiment Analysis Practical Considerations When Working with Text Big Tech Has the Upper Hand Chapter Summary Chapter 12 Conceptualize Deep Learning Neural Networks How Are Neural Networks Like the Brain? A Simple Neural Network How a Neural Network Learns A Slightly More Complex Neural Network Applications of Deep Learning The Benefits of Deep Learning How Computers “See” Images Convolutional Neural Networks Deep Learning on Language and Sequences Deep Learning in Practice Do You Have Data? Is Your Data Structured? What Will the Network Look Like? Artificial Intelligence and You Big Tech Has the Upper Hand Ethics in Deep Learning Chapter Summary Part IV Ensuring Success Chapter 13 Watch Out for Pitfalls Biases and Weird Phenomena in Data Survivorship Bias Regression to the Mean Simpson’s Paradox Confirmation Bias Effort Bias (aka the “Sunk Cost Fallacy”) Algorithmic Bias Uncategorized Bias The Big List of Pitfalls Statistical and Machine Learning Pitfalls Project Pitfalls Chapter Summary Chapter 14 Know the People and Personalities Seven Scenes of Communication Breakdowns The Postmortem Storytime The Telephone Game Into the Weeds The Reality Check The Takeover The Blowhard Data Personalities Data Enthusiasts Data Cynics Data Heads Chapter Summary Chapter 15 What’s Next? Index EULA