The Analytics Edge | Sloan School of Management | MIT OpenCourseWare

Browse Course Material Syllabus Readings Lecture and Recitation Notes Assignments Unit Index Unit 1: An Introduction to Analytics 1.1 Welcome to Unit 1 1.2 The Analytics Edge: Intelligence, Happiness, and Health (Lecture Sequence) 1.3 Working with Data: An Introduction to R 1.4 Understanding Food: Nutritional Education with Data (Recitation) 1.5 Assignment 1 Unit 2: Linear Regression 2.1 Welcome to Unit 2 2.2 The Statistical Sommelier: An Introduction to Linear Regression 2.3 Moneyball: The Power of Sports Analytics 2.4 Playing Moneyball in the NBA (Recitation) 2.5 Assignment 2 Unit 3: Logistic Regression 3.1 Welcome to Unit 3 3.2 Modeling the Expert: An Introduction to Logistic Regression 3.3 The Framingham Heart Study: Evaluating Risk Factors to Save Lives 3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) 3.5 Assignment 3 Unit 4: Trees 4.1 Welcome to Unit 4 4.2 Judge, Jury, and Classifier: An Introduction to Trees 4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story 4.4 Location, Location, Location: Regression Trees for Housing Data (Recitation) 4.5 Assignment 4 Unit 5: Text Analytics 5.1 Welcome to Unit 5 5.2 Turning Tweets into Knowledge: An Introduction to Text Analytics 5.3 Man vs. Machine: How IBM Built a Jeopardy Champion 5.4 Predictive Coding: Bringing Text Analytics to the Courtroom (Recitation) 5.5 Assignment 5 Unit 6: Clustering 6.1 Welcome to Unit 6 6.2 Recommendations Worth a Million: An Introduction to Clustering 6.3 Predictive Diagnosis: Discovering Patterns for Disease Detection 6.4 Seeing the Big Picture: Segmenting Images to Create Data (Recitation) 6.5 Assignment 6 Unit 7: Visualization 7.1 Welcome to Unit 7 7.2 Visualizing the World: An Introduction to Visualization 7.3 The Analytical Policeman: Visualization for Law and Order 7.4 The Good, the Bad, and the Ugly: Visualization Recitation (Recitation) 7.5 Assignment 7 Unit 8: Linear Optimization 8.1 Welcome to Unit 8 8.2 Airline Revenue Management: An Introduction to Linear Optimization 8.3 Radiation Therapy: An Application of Linear Optimization 8.4 Google AdWords: Optimizing Online Advertising (Recitation) 8.5 Assignment 8 Unit 9: Integer Optimization 9.1 Welcome to Unit 9 9.2 Sports Scheduling: An Introduction to Integer Optimization 9.3 eHarmony: Maximizing the Probability of Love 9.4 Operating Room Scheduling: Making Hospitals Run Smoothly (Recitation) 9.5 Assignment 9 Course Info Instructor Prof. Dimitris Bertsimas Departments Sloan School of Management As Taught In Spring 2017 Level Graduate Course Info Instructor Prof. Dimitris Bertsimas Departments Sloan School of Management As Taught In Spring 2017 Level Graduate Topics Business Management Operations Management Mathematics Probability and Statistics Topics Business & Management Management Operations Science & Math Mathematics Learning Resource Types notes Lecture Notes theaters Lecture Videos assignment_turned_in Problem Set Solutions assignment Problem Sets Download Course menu search search Give Now About OCW Help & Faqs Contact Us search search GIVE NOW about ocw help & faqs contact us 15.071 | Spring 2017 | Graduate The Analytics Edge Menu Syllabus Readings Lecture and Recitation Notes Assignments Unit Index Unit 1: An Introduction to Analytics 1.1 Welcome to Unit 1 1.2 The Analytics Edge: Intelligence, Happiness, and Health (Lecture Sequence) 1.3 Working with Data: An Introduction to R 1.4 Understanding Food: Nutritional Education with Data (Recitation) 1.5 Assignment 1 Unit 2: Linear Regression 2.1 Welcome to Unit 2 2.2 The Statistical Sommelier: An Introduction to Linear Regression 2.3 Moneyball: The Power of Sports Analytics 2.4 Playing Moneyball in the NBA (Recitation) 2.5 Assignment 2 Unit 3: Logistic Regression 3.1 Welcome to Unit 3 3.2 Modeling the Expert: An Introduction to Logistic Regression 3.3 The Framingham Heart Study: Evaluating Risk Factors to Save Lives 3.4 Election Forecasting: Predicting the Winner Before any Votes are Cast (Recitation) 3.5 Assignment 3 Unit 4: Trees 4.1 Welcome to Unit 4 4.2 Judge, Jury, and Classifier: An Introduction to Trees 4.3 Keeping an Eye on Healthcare Costs: The D2Hawkeye Story 4.4 Location, Location, Location: Regression Trees for Housing Data (Recitation) 4.5 Assignment 4 Unit 5: Text Analytics 5.1 Welcome to Unit 5 5.2 Turning Tweets into Knowledge: An Introduction to Text Analytics 5.3 Man vs. Machine: How IBM Built a Jeopardy Champion 5.4 Predictive Coding: Bringing Text Analytics to the Courtroom (Recitation) 5.5 Assignment 5 Unit 6: Clustering 6.1 Welcome to Unit 6 6.2 Recommendations Worth a Million: An Introduction to Clustering 6.3 Predictive Diagnosis: Discovering Patterns for Disease Detection 6.4 Seeing the Big Picture: Segmenting Images to Create Data (Recitation) 6.5 Assignment 6 Unit 7: Visualization 7.1 Welcome to Unit 7 7.2 Visualizing the World: An Introduction to Visualization 7.3 The Analytical Policeman: Visualization for Law and Order 7.4 The Good, the Bad, and the Ugly: Visualization Recitation (Recitation) 7.5 Assignment 7 Unit 8: Linear Optimization 8.1 Welcome to Unit 8 8.2 Airline Revenue Management: An Introduction to Linear Optimization 8.3 Radiation Therapy: An Application of Linear Optimization 8.4 Google AdWords: Optimizing Online Advertising (Recitation) 8.5 Assignment 8 Unit 9: Integer Optimization 9.1 Welcome to Unit 9 9.2 Sports Scheduling: An Introduction to Integer Optimization 9.3 eHarmony: Maximizing the Probability of Love 9.4 Operating Room Scheduling: Making Hospitals Run Smoothly (Recitation) 9.5 Assignment 9 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 …Show more This course presents real-world examples in which quantitative methods prov