Is MIT 15.071 The Analytics Edge, Spring 2017 free?

Yes. This CourseHive course is available to learn online for free.

Does this course include individual lessons?

Yes. The course includes 193 lessons across 1 modules.

MIT 15.071 The Analytics Edge, Spring 2017

1.1.1 Welcome to Unit 1: An Introduction to Analytics 00:44

1.2.1 The Analytics Edge - Video 1: Introduction to The Analytics Edge 04:50

1.2.2 The Analytics Edge - Video 2: Example 1 - IBM Watson 06:38

1.2.3 The Analytics Edge - Video 3: Example 2 - eHarmony 01:51

1.2.4 The Analytics Edge - Video 4: Example 3 - The Framingham Heart Study 02:42

1.2.5 The Analytics Edge - Video 5: Example 4 - D2Hawkeye 02:03

1.2.6 The Analytics Edge - Video 6: This Class 01:14

1.3.2 Working with Data - Video 1: History of R 03:19

1.3.4 Working with Data - Video 2: Getting Started in R 08:09

1.3.6 Working with Data - Video 3: Vectors and Data Frames 09:29

1.3.8 Working with Data - Video 4: Loading Data Files 08:22

1.3.10 Working with Data - Video 5: Data Analysis - Summary Statistics and Scatterplots 07:56

1.3.12 Working with Data - Video 6: Data Analysis - Plots and Summary Tables 07:19

1.3.14 Working with Data - Video 7: Saving with Script Files 02:46

1.4.1 Welcome to Recitation 1 - Understanding Food: Nutritional Education with Data 00:27

1.4.2 R1. Understanding Food - Video 1: The Importance of Food and Nutrition 02:18

1.4.3 R1. Understanding Food - Video 2: Working with Data in R 04:11

1.4.4 R1. Understanding Food - Video 3: Data Analysis 07:42

1.4.5 R1. Understanding Food - Video 4: Creating Plots in R 07:46

1.4.6 R1. Understanding Food - Video 5: Adding Variables 04:50

1.4.7 R1. Understanding Food - Video 6: Summary Tables 06:27

2.1.1 Welcome to Unit 2 - An Introduction to Linear Regression 00:45

2.2.1 An Introduction to Linear Regression - Video 1: Predicting the Quality of Wine 04:06

2.2.3 An Introduction to Linear Regression - Video 2: One-variable Linear Regression 08:47

2.2.5 An Introduction to Linear Regression - Video 3: Multiple Linear Regression 03:53

2.2.7 An Introduction to Linear Regression - Video 4: Linear Regression in R 08:27

2.2.9 An Introduction to Linear Regression - Video 5: Understanding the Model 12:32

2.2.11 An Introduction to Linear Regression - Video 6: Correlation and Multicollinearity 07:26

2.2.13 An Introduction to Linear Regression - Video 7: Making Predictions 06:17

2.2.15 An Introduction to Linear Regression - Video 8: Comparing the Model to the Experts 01:48

2.3.2 Sports Analytics - Video 1: The Story of Moneyball 07:30

2.3.3 Sports Analytics - Video 2: Making It to the Playoffs 07:25

2.3.5 Sports Analytics - Video 3: Predicting Runs 05:49

2.3.7 Sports Analytics - Video 4: Using the Model to Make Predictions 04:18

2.3.9 Sports Analytics - Video 5: Winning the World Series 01:56

2.3.11 Sports Analytics - Video 6: The Analytics Edge in Sports 04:36

2.4.1 R2. Playing Moneyball in the NBA - Welcome to Recitation 2 00:23

2.4.2 R2. Moneyball in the NBA - Video 1: The Data 04:22

2.4.3 R2. Moneyball in the NBA - Video 2: Playoffs and Wins 06:56

2.4.4 R2. Moneyball in the NBA - Video 3: Points Scored 10:16

2.4.5 R2. Moneyball in the NBA - Video 4: Making Predictions 03:34

3.1.1 Welcome to Unit 3: Modeling the Expert - An Introduction to Logistical Regression 00:38

3.2.1 Introduction to Logistical Regression - Video 1: Replicating Expert Assessment 04:04

3.2.2 Introduction to Logistical Regression - Video 2: Building the Dataset 03:41

3.2.4 Introduction to Logistical Regression - Video 3: Logistic Regression 04:03

3.2.6 Introduction to Logistical Regression - Video 4: Logistic Regression in R 12:00

3.2.8 Introduction to Logistical Regression - Video 5: Thresholding 08:03

3.2.10 Introduction to Logistical Regression - Video 6: ROC Curves 07:59

3.2.12 Introduction to Logistical Regression - Video 7: Interpreting the Model 08:33

3.2.14 Introduction to Logistical Regression - Video 8: The Analytics Edge 02:56

3.3.1 The Framingham Heart Study - Video 1: Evaluating Risk Factors to Save Lives 04:12

3.3.3 The Framingham Heart Study - Video 2: Risk Factors 04:08

3.3.5 The Framingham Heart Study - Video 3: A Logistical Regression Model 10:23

3.3.7 The Framingham Heart Study - Video 4: Validating the Model 02:45

3.3.9 The Framingham Heart Study - Video 5: Interventions 01:45

3.3.11 The Framingham Heart Study - Video 6: Overall Impact 04:06

3.4.1 Recitation 3 - Election Forecasting: Predicting the Winner Before Any Votes Are Cast 00:25

3.4.2 R3. Election Forecasting - Video 1: Election Prediction 04:41

3.4.3 R3. Election Forecasting - Video 2: Dealing with Missing Data 07:14

3.4.4 R3. Election Forecasting - Video 3: A Sophisticated Baseline Method 05:46

3.4.5 R3. Election Forecasting - Video 4: Logistic Regression Models 08:00

3.4.6 R3. Election Forecasting - Video 5: Test Set Predictions 04:25

4.1.1 Welcome to Unit 4 - Judge, Jury, and Classifier: An Introduction to Trees 00:46

4.2.1 An Introduction to Trees - Video 1: The Supreme Court 05:52

4.2.3 An Introduction to Trees - Video 2: CART 07:03

4.2.5 An Introduction to Trees - Video 3: Splitting and Predictions 02:40

4.2.7 An Introduction to Trees - Video 4: CART in R 12:08

4.2.9 An Introduction to Trees - Video 5: Random Forests 07:41

4.2.11 An Introduction to Trees - Video 6: Cross-Validation 10:47

4.2.13 An Introduction to Trees - Video 7: The Model v. The Experts 05:36

4.3.1 Healthcare Costs - Video 1: The Story of D2Hawkeye 04:04

4.3.3 Healthcare Costs - Video 2: Claims Data 04:06

4.3.5 Healthcare Costs - Video 3: The Variables 05:22

4.3.7 Healthcare Costs- Video 4: Error Measures 02:21

4.3.9 Healthcare Costs - Video 5: CART to Predict Cost 03:56

4.3.11 Healthcare Costs - Video 6: Claims Data in R 05:46

4.3.13 Healthcare Costs - Video 7: Baseline Method and Penalty Matrix 05:20

4.3.15 Healthcare Costs - Video 8: Predicting Healthcare Cost in R 09:27

4.3.17 Healthcare Costs - Video 9: Results 03:05

4.4.1 Welcome to Recitation 4 - Location, Location, Location: Regression Trees for Housing Data 00:20

4.4.2 R4. Regression Trees - Video 1: Boston Housing Data 04:46

4.4.3 R4. Regression Trees- Video 2: The Data 09:26

4.4.4 R4. Regression Trees - Video 3: Geographical Predictions 05:22

4.4.5 R4. Regression Trees - Video 4: Regression Trees 06:36

4.4.6 R4. Regression Trees - Video 5: Putting it all Together 06:37

4.4.7 R4. Regression Trees - Video 6: The CP Parameter 03:18

4.4.8 R4. Regression Trees - Video 7: Cross-Validation 07:01

5.1.1 Welcome to Unit 5 - Turning Tweets into Knowledge: An Introduction to Text Analytics 00:38

5.2.1 An Introduction to Text Analytics - Video 1: Twitter 02:22

5.2.2 An Introduction to Text Analytics - Video 2: Text Analytics 02:37

5.2.4 An Introduction to Text Analytics - Video 3: Creating the Dataset 04:26

5.2.6 An Introduction to Text Analytics - Video 4: Bag of Words 06:18

5.2.8 An Introduction to Text Analytics - Video 5: Pre-Processing in R 07:59

5.2.10 An Introduction to Text Analytics - Video 6: Bag of Words in R 06:47

5.2.12 An Introduction to Text Analytics - Video 7: Predicting Sentiment 06:42

5.2.14 An Introduction to Text Analytics - Video 8: Conclusion 01:42

5.3.1 How IBM Built a Jeopardy Champion - Video 1: IBM Watson 05:30

5.3.3 How IBM Built a Jeopardy Champion - Video 2: The Game of Jeopardy 02:57

5.3.5 How IBM Built a Jeopardy Champion - Video 3: Watson's Database and Tools 04:24

5.3.7 How IBM Built a Jeopardy Champion - Video 4: How Watson Works - Steps 1 and 2 03:42

5.3.9 How IBM Built a Jeopardy Champion - Video 5: How Watson Works - Steps 3 and 4 05:43

5.3.11 How IBM Built a Jeopardy Champion - Video 6: The Results 08:33

5.4.1 Welcome to Recitation 5 - Predictive Coding: Bringing Text Analytics to the Courtroom 00:28

5.4.2 R5. Predictive Coding - Video 1: The Story of Enron 03:30

5.4.3 R5. Predictive Coding - Video 2: The Data 03:57

5.4.4 R5. Predictive Coding - Video 3: Pre-Processing 02:36

5.4.5 R5. Predictive Coding - Video 4: Bag of Words 02:28

5.4.6 R5. Predictive Coding - Video 5: Building Models 03:08

5.4.7 R5. Predictive Coding - Video 6: Evaluating the Model 04:10

5.4.8 R5. Predictive Coding - Video 7: The ROC Curve 03:20

5.4.9 R5. Predictive Coding - Video 8: Predictive Coding Today 00:49

6.1.1 Welcome to Unit 6 - An Introduction to Clustering 00:39

6.2.1 An Introduction to Clustering - Video 1: Introduction to Netflix 04:29

6.2.3 An Introduction to Clustering - Video 2: Recommendation Systems 04:47

6.2.5 An Introduction to Clustering - Video 3: Movie Data and Clustering 03:26

6.2.7 An Introduction to Clustering - Video 4: Computing Distances 06:19

6.2.9 An Introduction to Clustering - Video 5: Hierarchical Clustering 04:13

6.2.11 An Introduction to Clustering - Video 6: Getting the Data 06:47

6.2.13 An Introduction to Clustering - Video 7: Hierarchical Clustering in R 08:38

6.2.15 An Introduction to Clustering - Video 8: The Analytics Edge of Recommendation Systems 03:23

6.3.1 Predictive Diagnosis - Video 1: Heart Attacks 02:33

6.3.3 Predictive Diagnosis - Video 2: The Data 05:47

6.3.5 Predictive Diagnosis - Video 3: Predicting Heart Attacks Using Clustering 05:21

6.3.7 Predictive Diagnosis - Video 4: Understanding Cluster Patterns 03:23

6.3.9 Predictive Diagnosis - Video 5: The Analytics Edge 01:12

6.4.1 Welcome to Recitation 6 - Seeing the Big Picture: Segmenting Images to Create Data 00:28

6.4.2 Recitation 6 - Video 1: Image Segmentation 04:00

6.4.3 R6. Segmenting Images - Video 2: Clustering Pixels 07:42

6.4.4 R6. Segmenting Images - Video 3: Hierarchical Clustering 07:43

6.4.6 R6. Segmenting Images - Video 4: MRI Image 05:34

6.4.7 R6. Segmenting Images - Video 5: K-Means Clustering 06:36

6.4.8 R6. Segmenting Images - Video 6: Detecting Tumors 07:27

6.4.9 R6. Segmenting Images - Video 7: Comparing Methods 03:41

7.1.1 Welcome to Unit 7 - Visualizing the World: An Introduction to Visualization 00:46

7.2.1 An Introduction to Visualization - Video 1: The Power of Visualizations 03:17

7.2.3 An Introduction to Visualization - Video 2: The World Health Organization (WHO) 01:26

7.2.5 An Introduction to Visualization - Video 3: What is Data Visualization? 04:59

7.2.7 An Introduction to Visualization - Video 4: Basic Scatterplots Using ggplot 08:24

7.2.9 An Introduction to Visualization - Video 5: Advanced Scatterplots Using ggplot 07:14

7.3.1 Visualization for Law and Order - Video 1: Predictive Policing 04:23

7.3.3 Visualization for Law and Order - Video 2: Visualizing Crime Over Time 04:17

7.3.5 Visualization for Law and Order - Video 3: A Line Plot 08:08

7.3.7 Visualization for Law and Order - Video 4: A Heatmap 09:06

7.3.9 Visualization for Law and Order - Video 5: A Geographical Hot Spot Map 08:25

7.3.11 Visualization for Law and Order - Video 6: A Heatmap on the United States 09:26

7.3.13 Visualization for Law and Order - Video 7: The Analytics Edge 01:07

7.4.1 Welcome to Recitation 7 - The Good, the Bad, and the Ugly in Visualization 00:27

7.4.2 R7. Visualization - Video 1: Introduction 02:12

7.4.3 R7. Visualization - Video 2: Pie Charts 03:54

7.4.4 R7. Visualization - Video 3: Bar Charts in R 07:30

7.4.5 R7. Visualization - Video 4: A Better Visualization 01:36

7.4.6 R7. Visualization - Video 5: World Maps in R 11:38

7.4.7 R7. Visualization - Video 6: Scales 04:53

7.4.8 R7. Visualization - Video 7: Using Line Charts Instead 05:47

8.1.1 Welcome to Unit 8 - Airline Revenue Management: An Introduction to Linear Optimization 00:35

8.2.1 An Introduction to Linear Optimization - Video 1: Introduction 03:25

8.2.2 An Introduction to Linear Optimization - Video 2: A Single Flight 02:27

8.2.4 An Introduction to Linear Optimization - Video 3: The Problem Formulation 03:46

8.2.6 An Introduction to Linear Optimization - Video 4: Solving the Problem 06:40

8.2.8 An Introduction to Linear Optimization - Video 5: Visualizing the Problem 02:42

8.2.10 An Introduction to Linear Optimization - Video 6: Sensitivity Analysis 06:34

8.2.12 An Introduction to Linear Optimization - Video 7: Connecting Flights 08:18

8.2.14 An Introduction to Linear Optimization - Video 8: The Edge of Revenue Management 02:50

8.3.1 An Application of Linear Optimization - Video 1: Introduction to Radiation Therapy 05:30

8.3.3 Radiation Therapy - Video 2: An Optimization Problem 05:56

8.3.5 Radiation Therapy - Video 3: Solving the Problem 08:14

8.3.7 Radiation Therapy - Video 4: A Head and Neck Case 03:12

8.3.9 Radiation Therapy - Video 5: Sensitivity Analysis 03:51

8.3.11 Radiation Therapy - Video 6: The Analytics Edge 03:12

8.4.1 Welcome to Recitation 8 - Google AdWords: Optimizing Online Advertising 00:38

8.4.2 R8. Google AdWords - Video 1: Introduction 04:52

8.4.3 R8. Google AdWords - Video 2: How Online Advertising Works 05:05

8.4.4 R8. Google AdWords - Video 3: Prices and Queries 02:53

8.4.5 R8. Google AdWords - Video 4: Modeling the Problem 04:55

8.4.6 R8. Google AdWords - Video 5: Solving the Problem 10:26

8.4.7 R8. Google AdWords - Video 6: A Greedy Approach 10:59

8.4.8 R8. Google AdWords - Video 7: Sensitivity Analysis 06:25

8.4.9 R8. Google AdWords - Video 8: Extensions and the Edge 06:06

9.1.1 Welcome to Unit 9: An Introduction to Integer Optimization 00:41

9.2.1 Sports Scheduling - Video 1: Introduction 05:22

9.2.3 Sports Scheduling - Video 2: The Optimization Problem 08:18

9.2.5 Sports Scheduling - Video 3: Solving the Problem 07:52

9.2.7 Sports Scheduling - Video 4: Logical Constraints 05:30

9.2.9 Sports Scheduling - Video 5: The Edge 04:04

9.3.1 eHarmony - Video 1: The Goal of eHarmony 03:49

9.3.3 eHarmony - Video 2: Using Integer Optimization 03:31

9.3.5 eHarmony - Video 3: Predicting Compatibility Scores 03:45

9.3.7 eHarmony - Video 4: The Analytics Edge 01:24

9.4.1 Welcome to Recitation 9 - Operating Room Scheduling: Making Hospitals Run Smoothly 00:24

9.4.2 R9. Operating Room Scheduling - Video 1: The Problem 06:05

9.4.3 R9. Operating Room Scheduling - Video 2: An Optimization Model 04:24

9.4.4 R9. Operating Room Scheduling - Video 3: Solving the Problem 15:21

9.4.5 R9. Operating Room Scheduling - Video 4: The Solution 09:35

Welcome

MIT 15.071 The Analytics Edge, Spring 2017

What you'll learn

This course includes

Course content

MIT 15.071 The Analytics Edge, Spring 2017

Take this course with you

FAQs