Doing Data Science
Publisher: O'Reilly Media, Inc, USA
Published: 2013
Language: eng
Number of pages: 408
ISBN-13: 9781449358655
Table of contents: title: Preface
pagenum: xiii; label: 1
title: Introduction: What Is Data Science?
pagenum: 1; title: Big Data and Data Science Hype
pagenum: 1; title: Getting Past the Hype
pagenum: 3; title: Why Now?
pagenum: 4; title: Datafication
pagenum: 5; title: The Current Landscape (with a Little History)
pagenum: 6; title: Data Science Jobs
pagenum: 10; title: A Data Science Profile
pagenum: 10; title: Thought Experiment: Meta-Definition
pagenum: 13; title: OK, So What Is a Data Scientist, Really?
pagenum: 14; title: In Academia
pagenum: 14; title: In Industry
pagenum: 15; label: 2
title: Statistical Inference, Exploratory Data Analysis, and the Data Science Process
pagenum: 17; title: Statistical Thinking in the Age of Big Data
pagenum: 17; title: Statistical Inference
pagenum: 18; title: Populations and Samples
pagenum: 19; title: Populations and Samples of Big Data
pagenum: 21; title: Big Data Can Mean Big Assumptions
pagenum: 24; title: Modeling
pagenum: 26; title: Exploratory Data Analysis
pagenum: 34; title: Philosophy of Exploratory Data Analysis
pagenum: 36; title: Exercise: EDA
pagenum: 37; title: The Data Science Process
pagenum: 41; title: A Data Scientist's Role in This Process
pagenum: 43; title: Thought Experiment: How Would You Simulate Chaos?
pagenum: 44; title: Case Study: RealDirect
pagenum: 46; title: How Does RealDirect Make Money?
pagenum: 47; title: Exercise: RealDirect Data Strategy
pagenum: 48; label: 3
title: Algorithms
pagenum: 51; title: Machine Learning Algorithms
pagenum: 52; title: Three Basic Algorithms
pagenum: 54; title: Linear Regression
pagenum: 55; title: k-Nearest Neighbors (k-NN)
pagenum: 71; title: k-means
pagenum: 81; title: Exercise: Basic Machine Learning Algorithms
pagenum: 85; title: Solutions
pagenum: 86; title: Summing It All Up
pagenum: 90; title: Thought Experiment: Automated Statistician
pagenum: 91; label: 4
title: Spam Filters, Naive Bayes, and Wrangling
pagenum: 93; title: Thought Experiment: Learning by Example
pagenum: 93; title: Why Won't Linear Regression Work for Filtering Spam?
pagenum: 95; title: How About k-Nearest Neighbors?
pagenum: 96; title: Naive Bayes
pagenum: 98; title: Bayes' Law
pagenum: 98; title: A Spam Filter for Individual Words
pagenum: 99; title: A Spam Filter That Combines Words: Naive Bayes
pagenum: 101; title: Fancy It Up: Laplace Smoothing
pagenum: 103; title: Comparing Naive Bayes to k-NN
pagenum: 105; title: Sample Code in bash
pagenum: 105; title: Scraping the Web: APIs and Other Tools
pagenum: 106; title: Jake's Exercise: Naive Bayes for Article Classification
pagenum: 109; title: Sample R Code for Dealing with the NYT API
pagenum: 110; label: 5
title: Logistic Regression
pagenum: 113; title: Thought Experiments
pagenum: 114; title: Classifiers
pagenum: 115; title: Runtime
pagenum: 116; title: You
pagenum: 117; title: Interpretability
pagenum: 117; title: Scalability
pagenum: 117; title: Media 6 Degrees Logistic Regression Case Study
pagenum: 118; title: Click Models
pagenum: 118; title: The Underlying Math
pagenum: 120; title: Estimating α and β
pagenum: 122; title: Newton's Method
pagenum: 124; title: Stochastic Gradient Descent
pagenum: 124; title: Implementation
pagenum: 124; title: Evaluation
pagenum: 125; title: Media 6 Degrees Exercise
pagenum: 128; title: Sample R Code
pagenum: 129; label: 6
title: Time Stamps and Financial Modeling
pagenum: 135; title: Kyle Teague and GetGlue
pagenum: 135; title: Timestamps
pagenum: 137; title: Exploratory Data Analysis (EDA)
pagenum: 138; title: Metrics and New Variables or Features
pagenum: 142; title: What's Next?
pagenum: 142; title: Cathy O'Neil
pagenum: 144; title: Thought Experiment
pagenum: 144; title: Financial Modeling
pagenum: 145; title: In-Sample, Out-of-Sample, and Causality
pagenum: 146; title: Preparing Financial Data
pagenum: 148; title: Log Returns
pagenum: 149; title: Example: The S&P Index
pagenum: 151; title: Working out a Volatility Measurement
pagenum: 153; title: Exponential Downweighting
pagenum: 155; title: The Financial Modeling Feedback Loop
pagenum: 156; title: Why Regression?
pagenum: 158; title: Adding Priors
pagenum: 158; title: A Baby Model
pagenum: 159; title: Exercise: GetGlue and Timestamped Event Data
pagenum: 162; title: Exercise: Financial Data
pagenum: 163; label: 7
title: Extracting Meaning from Data
pagenum: 165; title: William Cukierski
pagenum: 165; title: Background: Data Science Competitions
pagenum: 166; title: Background: Crowdsourcing
pagenum: 167; title: The Kaggle Model
pagenum: 170; title: A Single Contestant
pagenum: 170; title: Their Customers
pagenum: 172; title: Thought Experiment: What Are the Ethical Implications of a Robo-Grader?
pagenum: 174; title: Feature Selection
pagenum: 176; title: Example: User Retention
pagenum: 177; title: Filters
pagenum: 181; title: Wrappers
pagenum: 181; title: Embedded Methods: Decision Trees
pagenum: 184; title: Entropy
pagenum: 186; title: The Decision Tree Algorithm
pagenum: 187; title: Handling Continuous Variables in Decision Trees
pagenum: 188; title: Random Forests
pagenum: 190; title: User Retention: Interpretability Versus Predictive Power
pagenum: 192; title: David Huffaker: Google's Hybrid Approach to Social Research
pagenum: 193; title: Moving from Descriptive to Predictive
pagenum: 194; title: Social at Google
pagenum: 196; title: Privacy
pagenum: 196; title: Thought Experiment: What Is the Best Way to Decrease Concern and Increase Understanding and Control?
pagenum: 197; label: 8
title: Recommendation Engines: Building a User-Facing Data Product at Scale
pagenum: 199; title: A Real-World Recommendation Engine
pagenum: 200; title: Nearest Neighbor Algorithm Review
pagenum: 202; title: Some Problems with Nearest Neighbors
pagenum: 202; title: Beyond Nearest Neighbor: Machine Learning Classification
pagenum: 204; title: The Dimensionality Problem
pagenum: 206; title: Singular Value Decomposition (SVD)
pagenum: 207; title: Important Properties of SVD
pagenum: 208; title: Principal Component Analysis (PCA)
pagenum: 209; title: Alternating Least Squares
pagenum: 211; title: Fix V and Update U
pagenum: 212; title: Last Thoughts on These Algorithms
pagenum: 213; title: Thought Experiment: Filter Bubbles
pagenum: 213; title: Exercise: Build Your Own Recommendation System
pagenum: 214; title: Sample Code in Python
pagenum: 214; label: 9
title: Data Visualization and Fraud Detection
pagenum: 217; title: Data Visualization History
pagenum: 217; title: Gabriel Tarde
pagenum: 218; title: Mark's Thought Experiment
pagenum: 219; title: What Is Data Science, Redux?
pagenum: 220; title: Processing
pagenum: 221; title: Franco Moretti
pagenum: 221; title: A Sample of Data Visualization Projects
pagenum: 222; title: Mark's Data Visualization Projects
pagenum: 227; title: New York Times Lobby: Moveable Type
pagenum: 227; title: Project Cascade: Lives on a Screen
pagenum: 230; title: Cronkite Plaza
pagenum: 231; title: eBay Transactions and Books
pagenum: 232; title: Public Theater Shakespeare Machine
pagenum: 234; title: Goals of These Exhibits
pagenum: 235; title: Data Science and Risk
pagenum: 235; title: About Square
pagenum: 236; title: The Risk Challenge
pagenum: 237; title: The Trouble with Performance Estimation
pagenum: 240; title: Model Building Tips
pagenum: 244; title: Data Visualization at Square
pagenum: 248; title: Ian's Thought Experiment
pagenum: 249; title: Data Visualization for the Rest of Us
pagenum: 250; title: Data Visualization Exercise
pagenum: 251; label: 10
title: Social Networks and Data Journalism
pagenum: 253; title: Social Network Analysis at Morning Analytics
pagenum: 254; title: Case-Attribute Data versus Social Network Data
pagenum: 254; title: Social Network Analysis
pagenum: 255; title: Terminology from Social Networks
pagenum: 256; title: Centrality Measures
pagenum: 257; title: The Industry of Centrality Measures
pagenum: 258; title: Thought Experiment
pagenum: 259; title: Morningside Analytics
pagenum: 260; title: How Visualizations Help Us Find Schools of Fish
pagenum: 262; title: More Background on Social Network Analysis from a Statistical Point of View
pagenum: 263; title: Representations of Networks and Eigenvalue Centrality
pagenum: 264; title: A First Example of Random Graphs: The Erdos-Renyi Model
pagenum: 265; title: A Second Example of Random Graphs: The Exponential Random Graph Model
pagenum: 266; title: Data Journalism
pagenum: 269; title: A Bit of History on Data Journalism
pagenum: 269; title: Writing Technical Journalism: Advice from an Expert
pagenum: 270; label: 11
title: Causality
pagenum: 273; title: Correlation Doesn't Imply Causation
pagenum: 274; title: Asking Causal Questions
pagenum: 274; title: Confounders: A Dating Example
pagenum: 275; title: OK Cupid's Attempt
pagenum: 276; title: The Gold Standard: Randomized Clinical Trials
pagenum: 279; title: A/B Tests
pagenum: 280; title: Second Best: Observational Studies
pagenum: 283; title: Simpson's Paradox
pagenum: 283; title: The Rubin Causal Model
pagenum: 285; title: Visualizing Causality
pagenum: 286; title: Definition: The Causal Effect
pagenum: 287; title: Three Pieces of Advice
pagenum: 289; label: 12
title: Epidemiology
pagenum: 291; title: Madigan's Background
pagenum: 291; title: Thought Experiment
pagenum: 292; title: Modern Academic Statistics
pagenum: 293; title: Medical Literature and Observational Studies
pagenum: 293; title: Stratification Does Not Solve the Confounder Problem
pagenum: 294; title: What Do People Do About Confounding Things in Practice?
pagenum: 295; title: Is There a Better Way?
pagenum: 296; title: Research Experiment (Observational Medical Outcomes Partnership)
pagenum: 298; title: Closing Thought Experiment
pagenum: 302; label: 13
title: Lessons Learned from Data Competitions: Data Leakage and Model Evaluation
pagenum: 303; title: Claudia's Data Scientist Profile
pagenum: 304; title: The Life of a Chief Data Scientist
pagenum: 304; title: On Being a Female Data Scientist
pagenum: 305; title: Data Mining Competitions
pagenum: 305; title: How to Be a Good Modeler
pagenum: 307; title: Data Leakage
pagenum: 307; title: Market Predictions
pagenum: 308; title: Amazon Case Study: Big Spenders
pagenum: 308; title: A Jewelry Sampling Problem
pagenum: 309; title: IBM Customer Targeting
pagenum: 309; title: Breast Cancer Detection
pagenum: 310; title: Pneumonia Prediction
pagenum: 311; title: How to Avoid Leakage
pagenum: 313; title: Evaluating Models
pagenum: 313; title: Accuracy: Meh
pagenum: 315; title: Probabilities Matter, Not 0s and 1s
pagenum: 315; title: Choosing an Algorithm
pagenum: 318; title: A Final Example
pagenum: 319; title: Parting Thoughts
pagenum: 320; label: 14
title: Data Engineering: MapReduce, Pregel, and Hadoop
pagenum: 321; title: About David Crawshaw
pagenum: 322; title: Thought Experiment
pagenum: 323; title: MapReduce
pagenum: 324; title: Word Frequency Problem
pagenum: 325; title: Enter MapReduce
pagenum: 328; title: Other Examples of MapReduce
pagenum: 329; title: What Can't MapReduce Do?
pagenum: 330; title: Pregel
pagenum: 331; title: About Josh Wills
pagenum: 331; title: Thought Experiment
pagenum: 332; title: On Being a Data Scientist
pagenum: 332; title: Data Abundance Versus Data Scarcity
pagenum: 332; title: Designing Models
pagenum: 333; title: Economic Interlude: Hadoop
pagenum: 333; title: A Brief Introduction to Hadoop
pagenum: 334; title: Cloudera
pagenum: 334; title: Back to Josh: Workflow
pagenum: 335; title: So How to Get Started with Hadoop?
pagenum: 335; label: 15
title: The Students Speak
pagenum: 337; title: Process Thinking
pagenum: 337; title: Naive No Longer
pagenum: 339; title: Helping Hands
pagenum: 340; title: Your Mileage May Vary
pagenum: 342; title: Bridging Tunnels
pagenum: 345; title: Some of Our Work
pagenum: 345; label: 16
title: Next-Generation Data Scientists, Hubris, and Ethics
pagenum: 347; title: What Just Happened?
pagenum: 347; title: What Is Data Science (Again)?
pagenum: 348; title: What Are Next-Gen Data Scientists?
pagenum: 350; title: Being Problem Solvers
pagenum: 350; title: Cultivating Soft Skills
pagenum: 351; title: Being Question Askers
pagenum: 352; title: Being an Ethical Data Scientist
pagenum: 354; title: Career Advice
pagenum: 359; title: Index
pagenum: 361