1 1.1.1 Welcome to Unit 1: An Introduction to Analytics 2 1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge 3 1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson 4 1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony 5 1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study 6 1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye 7 1.2.6 The Analytics Edge - Video 6: This Class 8 1.3.2 Working with Data - Video 1: History of R 9 1.3.4 Working with Data - Video 2: Getting Started in R 10 1.3.6 Working with Data - Video 3: Vectors and Data Frames 11 1.3.8 Working with Data - Video 4: Loading Data Files 12 1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots 13 1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables 14 1.3.14 Working with Data - Video 7: Saving with Script Files 15 1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data 16 1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition 17 1.4.3 R1. Understanding Food - Video 2: Working with Data in R 18 1.4.4 R1. Understanding Food - Video 3: Data Analysis 19 1.4.5 R1. Understanding Food - Video 4: Creating Plots in R 20 1.4.6 R1. Understanding Food - Video 5: Adding Variables 21 1.4.7 R1. Understanding Food - Video 6: Summary Tables 22 2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression 23 2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine 24 2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression 25 2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression 26 2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R 27 2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model 28 2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity 29 2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions 30 2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts 31 2.3.2 Sports Analytics - Video 1: The Story of Moneyball 32 2.3.3 Sports Analytics - Video 2: Making It to the Playoffs 33 2.3.5 Sports Analytics - Video 3: Predicting Runs 34 2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions 35 2.3.9 Sports Analytics - Video 5: Winning the World Series 36 2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports 37 2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2 38 2.4.2 R2. Moneyball in the NBA - Video 1: The Data 39 2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins 40 2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored 41 2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions 42 3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression 43 3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment 44 3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset 45 3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression 46 3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R 47 3.2.8 Introduction to Logistical Regression - Video 5: Thresholding 48 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves 49 3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model 50 3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge 51 3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives 52 3.3.3 The Framingham Heart Study - Video 2: Risk Factors 53 3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model 54 3.3.7 The Framingham Heart Study - Video 4: Validating the Model 55 3.3.9 The Framingham Heart Study - Video 5: Interventions 56 3.3.11 The Framingham Heart Study - Video 6: Overall Impact 57 3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast 58 3.4.2 R3. Election Forecasting - Video 1: Election Prediction 59 3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data 60 3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method 61 3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models 62 3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions 63 4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees 64 4.2.1 An Introduction to Trees - Video 1: The Supreme Court 65 4.2.3 An Introduction to Trees - Video 2: CART 66 4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions 67 4.2.7 An Introduction to Trees - Video 4: CART in R 68 4.2.9 An Introduction to Trees - Video 5: Random Forests 69 4.2.11 An Introduction to Trees - Video 6: Cross-Validation 70 4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts 71 4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye 72 4.3.3 Healthcare Costs - Video 2: Claims Data 73 4.3.5 Healthcare Costs - Video 3: The Variables 74 4.3.7 Healthcare Costs- Video 4: Error Measures 75 4.3.9 Healthcare Costs - Video 5: CART to Predict Cost 76 4.3.11 Healthcare Costs - Video 6: Claims Data in R 77 4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix 78 4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R 79 4.3.17 Healthcare Costs - Video 9: Results 80 4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data 81 4.4.2 R4. Regression Trees - Video 1: Boston Housing Data 82 4.4.3 R4. Regression Trees- Video 2: The Data 83 4.4.4 R4. Regression Trees - Video 3: Geographical Predictions 84 4.4.5 R4. Regression Trees - Video 4: Regression Trees 85 4.4.6 R4. Regression Trees - Video 5: Putting it all Together 86 4.4.7 R4. Regression Trees - Video 6: The CP Parameter 87 4.4.8 R4. Regression Trees - Video 7: Cross-Validation 88 5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics 89 5.2.1 An Introduction to Text Analytics - Video 1: Twitter 90 5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics 91 5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset 92 5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words 93 5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R 94 5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R 95 5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment 96 5.2.14 An Introduction to Text Analytics - Video 8: Conclusion 97 5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson 98 5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy 99 5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools 100 5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2 101 5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4 102 5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results 103 5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom 104 5.4.2 R5. Predictive Coding - Video 1: The Story of Enron 105 5.4.3 R5. Predictive Coding - Video 2: The Data 106 5.4.4 R5. Predictive Coding - Video 3: Pre-Processing 107 5.4.5 R5. Predictive Coding - Video 4: Bag of Words 108 5.4.6 R5. Predictive Coding - Video 5: Building Models 109 5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model 110 5.4.8 R5. Predictive Coding - Video 7: The ROC Curve 111 5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today 112 6.1.1 Welcome to Unit 6 - An Introduction to Clustering 113 6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix 114 6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems 115 6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering 116 6.2.7 An Introduction to Clustering - Video 4: Computing Distances 117 6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering 118 6.2.11 An Introduction to Clustering - Video 6: Getting the Data 119 6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R 120 6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems 121 6.3.1 Predictive Diagnosis - Video 1: Heart Attacks 122 6.3.3 Predictive Diagnosis - Video 2: The Data 123 6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering 124 6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns 125 6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge 126 6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data 127 6.4.2 Recitation 6 - Video 1: Image Segmentation 128 6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels 129 6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering 130 6.4.6 R6. Segmenting Images - Video 4: MRI Image 131 6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering 132 6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors 133 6.4.9 R6. Segmenting Images - Video 7: Comparing Methods 134 7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization 135 7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations 136 7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO) 137 7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization? 138 7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot 139 7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot 140 7.3.1 Visualization for Law and Order - Video 1: Predictive Policing 141 7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time 142 7.3.5 Visualization for Law and Order - Video 3: A Line Plot 143 7.3.7 Visualization for Law and Order - Video 4: A Heatmap 144 7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map 145 7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States 146 7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge 147 7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization 148 7.4.2 R7. Visualization - Video 1: Introduction 149 7.4.3 R7. Visualization - Video 2: Pie Charts 150 7.4.4 R7. Visualization - Video 3: Bar Charts in R 151 7.4.5 R7. Visualization - Video 4: A Better Visualization 152 7.4.6 R7. Visualization - Video 5: World Maps in R 153 7.4.7 R7. Visualization - Video 6: Scales 154 7.4.8 R7. Visualization - Video 7: Using Line Charts Instead 155 8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization 156 8.2.1 An Introduction to Linear Optimization - Video 1: Introduction 157 8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight 158 8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation 159 8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem 160 8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem 161 8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis 162 8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights 163 8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management 164 8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy 165 8.3.3 Radiation Therapy - Video 2: An Optimization Problem 166 8.3.5 Radiation Therapy - Video 3: Solving the Problem 167 8.3.7 Radiation Therapy - Video 4: A Head and Neck Case 168 8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis 169 8.3.11 Radiation Therapy - Video 6: The Analytics Edge 170 8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising 171 8.4.2 R8. Google AdWords - Video 1: Introduction 172 8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works 173 8.4.4 R8. Google AdWords - Video 3: Prices and Queries 174 8.4.5 R8. Google AdWords - Video 4: Modeling the Problem 175 8.4.6 R8. Google AdWords - Video 5: Solving the Problem 176 8.4.7 R8. Google AdWords - Video 6: A Greedy Approach 177 8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis 178 8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge 179 9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization 180 9.2.1 Sports Scheduling - Video 1: Introduction 181 9.2.3 Sports Scheduling - Video 2: The Optimization Problem 182 9.2.5 Sports Scheduling - Video 3: Solving the Problem 183 9.2.7 Sports Scheduling - Video 4: Logical Constraints 184 9.2.9 Sports Scheduling - Video 5: The Edge 185 9.3.1 eHarmony - Video 1: The Goal of eHarmony 186 9.3.3 eHarmony - Video 2: Using Integer Optimization 187 9.3.5 eHarmony - Video 3: Predicting Compatibility Scores 188 9.3.7 eHarmony - Video 4: The Analytics Edge 189 9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly 190 9.4.2 R9. Operating Room Scheduling - Video 1: The Problem 191 9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model 192 9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem 193 9.4.5 R9. Operating Room Scheduling - Video 4: The Solution