The Analytics Edge

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1.1.1 Welcome to Unit 1: An Introduction to Analytics

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1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge

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1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson

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1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony

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1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study

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1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye

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1.2.6 The Analytics Edge - Video 6: This Class

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1.3.2 Working with Data - Video 1: History of R

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1.3.4 Working with Data - Video 2: Getting Started in R

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1.3.6 Working with Data - Video 3: Vectors and Data Frames

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1.3.8 Working with Data - Video 4: Loading Data Files

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1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots

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1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables

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1.3.14 Working with Data - Video 7: Saving with Script Files

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1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data

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1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition

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1.4.3 R1. Understanding Food - Video 2: Working with Data in R

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1.4.4 R1. Understanding Food - Video 3: Data Analysis

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1.4.5 R1. Understanding Food - Video 4: Creating Plots in R

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1.4.6 R1. Understanding Food - Video 5: Adding Variables

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1.4.7 R1. Understanding Food - Video 6: Summary Tables

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2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression

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2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine

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2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression

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2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression

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2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R

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2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model

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2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity

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2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions

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2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts

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2.3.2 Sports Analytics - Video 1: The Story of Moneyball

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2.3.3 Sports Analytics - Video 2: Making It to the Playoffs

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2.3.5 Sports Analytics - Video 3: Predicting Runs

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2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions

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2.3.9 Sports Analytics - Video 5: Winning the World Series

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2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports

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2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2

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2.4.2 R2. Moneyball in the NBA - Video 1: The Data

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2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins

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2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored

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2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions

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3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression

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3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment

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3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset

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3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression

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3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R

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3.2.8 Introduction to Logistical Regression - Video 5: Thresholding

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3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves

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3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model

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3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge

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3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives

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3.3.3 The Framingham Heart Study - Video 2: Risk Factors

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3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model

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3.3.7 The Framingham Heart Study - Video 4: Validating the Model

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3.3.9 The Framingham Heart Study - Video 5: Interventions

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3.3.11 The Framingham Heart Study - Video 6: Overall Impact

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3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast

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3.4.2 R3. Election Forecasting - Video 1: Election Prediction

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3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data

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3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method

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3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models

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3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions

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4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees

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4.2.1 An Introduction to Trees - Video 1: The Supreme Court

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4.2.3 An Introduction to Trees - Video 2: CART

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4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions

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4.2.7 An Introduction to Trees - Video 4: CART in R

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4.2.9 An Introduction to Trees - Video 5: Random Forests

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4.2.11 An Introduction to Trees - Video 6: Cross-Validation

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4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts

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4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye

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4.3.3 Healthcare Costs - Video 2: Claims Data

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4.3.5 Healthcare Costs - Video 3: The Variables

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4.3.7 Healthcare Costs- Video 4: Error Measures

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4.3.9 Healthcare Costs - Video 5: CART to Predict Cost

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4.3.11 Healthcare Costs - Video 6: Claims Data in R

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4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix

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4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R

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4.3.17 Healthcare Costs - Video 9: Results

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4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data

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4.4.2 R4. Regression Trees - Video 1: Boston Housing Data

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4.4.3 R4. Regression Trees- Video 2: The Data

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4.4.4 R4. Regression Trees - Video 3: Geographical Predictions

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4.4.5 R4. Regression Trees - Video 4: Regression Trees

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4.4.6 R4. Regression Trees - Video 5: Putting it all Together

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4.4.7 R4. Regression Trees - Video 6: The CP Parameter

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4.4.8 R4. Regression Trees - Video 7: Cross-Validation

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5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics

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5.2.1 An Introduction to Text Analytics - Video 1: Twitter

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5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics

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5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset

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5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words

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5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R

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5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R

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5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment

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5.2.14 An Introduction to Text Analytics - Video 8: Conclusion

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5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson

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5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy

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5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools

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5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2

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5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4

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5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results

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5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom

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5.4.2 R5. Predictive Coding - Video 1: The Story of Enron

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5.4.3 R5. Predictive Coding - Video 2: The Data

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5.4.4 R5. Predictive Coding - Video 3: Pre-Processing

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5.4.5 R5. Predictive Coding - Video 4: Bag of Words

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5.4.6 R5. Predictive Coding - Video 5: Building Models

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5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model

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5.4.8 R5. Predictive Coding - Video 7: The ROC Curve

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5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today

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6.1.1 Welcome to Unit 6 - An Introduction to Clustering

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6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix

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6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems

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6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering

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6.2.7 An Introduction to Clustering - Video 4: Computing Distances

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6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering

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6.2.11 An Introduction to Clustering - Video 6: Getting the Data

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6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R

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6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems

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6.3.1 Predictive Diagnosis - Video 1: Heart Attacks

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6.3.3 Predictive Diagnosis - Video 2: The Data

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6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering

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6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns

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6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge

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6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data

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6.4.2 Recitation 6 - Video 1: Image Segmentation

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6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels

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6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering

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6.4.6 R6. Segmenting Images - Video 4: MRI Image

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6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering

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6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors

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6.4.9 R6. Segmenting Images - Video 7: Comparing Methods

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7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization

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7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations

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7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO)

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7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization?

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7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot

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7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot

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7.3.1 Visualization for Law and Order - Video 1: Predictive Policing

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7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time

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7.3.5 Visualization for Law and Order - Video 3: A Line Plot

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7.3.7 Visualization for Law and Order - Video 4: A Heatmap

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7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map

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7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States

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7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge

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7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization

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7.4.2 R7. Visualization - Video 1: Introduction

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7.4.3 R7. Visualization - Video 2: Pie Charts

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7.4.4 R7. Visualization - Video 3: Bar Charts in R

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7.4.5 R7. Visualization - Video 4: A Better Visualization

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7.4.6 R7. Visualization - Video 5: World Maps in R

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7.4.7 R7. Visualization - Video 6: Scales

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7.4.8 R7. Visualization - Video 7: Using Line Charts Instead

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8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization

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8.2.1 An Introduction to Linear Optimization - Video 1: Introduction

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8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight

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8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation

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8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem

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8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem

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8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis

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8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights

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8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management

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8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy

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8.3.3 Radiation Therapy - Video 2: An Optimization Problem

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8.3.5 Radiation Therapy - Video 3: Solving the Problem

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8.3.7 Radiation Therapy - Video 4: A Head and Neck Case

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8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis

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8.3.11 Radiation Therapy - Video 6: The Analytics Edge

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8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising

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8.4.2 R8. Google AdWords - Video 1: Introduction

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8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works

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8.4.4 R8. Google AdWords - Video 3: Prices and Queries

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8.4.5 R8. Google AdWords - Video 4: Modeling the Problem

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8.4.6 R8. Google AdWords - Video 5: Solving the Problem

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8.4.7 R8. Google AdWords - Video 6: A Greedy Approach

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8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis

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8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge

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9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization

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9.2.1 Sports Scheduling - Video 1: Introduction

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9.2.3 Sports Scheduling - Video 2: The Optimization Problem

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9.2.5 Sports Scheduling - Video 3: Solving the Problem

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9.2.7 Sports Scheduling - Video 4: Logical Constraints

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9.2.9 Sports Scheduling - Video 5: The Edge

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9.3.1 eHarmony - Video 1: The Goal of eHarmony

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9.3.3 eHarmony - Video 2: Using Integer Optimization

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9.3.5 eHarmony - Video 3: Predicting Compatibility Scores

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9.3.7 eHarmony - Video 4: The Analytics Edge

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9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly

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9.4.2 R9. Operating Room Scheduling - Video 1: The Problem

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9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model

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9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem

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9.4.5 R9. Operating Room Scheduling - Video 4: The Solution