Unit Index | The Analytics Edge | Sloan School of Management

Topics	Course Materials
Unit 1: An Introduction to Analytics
1.1.1 Welcome to Unit 1	1.1.1 Video: Welcome to Unit 1
1.2 The Analytics Edge: Intelligence, Happiness, and Health	1.2.1 Video 1: Introduction to The Analytics Edge 1.2.2 Video 2: Example 1 - IBM Watson 1.2.3 Video 3: Example 2 - eHarmony 1.2.4 Video 4: Example 3 - The Framingham Heart Study 1.2.5 Video 5: Example 4 - D2Hawkeye 1.2.6 Video 6: This Class
1.3 Working with Data: An Introduction to R	1.3.1 Download and Install R 1.3.2 Video 1: Why R? 1.3.3 Quick Question 1.3.4 Video 2: Getting Started in R 1.3.5 Quick Question 1.3.6 Video 3: Vectors and Data Frames 1.3.7 Quick Question 1.3.8 Video 4: Loading Data Files 1.3.9 Quick Question 1.3.10 Video 5: Data Analysis - Summary Statistics and Scatterplots 1.3.11 Quick Question 1.3.12 Video 6: Data Analysis - Plots and Summary Tables 1.3.13 Quick Question 1.3.14 Video 7: Saving with Script Files
1.4 Recitation: Understanding Food - Nutritional Education with Data	1.4.1 Welcome to Recitation 1 1.4.2 Video 1: The Importance of Food and Nutrition 1.4.3 Video 2: Working with Data in R 1.4.4 Video 3: Data Analysis 1.4.5 Video 4: Creating Plots in R 1.4.6 Video 5: Adding Variables 1.4.7 Video 6: Summary Tables
1.5 Assignment 1	1.5.1 An Analytical Detective 1.5.2 Stock Dynamics 1.5.3 Demographics and Employment in the United States 1.5.4 Internet Privacy Poll
Unit 2: Linear Regression
2.1 Welcome to Unit 2	2.1.1 Welcome to Unit 2
2.2 The Statistical Sommelier: An Introduction to Linear Regression	2.2.1 Video 1: Predicting the Quality of Wine 2.2.2 Quick Question 2.2.3 Video 2: One-Variable Linear Regression 2.2.4 Quick Question 2.2.5 Video 3: Multiple Linear Regression 2.2.6 Quick Question 2.2.7 Video 4: Linear Regression in R 2.2.8 Quick Question 2.2.9 Video 5: Understanding the Model 2.2.10 Quick Question 2.2.11 Video 6: Correlation and Multicollinearity 2.2.12 Quick Question 2.2.13 Video 7: Making Predictions 2.2.14 Quick Question 2.2.15 Video 8: Comparing the Model to the Experts
2.3 Moneyball: The Power of Sports Analytics	2.3.1 A Quick Introduction to Baseball 2.3.2 Video 1: The Story of Moneyball 2.3.3 Video 2: Making it to the Playoffs 2.3.4 Quick Question 2.3.5 Video 3: Predicting Runs 2.3.6 Quick Question 2.3.7 Video 4: Using the Models to Make Predictions 2.3.8 Quick Question 2.3.9 Video 5: Winning the World Series 2.3.10 Quick Question 2.3.11 Video 6: The Analytics Edge in Sports 2.3.12 Quick Question
2.4 Recitation: Playing Moneyball in the NBA	2.4.1 Welcome to Recitation 2 2.4.2 Video 1: The Data 2.4.3 Video 2: Playoffs and Wins 2.4.4 Video 3: Points Scored 2.4.5 Video 4: Making Predictions
2.5 Assignment 2	2.5.1 Climate Change 2.5.2 Reading Test Scores 2.5.3 Detecting Flu Epidemics via Search Engine Query Data 2.5.4 State Data
Unit 3: Logistical Regression
3.1 Welcome to Unit 3	3.1.1 Welcome to Unit 3
3.2 Modeling the Expert: An Introduction to Logistic Regression	3.2.1 Video 1: Replicating Expert Assessment 3.2.2 Video 2: Building the Dataset 3.2.3 Quick Question 3.2.4 Video 3: Logistic Regression 3.2.5 Quick Question 3.2.6 Video 4: Logistic Regression in R 3.2.7 Quick Question 3.2.8 Video 5: Thresholding 3.2.9 Quick Question 3.2.10 Video 6: ROC Curves 3.2.11 Quick Question 3.2.12 Video 7: Interpreting the Model 3.2.13 Quick Question 3.2.14 Video 8: The Analytics Edge
3.3 The Framingham Heart Study: Evaluating Risk Factors to Save Lives	3.3.1 Video 1: The Framingham Heart Study 3.3.2 Quick Question 3.3.3 Video 2: Risk Factors 3.3.4 Quick Question 3.3.5 Video 3: A Logistic Regression Model 3.3.6 Quick Question 3.3.7 Video 4: Validating the Model 3.3.8 Quick Question 3.3.9 Video 5: Interventions 3.3.10 Quick Question 3.3.11 Video 6: Overall Impact
3.4 Recitation: Election Forecasting - Predicting the Winner Before any Votes are Cast	3.4.1 Welcome to Recitation 3 3.4.2 Video 1: Election Prediction 3.4.3 Video 2: Dealing with Missing Data 3.4.4 Video 3: A Sophisticated Baseline Method 3.4.5 Video 4: Logistic Regression Models 3.4.6 Video 5: Test Set Predictions
3.5 Assignment 3	3.5.1 Popularity of Music Records 3.5.2 Predicting the Baseball World Series Champion
Unit 4: Trees
4.1 Welcome to Unit 4	4.1.1 Welcome to Unit 4
4.2 Judge, Jury, and Classifier: An Introduction to Trees	4.2.1 Video 1: The Supreme Court 4.2.2 Quick Question 4.2.3 Video 2: CART 4.2.4 Quick Question 4.2.5 Video 3: Splitting and Predictions 4.2.6 Quick Question 4.2.7 Video 4: CART in R 4.2.8 Quick Question 4.2.9 Video 5: Random Forests 4.2.10 Quick Question 4.2.11 Video 6: Cross-Validation 4.2.12 Quick Question 4.2.13 Video 7: The Model v. The Experts
4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story	4.3.1 Video 1: The Story of D2Hawkeye 4.3.2 Quick Question 4.3.3 Video 2: Claims Data 4.3.4 Quick Question 4.3.5 Video 3: The Variables 4.3.6 Quick Question 4.3.7 Video 4: Error Measures 4.3.8 Quick Question 4.3.9 Video 5: CART to Predict Cost 4.3.10 Quick Question 4.3.11 Video 6: Claims Data in R 4.3.12 Quick Question 4.3.13 Video 7: Baseline Method and Penalty Matrix 4.3.14 Quick Question 4.3.15 Video 8: Predicting Healthcare Cost in R 4.3.16 Quick Question 4.3.17 Video 9: Results
4.4 Recitation: Location, Location, Location - Regression Trees for Housing Data	4.4.1 Welcome to Recitation 4 4.4.2 Video 1: Boston Housing Data 4.4.3 Video 2: The Data 4.4.4 Video 3: Geographical Predictions 4.4.5 Video 4: Regression Trees 4.4.6 Video 5: Putting it all Together 4.4.7 Video 6: The CP Parameter 4.4.8 Video 7: Cross-Validation
4.5 Assignment 4	4.5.1 Predicting Earnings from Census Data 4.5.2 Understanding Why People Vote 4.5.3 State Data Revisted
Unit 5: Text Analytics
5.1 Welcome to Unit 5	5.1.1 Welcome to Unit 5
5.2 Turning Tweets into Knowledge: An Introduction to Text Analytics	5.2.1 Video 1: Twitter 5.2.2 Video 2: Text Analytics 5.2.3 Quick Question 5.2.4 Video 3: Creating the Dataset 5.2.5 Quick Question 5.2.6 Video 4: Bag of Words 5.2.7 Quick Question 5.2.8 Video 5: Pre-Processing in R 5.2.9 Quick Question 5.2.10 Video 6: Bag of Words in R 5.2.11 Quick Question 5.2.12 Video 7: Predicting Sentiment 5.2.13 Quick Question 5.2.14 Video 8: Conclusion
5.3 Man vs. Machine: How IBM Built a Jeopardy Champion	5.3.1 Video 1: IBM Watson 5.3.2 Quick Question 5.3.3 Video 2: The Game of Jeopardy 5.3.4 Quick Question 5.3.5 Video 3: Watson's Database and Tools 5.3.6 Quick Question 5.3.7 Video 4: How Watson Works - Steps 1 and 2 5.3.8 Quick Question 5.3.9 Video 5: How Watson Works - Steps 3 and 4 5.3.10 Quick Question 5.3.11 Video 6: The Results
5.4 Recitation: Predictive Coding - Bringing Text Analytics to the Courtroom	5.4.1 Welcome to Recitation 5 5.4.2 Video 1: The Story of Enron 5.4.3 Video 2: The Data 5.4.4 Video 3: Pre-Processing 5.4.5 Video 4: Bag of Words 5.4.6 Video 5: Building Models 5.4.7 Video 6: Evaluating the Model 5.4.8 Video 7: The ROC Curve 5.4.9 Video 8: Predictive Coding Today
5.5 Assignment 5	5.5.1 Separating Spam from Ham 5.5.2 Detecting Vandalism on Wikipedia 5.5.3 Automating Reviews in Medicine
Unit 6: Clustering
6.1 Welcome to Unit 6	6.1.1 Welcome to Unit 6
6.2 Recommendations Worth a Million: An Introduction to Clustering	6.2.1 Video 1: Introduction to Netflix 6.2.2 Quick Question 6.2.3 Video 2: Recommendation Systems 6.2.4 Quick Question 6.2.5 Video 3: Movie Data and Clustering 6.2.6 Quick Question 6.2.7 Video 4: Computing Distances 6.2.8 Quick Question 6.2.9 Video 5: Hierarchical Clustering 6.2.10 Quick Question 6.2.11 Video 6: Getting the Data 6.2.12 Quick Question 6.2.13 Video 7: Hierarchical Clustering in R 6.2.14 Quick Question 6.2.15 Video 8: The Analytics Edge of Recommendation Systems
6.3 Predictive Diagnosis: Discovering Patterns for Disease Detection	6.3.1 Video 1: Heart Attacks 6.3.2 Quick Question 6.3.3 Video 2: The Data 6.3.4 Quick Question 6.3.5 Video 3: Predicting Heart Attacks using Clustering 6.3.6 Quick Question 6.3.7 Video 4: Understanding Cluster Patterns 6.3.8 Quick Question 6.3.9 Video 5: The Analytics Edge
6.4 Recitation: Seeing the Big Picture - Segmenting Images to Create Data	6.4.1 Welcome to Recitation 6 6.4.2 Video 1: Image Segmentation 6.4.3 Video 2: Clustering Pixels 6.4.4 Video 3: Hierarchical Clustering 6.4.5 Caution 6.4.6 Video 4: MRI Image 6.4.7 Video 5: K-Means Clustering 6.4.8 Video 6: Detecting Tumors 6.4.9 Video 7: Comparing Methods
6.5 Assignment 6	6.5.1 Document Clustering with Daily Kos
Unit 7: Visualization
7.1 Welcome to Unit 7	7.1.1 Welcome to Unit 7
7.2 Visualizing the World: An Introduction to Visualization	7.2.1 Video 1: The Power of Visualizations 7.2.2 Quick Question 7.2.3 Video 2: The World Health Organization (WHO) 7.2.4 Quick Question 7.2.5 Video 3: What is Data Visualization? 7.2.6 Quick Question 7.2.7 Video 4: Basic Scatterplots Using ggplot 7.2.8 Quick Question 7.2.9 Video 5: Advanced Scatterplots Using ggplot 7.2.10 Quick Question
7.3 The Analytical Policeman: Visualization for Law and Order	7.3.1 Video 1: Predictive Policing 7.3.2 Quick Question 7.3.3 Video 2: Visualizing Crime Over Time 7.3.4 Quick Question 7.3.5 Video 3: A Line Plot 7.3.6 Quick Question 7.3.7 Video 4: A Heatmap 7.3.8 Quick Question 7.3.9 Video 5: A Geographical Hot Spot Map 7.3.10 Quick Question 7.3.11 Video 6: A Heatmap on the United States 7.3.12 Quick Question 7.3.13 Video 7: The Analytics Edge
7.4 Recitation: The Good, the Bad, and the Ugly - Visualizations	7.4.1 Welcome to Recitation 7 7.4.2 Video 1: Introduction 7.4.3 Video 2: Pie Charts 7.4.4 Video 3: Bar Charts in R 7.4.5 Video 4: A Better Visualization 7.4.6 Video 5: World Maps in R 7.4.7 Video 6: Scales 7.4.8 Video 7: Using Line Charts Instead
7.5 Assignment 7	7.5.1 Visualizing Attributes of Parole Violators
Unit 8: Linear Optimization
8.1 Welcome to Unit 8	8.1.1 Welcome to Unit 8 8.1.2 Installing LibreOffice or OpenOffice 8.1.3 Spreadsheet Tutorial
8.2 Airline Revenue Management: An Introduction to Linear Optimization	8.2.1 Video 1: Introduction 8.2.2 Video 2: A Single Flight 8.2.3 Quick Question 8.2.4 Video 3: The Problem Formulation 8.2.5 Quick Question 8.2.6 Video 4: Solving the Problem 8.2.7 Quick Question 8.2.8 Video 5: Visualizing the Problem 8.2.9 Quick Question 8.2.10 Video 6: Sensitivity Analysis 8.2.11 Quick Question 8.2.12 Video 7: Connecting Flights 8.2.13 Quick Question 8.2.14 Video 8: The Edge of Revenue Management
8.3 Radiation Therapy: An Application of Linear Optimization	8.3.1 Video 1: Introduction 8.3.2 Quick Question 8.3.3 Video 2: An Optimization Problem 8.3.4 Quick Question 8.3.5 Video 3: Solving the Problem 8.3.6 Quick Question 8.3.7 Video 4: A Head and Neck Case 8.3.8 Quick Question 8.3.9 Video 5: Sensitivity Analysis 8.3.10 Quick Question 8.3.11 Video 6: The Analytics Edge
8.4 Recitation: Google AdWords - Optimizing Online Advertising	8.4.1 Welcome to Recitation 8 8.4.2 Video 1: Introduction 8.4.3 Video 2: How Online Advertising Works 8.4.4 Video 3: Prices and Queries 8.4.5 Video 4: Modeling the Problem 8.4.6 Video 5: Solving the Problem 8.4.7 Video 6: A Greedy Approach 8.4.8 Video 7: Sensitivity Analysis 8.4.9 Video 8: Extensions and the Edge
Unit 9: Integer Optimization
9.1 Welcome to Unit 9	9.1.1 Welcome to Unit 9
9.2 Sports Scheduling: An Introduction to Integer Optimization	9.2.1 Video 1: Introduction 9.2.2 Quick Question 9.2.3 Video 2: The Optimization Problem 9.2.4 Quick Question 9.2.5 Video 3: Solving the Problem 9.2.6 Quick Question 9.2.7 Video 4: Logical Constraints 9.2.8 Quick Question 9.2.9 Video 5: The Edge
9.3 eHarmony: Maximizing the Probability of Love	9.3.1 Video 1: The Goal of eHarmony 9.3.2 Quick Question 9.3.3 Video 2: Using Integer Optimization 9.3.4 Quick Question 9.3.5 Video 3: Predicting Compatibility Scores 9.3.6 Quick Question 9.3.7 Video 4: The Analytics Edge
9.4 Recitation: Operating Room Scheduling - Making Hospitals Run Smoothly	9.4.1 Welcome to Recitation 9 9.4.2 Video 1: The Problem 9.4.3 Video 2: An Optimization Model 9.4.4 Video 3: Solving the Problem 9.4.5 Video 4: The Solution
9.5 Assignment 9	9.5.1 Even' Star Organic Farm Revisited 9.5.2 Gerrymandering New Mexico