The Analytics Edge (Spring 2017)

Massachusetts Institute of Technology via MIT OpenCourseWare

Overview

Course Features

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

Course Description

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.

Syllabus

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.

Taught by

Prof. Dimitris Bertsimas

Reviews

5.0 rating, based on 2 Class Central reviews

Start your review of The Analytics Edge (Spring 2017)

Dev Badodiya

so good love the course to view it is very simple to understand and its very reliable to understand and love the course it is.
Thakur Subhangi

This course is very simple to understand, relevant experience. I got to learn from basic to advanced.