Doing Data Science. Straight Talk from the Frontline

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Spis treści książki

• Doing Data Science
• Dedication
• Preface
  • Motivation
  • Origins of the Class
  • Origins of the Book
  • What to Expect from This Book
  • How This Book Is Organized
  • How to Read This Book
  • How Code Is Used in This Book
  • Who This Book Is For
  • Prerequisites
  • Supplemental Reading
  • About the Contributors
  • Conventions Used in This Book
  • Using Code Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
• 1. Introduction: What Is Data Science?
  • Big Data and Data Science Hype
  • Getting Past the Hype
  • Why Now?
    • Datafication
  • The Current Landscape (with a Little History)
    • Data Science Jobs
  • A Data Science Profile
  • Thought Experiment: Meta-Definition
  • OK, So What Is a Data Scientist, Really?
    • In Academia
    • In Industry
• 2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process
  • Statistical Thinking in the Age of Big Data
    • Statistical Inference
    • Populations and Samples
    • Populations and Samples of Big Data
    • Big Data Can Mean Big Assumptions
      • Can N=ALL?
      • Data is not objective
    • Modeling
      • What is a model?
      • Statistical modeling
      • But how do you build a model?
      • Probability distributions
      • Fitting a model
      • Overfitting
  • Exploratory Data Analysis
    • Philosophy of Exploratory Data Analysis
    • Exercise: EDA
      • Sample code
  • The Data Science Process
    • A Data Scientists Role in This Process
  • Thought Experiment: How Would You Simulate Chaos?
  • Case Study: RealDirect
    • How Does RealDirect Make Money?
    • Exercise: RealDirect Data Strategy
      • Sample R code
• 3. Algorithms
  • Machine Learning Algorithms
  • Three Basic Algorithms
    • Linear Regression
      • Start by writing something down
      • Fitting the model
      • Extending beyond least squares
        • Adding in modeling assumptions about the errors
        • Adding other predictors
        • Transformations
      • Review
      • Exercise
    • k-Nearest Neighbors (k-NN)
      • Example with credit scores
      • Similarity or distance metrics
      • Training and test sets
      • Pick an evaluation metric
      • Putting it all together
      • Choosing k
      • What are the modeling assumptions?
    • k-means
      • 2D version
  • Exercise: Basic Machine Learning Algorithms
    • Solutions
      • Sample R code: Linear regression on the housing dataset
      • Sample R code: K-NN on the housing dataset
  • Summing It All Up
  • Thought Experiment: Automated Statistician
• 4. Spam Filters, Naive Bayes, and Wrangling
  • Thought Experiment: Learning by Example
    • Why Wont Linear Regression Work for Filtering Spam?
    • How About k-nearest Neighbors?
  • Naive Bayes
    • Bayes Law
    • A Spam Filter for Individual Words
    • A Spam Filter That Combines Words: Naive Bayes
  • Fancy It Up: Laplace Smoothing
  • Comparing Naive Bayes to k-NN
  • Sample Code in bash
  • Scraping the Web: APIs and Other Tools
  • Jakes Exercise: Naive Bayes for Article Classification
    • Sample R Code for Dealing with the NYT API
• 5. Logistic Regression
  • Thought Experiments
  • Classifiers
    • Runtime
    • You
    • Interpretability
    • Scalability
  • M6D Logistic Regression Case Study
    • Click Models
    • The Underlying Math
    • Estimating and
    • Newtons Method
    • Stochastic Gradient Descent
    • Implementation
    • Evaluation
  • Media 6 Degrees Exercise
    • Sample R Code
• 6. Time Stamps and Financial Modeling
  • Kyle Teague and GetGlue
  • Timestamps
    • Exploratory Data Analysis (EDA)
    • Metrics and New Variables or Features
    • Whats Next?
  • Cathy ONeil
  • Thought Experiment
  • Financial Modeling
    • In-Sample, Out-of-Sample, and Causality
    • Preparing Financial Data
    • Log Returns
    • Example: The S&P Index
    • Working out a Volatility Measurement
    • Exponential Downweighting
    • The Financial Modeling Feedback Loop
    • Why Regression?
    • Adding Priors
    • A Baby Model
    • Exercise: GetGlue and Timestamped Event Data
    • Exercise: Financial Data
• 7. Extracting Meaning from Data
  • William Cukierski
    • Background: Data Science Competitions
    • Background: Crowdsourcing
  • The Kaggle Model
    • A Single Contestant
    • Their Customers
  • Thought Experiment: What Are the Ethical Implications of a Robo-Grader?
  • Feature Selection
    • Example: User Retention
    • Filters
    • Wrappers
      • Selecting an algorithm
      • Selection criterion
      • In practice
    • Embedded Methods: Decision Trees
    • Entropy
    • The Decision Tree Algorithm
    • Handling Continuous Variables in Decision Trees
    • Random Forests
    • User Retention: Interpretability Versus Predictive Power
  • David Huffaker: Googles Hybrid Approach to Social Research
    • Moving from Descriptive to Predictive
    • Social at Google
    • Privacy
    • Thought Experiment: What Is the Best Way to Decrease Concern and Increase Understanding and Control?
• 8. Recommendation Engines: Building a User-Facing Data Product at Scale
  • A Real-World Recommendation Engine
    • Nearest Neighbor Algorithm Review
    • Some Problems with Nearest Neighbors
    • Beyond Nearest Neighbor: Machine Learning Classification
    • The Dimensionality Problem
    • Singular Value Decomposition (SVD)
    • Important Properties of SVD
    • Principal Component Analysis (PCA)
      • Theorem: The resulting latent features will be uncorrelated
    • Alternating Least Squares
      • Theorem with no proof: The preceding algorithm will converge if your prior is large enough
    • Fix V and Update U
    • Last Thoughts on These Algorithms
  • Thought Experiment: Filter Bubbles
  • Exercise: Build Your Own Recommendation System
    • Sample Code in Python
• 9. Data Visualization and Fraud Detection
  • Data Visualization History
    • Gabriel Tarde
    • Marks Thought Experiment
  • What Is Data Science, Redux?
    • Processing
    • Franco Moretti
  • A Sample of Data Visualization Projects
  • Marks Data Visualization Projects
    • New York Times Lobby: Moveable Type
    • Project Cascade: Lives on a Screen
    • Cronkite Plaza
    • eBay Transactions and Books
    • Public Theater Shakespeare Machine
    • Goals of These Exhibits
  • Data Science and Risk
    • About Square
    • The Risk Challenge
      • Detecting suspicious activity using machine learning
    • The Trouble with Performance Estimation
      • Defining the error metric
      • Defining the labels
      • Challenges in features and learning
    • Model Building Tips
      • Code readability and reusability
      • Get a pair!
      • Productionizing machine learning models
  • Data Visualization at Square
  • Ians Thought Experiment
  • Data Visualization for the Rest of Us
    • Data Visualization Exercise
• 10. Social Networks and Data Journalism
  • Social Network Analysis at Morning Analytics
    • Case-Attribute Data versus Social Network Data
  • Social Network Analysis
  • Terminology from Social Networks
    • Centrality Measures
    • The Industry of Centrality Measures
  • Thought Experiment
  • Morningside Analytics
    • How Visualizations Help Us Find Schools of Fish
  • More Background on Social Network Analysis from a Statistical Point of View
    • Representations of Networks and Eigenvalue Centrality
    • A First Example of Random Graphs: The Erdos-Renyi Model
    • A Second Example of Random Graphs: The Exponential Random Graph Model
      • Inference for ERGMs
      • Further examples of random graphs: latent space models, small-world networks
  • Data Journalism
    • A Bit of History on Data Journalism
    • Writing Technical Journalism: Advice from an Expert
• 11. Causality
  • Correlation Doesnt Imply Causation
    • Asking Causal Questions
    • Confounders: A Dating Example
  • OK Cupids Attempt
  • The Gold Standard: Randomized Clinical Trials
  • A/B Tests
  • Second Best: Observational Studies
    • Simpsons Paradox
    • The Rubin Causal Model
    • Visualizing Causality
    • Definition: The Causal Effect
  • Three Pieces of Advice
• 12. Epidemiology
  • Madigans Background
  • Thought Experiment
  • Modern Academic Statistics
  • Medical Literature and Observational Studies
  • Stratification Does Not Solve the Confounder Problem
    • What Do People Do About Confounding Things in Practice?
  • Is There a Better Way?
  • Research Experiment (Observational Medical Outcomes Partnership)
  • Closing Thought Experiment
• 13. Lessons Learned from Data Competitions: Data Leakage and Model Evaluation
  • Claudias Data Scientist Profile
    • The Life of a Chief Data Scientist
    • On Being a Female Data Scientist
  • Data Mining Competitions
  • How to Be a Good Modeler
  • Data Leakage
    • Market Predictions
    • Amazon Case Study: Big Spenders
    • A Jewelry Sampling Problem
    • IBM Customer Targeting
    • Breast Cancer Detection
    • Pneumonia Prediction
  • How to Avoid Leakage
  • Evaluating Models
    • Accuracy: Meh
    • Probabilities Matter, Not 0s and 1s
  • Choosing an Algorithm
  • A Final Example
  • Parting Thoughts
• 14. Data Engineering: MapReduce, Pregel, and Hadoop
  • About David Crawshaw
  • Thought Experiment
  • MapReduce
  • Word Frequency Problem
    • Enter MapReduce
  • Other Examples of MapReduce
    • What Cant MapReduce Do?
  • Pregel
  • About Josh Wills
  • Thought Experiment
  • On Being a Data Scientist
    • Data Abundance Versus Data Scarcity
    • Designing Models
      • Mind the gap
  • Economic Interlude: Hadoop
    • A Brief Introduction to Hadoop
    • Cloudera
  • Back to Josh: Workflow
  • So How to Get Started with Hadoop?
• 15. The Students Speak
  • Process Thinking
  • Naive No Longer
  • Helping Hands
  • Your Mileage May Vary
  • Bridging Tunnels
  • Some of Our Work
• 16. Next-Generation Data Scientists, Hubris, and Ethics
  • What Just Happened?
  • What Is Data Science (Again)?
  • What Are Next-Gen Data Scientists?
    • Being Problem Solvers
    • Cultivating Soft Skills
    • Being Question Askers
  • Being an Ethical Data Scientist
  • Career Advice
• Index
• About the Authors
• Colophon
• Copyright

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