MIT 15.071 The Analytics Edge, Spring 2017 (English) (Part 1)

   Tuesday, December 10, 2024   

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Lecture 1: 1.1.1 Welcome to Unit 1: An Introduction to Analytics

Lecture 2: 1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge

Lecture 3: 1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson

Lecture 4: 1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony

Lecture 5: 1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study

Lecture 6: 1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye

Lecture 7: 1.2.6 The Analytics Edge - Video 6: This Class

Lecture 8: 1.3.2 Working with Data - Video 1: History of R

Lecture 9: 1.3.4 Working with Data - Video 2: Getting Started in R

Lecture 10: 1.3.6 Working with Data - Video 3: Vectors and Data Frames

Lecture 11: 1.3.8 Working with Data - Video 4: Loading Data Files

Lecture 12: 1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots

Lecture 13: 1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables

Lecture 14: 1.3.14 Working with Data - Video 7: Saving with Script Files

Lecture 15: 1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data

Lecture 16: 1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition

Lecture 17: 1.4.3 R1. Understanding Food - Video 2: Working with Data in R

Lecture 18: 1.4.4 R1. Understanding Food - Video 3: Data Analysis

Lecture 19: 1.4.5 R1. Understanding Food - Video 4: Creating Plots in R

Lecture 20: 1.4.6 R1. Understanding Food - Video 5: Adding Variables

Lecture 21: 1.4.7 R1. Understanding Food - Video 6: Summary Tables

Lecture 22: 2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression

Lecture 23: 2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine

Lecture 24: 2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression

Lecture 25: 2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression

Lecture 26: 2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R

Lecture 27: 2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model

Lecture 28: 2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity

Lecture 29: 2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions

Lecture 30: 2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts

Lecture 31: 2.3.2 Sports Analytics - Video 1: The Story of Moneyball

Lecture 32: 2.3.3 Sports Analytics - Video 2: Making It to the Playoffs

Lecture 33: 2.3.5 Sports Analytics - Video 3: Predicting Runs

Lecture 34: 2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions

Lecture 35: 2.3.9 Sports Analytics - Video 5: Winning the World Series

Lecture 36: 2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports

Lecture 37: 2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2

Lecture 38: 2.4.2 R2. Moneyball in the NBA - Video 1: The Data

Lecture 39: 2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins

Lecture 40: 2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored

Lecture 41: 2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions

Lecture 42: 3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression

Lecture 43: 3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment

Lecture 44: 3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset

Lecture 45: 3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression

Lecture 46: 3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R

Lecture 47: 3.2.8 Introduction to Logistical Regression - Video 5: Thresholding

Lecture 48: 3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves

Lecture 49: 3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model

Lecture 50: 3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge

Lecture 51: 3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives

Lecture 52: 3.3.3 The Framingham Heart Study - Video 2: Risk Factors

Lecture 53: 3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model

Lecture 54: 3.3.7 The Framingham Heart Study - Video 4: Validating the Model

Lecture 55: 3.3.9 The Framingham Heart Study - Video 5: Interventions

Lecture 56: 3.3.11 The Framingham Heart Study - Video 6: Overall Impact

Lecture 57: 3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast

Lecture 58: 3.4.2 R3. Election Forecasting - Video 1: Election Prediction

Lecture 59: 3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data

Lecture 60: 3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method

Lecture 61: 3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models

Lecture 62: 3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions

Lecture 63: 4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees

Lecture 64: 4.2.1 An Introduction to Trees - Video 1: The Supreme Court

Lecture 65: 4.2.3 An Introduction to Trees - Video 2: CART

Lecture 66: 4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions