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