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MGS 8040: Data Mining Fall 2018

 

Thursdays 5:30 – 9:45 PM

Buckhead Center

(200 Tower Place) Room 413

CRN: 82339

 

            Date

Topic

Readings

Assignments Due

(Post to iCollege)

Overview / Understanding Data / Preliminary Analysis                                                                                        

Week 1: 8/16

CRISP-DM

Introduction – DM Overview

Regression Review 

Simple Regression

Shoe size data

Height - Multicollinearity

Data and Cows

Notes

Notes – Simple Regression

Notes – Multiple Regression

Exercise

Solution to Exercise

Does p-value matter?

Multicollinearity

Interaction Effect

1.   Regression Analysis

Regression Dataset

 

(due Sunday, 8/19 by 11:59 PM. To iCollege)

Week 2: 8/23

Understanding Credit Data –

Equifax / Experian / Trans Union

 

The Initial Client Meeting

 

Introduction to SAS

SAS Training at UCLA

Notes – Initial Client Meeting

Sample Design Exercise

Solution to Exercise

Data Cleaning

Notes – Basic SAS Analysis
The Little SAS Book

By Delwiche & Slaughter

Data1 subset in Excel

 2.  Application – Dep. Var, Outcome, Sample time frame

 

3. SAS assignment

Folder Instructions

 

(both due Sunday, 8/26) by 11:59 PM. Post to iCollege as separate Assignments).

Week 3: 8/30

5:30 to 6:30 PM: Guest Lecture

Ashley Bentley, Lexis Nexis

Statistical Modeling

 

Data Cleaning

Dummy Variable Definition

 

Class Handout

Solution to Handout

 

Data Warehouse introduction

Books by Edward Tufte.

Gallery of Data Visualization

WHO visualization

4. Crosstabs, Dummy decisions

(due Sunday 9/2 by 11:59 PM)

Week 4: 9/6

Test 1   (5:30 – 7:00 PM)

 

7:15 PM - Intro to Logistic Regression,

Discriminant Analysis

 

 

SAS Programs for dummies, Regression and Scoring

PROJECT Dataset Sources

 

1. Dr. Miller’s List

2. UCI Machine Learning

3. Kaggle

Predictive Modeling/Validation/Segmentation/Association

Week 5: 9/13

Validation – KS Test

 

7:15 - 9:45 PM: Guest Lecture:

Gregg Weldon, CAO,

Analytics IQ Inc.  

Logistic Reg, Classification Trees.

www.statsoft.com

 

Please print files below, bring to class (or bring laptops)

Logistic Regression

Logistic Reg SAS program

Classification Trees

5. Discrim, KS

(due 9/16 by 11:59 PM)

 

SAS Programs for Regression/Scoring

Week 6: 9/20

Effectiveness of models – A review of methods

 

Segmentation

   Cluster Analysis

   SPSS Output (Cluster)

Research Paper on Model Effectivenss

www.statsoft.com

 

www.statsoft.com

Factor Analysis

Clustering Paper

6. Clustering

(due Sunday, 9/23 by 11:59 PM - Do with your project team)

Week 7: 9/27

Test 2 (5:30 – 7:00 PM)

 

Association Techniques, Monitoring Reports, Review

 

Week 8: 10/4

Project Presentations – 5:30 to 7:30/7:45 PM

Guest Lecture: J. P. James, Libreum International

Conclusion

Project Reports Due [Guidelines]

Sample Final Project