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1.4.5 R1. Understanding Food - Video 4: Creating Plots in R
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Classroom Contents
The Analytics Edge (Spring 2017)
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- 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