The Analytics Edge (Spring 2017)

• Video lectures
• Captions/transcript
• Interactive assessments
• Lecture notes
• Assignments: problem sets with solutions

This course presents real-world examples in which quantitative methods provide a significant competitive edge that has led to a first order impact on some of today's most important companies. We outline the competitive landscape and present the key quantitative methods that created the edge (data-mining, dynamic optimization, simulation), and discuss their impact.

1.1.1 Welcome to Unit 1: An Introduction to Analytics.
1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge.
1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson.
1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony.
1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study.
1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye.
1.2.6 The Analytics Edge - Video 6: This Class.
1.3.2 Working with Data - Video 1: History of R.
1.3.4 Working with Data - Video 2: Getting Started in R.
1.3.6 Working with Data - Video 3: Vectors and Data Frames.
1.3.8 Working with Data - Video 4: Loading Data Files.
1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots.
1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables.
1.3.14 Working with Data - Video 7: Saving with Script Files.
1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data.
1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition.
1.4.3 R1. Understanding Food - Video 2: Working with Data in R.
1.4.4 R1. Understanding Food - Video 3: Data Analysis.
1.4.5 R1. Understanding Food - Video 4: Creating Plots in R.
1.4.6 R1. Understanding Food - Video 5: Adding Variables.
1.4.7 R1. Understanding Food - Video 6: Summary Tables.
2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression.
2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine.
2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression.
2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression.
2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R.
2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model.
2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity.
2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions.
2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts.
2.3.2 Sports Analytics - Video 1: The Story of Moneyball.
2.3.3 Sports Analytics - Video 2: Making It to the Playoffs.
2.3.5 Sports Analytics - Video 3: Predicting Runs.
2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions.
2.3.9 Sports Analytics - Video 5: Winning the World Series.
2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports.
2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2.
2.4.2 R2. Moneyball in the NBA - Video 1: The Data.
2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins.
2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored.
2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions.
3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression.
3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment.
3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset.
3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression.
3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R.
3.2.8 Introduction to Logistical Regression - Video 5: Thresholding.
3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves.
3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model.
3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge.
3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives.
3.3.3 The Framingham Heart Study - Video 2: Risk Factors.
3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model.
3.3.7 The Framingham Heart Study - Video 4: Validating the Model.
3.3.9 The Framingham Heart Study - Video 5: Interventions.
3.3.11 The Framingham Heart Study - Video 6: Overall Impact.
3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast.
3.4.2 R3. Election Forecasting - Video 1: Election Prediction.
3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data.
3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method.
3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models.
3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions.
4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees.
4.2.1 An Introduction to Trees - Video 1: The Supreme Court.
4.2.3 An Introduction to Trees - Video 2: CART.
4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions.
4.2.7 An Introduction to Trees - Video 4: CART in R.
4.2.9 An Introduction to Trees - Video 5: Random Forests.
4.2.11 An Introduction to Trees - Video 6: Cross-Validation.
4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts.
4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye.
4.3.3 Healthcare Costs - Video 2: Claims Data.
4.3.5 Healthcare Costs - Video 3: The Variables.
4.3.7 Healthcare Costs- Video 4: Error Measures.
4.3.9 Healthcare Costs - Video 5: CART to Predict Cost.
4.3.11 Healthcare Costs - Video 6: Claims Data in R.
4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix.
4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R.
4.3.17 Healthcare Costs - Video 9: Results.
4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data.
4.4.2 R4. Regression Trees - Video 1: Boston Housing Data.
4.4.3 R4. Regression Trees- Video 2: The Data.
4.4.4 R4. Regression Trees - Video 3: Geographical Predictions.
4.4.5 R4. Regression Trees - Video 4: Regression Trees.
4.4.6 R4. Regression Trees - Video 5: Putting it all Together.
4.4.7 R4. Regression Trees - Video 6: The CP Parameter.
4.4.8 R4. Regression Trees - Video 7: Cross-Validation.
5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics.
5.2.1 An Introduction to Text Analytics - Video 1: Twitter.
5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics.
5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset.
5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words.
5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R.
5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R.
5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment.
5.2.14 An Introduction to Text Analytics - Video 8: Conclusion.
5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson.
5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy.
5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools.
5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2.
5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4.
5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results.
5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom.
5.4.2 R5. Predictive Coding - Video 1: The Story of Enron.
5.4.3 R5. Predictive Coding - Video 2: The Data.
5.4.4 R5. Predictive Coding - Video 3: Pre-Processing.
5.4.5 R5. Predictive Coding - Video 4: Bag of Words.
5.4.6 R5. Predictive Coding - Video 5: Building Models.
5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model.
5.4.8 R5. Predictive Coding - Video 7: The ROC Curve.
5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today.
6.1.1 Welcome to Unit 6 - An Introduction to Clustering.
6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix.
6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems.
6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering.
6.2.7 An Introduction to Clustering - Video 4: Computing Distances.
6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering.
6.2.11 An Introduction to Clustering - Video 6: Getting the Data.
6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R.
6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems.
6.3.1 Predictive Diagnosis - Video 1: Heart Attacks.
6.3.3 Predictive Diagnosis - Video 2: The Data.
6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering.
6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns.
6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge.
6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data.
6.4.2 Recitation 6 - Video 1: Image Segmentation.
6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels.
6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering.
6.4.6 R6. Segmenting Images - Video 4: MRI Image.
6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering.
6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors.
6.4.9 R6. Segmenting Images - Video 7: Comparing Methods.
7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization.
7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations.
7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO).
7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization?.
7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot.
7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot.
7.3.1 Visualization for Law and Order - Video 1: Predictive Policing.
7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time.
7.3.5 Visualization for Law and Order - Video 3: A Line Plot.
7.3.7 Visualization for Law and Order - Video 4: A Heatmap.
7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map.
7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States.
7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge.
7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization.
7.4.2 R7. Visualization - Video 1: Introduction.
7.4.3 R7. Visualization - Video 2: Pie Charts.
7.4.4 R7. Visualization - Video 3: Bar Charts in R.
7.4.5 R7. Visualization - Video 4: A Better Visualization.
7.4.6 R7. Visualization - Video 5: World Maps in R.
7.4.7 R7. Visualization - Video 6: Scales.
7.4.8 R7. Visualization - Video 7: Using Line Charts Instead.
8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization.
8.2.1 An Introduction to Linear Optimization - Video 1: Introduction.
8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight.
8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation.
8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem.
8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem.
8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis.
8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights.
8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management.
8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy.
8.3.3 Radiation Therapy - Video 2: An Optimization Problem.
8.3.5 Radiation Therapy - Video 3: Solving the Problem.
8.3.7 Radiation Therapy - Video 4: A Head and Neck Case.
8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis.
8.3.11 Radiation Therapy - Video 6: The Analytics Edge.
8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising.
8.4.2 R8. Google AdWords - Video 1: Introduction.
8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works.
8.4.4 R8. Google AdWords - Video 3: Prices and Queries.
8.4.5 R8. Google AdWords - Video 4: Modeling the Problem.
8.4.6 R8. Google AdWords - Video 5: Solving the Problem.
8.4.7 R8. Google AdWords - Video 6: A Greedy Approach.
8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis.
8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge.
9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization.
9.2.1 Sports Scheduling - Video 1: Introduction.
9.2.3 Sports Scheduling - Video 2: The Optimization Problem.
9.2.5 Sports Scheduling - Video 3: Solving the Problem.
9.2.7 Sports Scheduling - Video 4: Logical Constraints.
9.2.9 Sports Scheduling - Video 5: The Edge.
9.3.1 eHarmony - Video 1: The Goal of eHarmony.
9.3.3 eHarmony - Video 2: Using Integer Optimization.
9.3.5 eHarmony - Video 3: Predicting Compatibility Scores.
9.3.7 eHarmony - Video 4: The Analytics Edge.
9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly.
9.4.2 R9. Operating Room Scheduling - Video 1: The Problem.
9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model.
9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem.
9.4.5 R9. Operating Room Scheduling - Video 4: The Solution.