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MGS 8040: Data Mining Spring 2019:                       CRN 11475                Buckhead Room 548




Assignments Due

(Post to iCollege)

Overview / Understanding Data / Preliminary Analysis                                                                                       

Week 1: 3/5


Introduction – DM Overview

Regression Review 

Simple Regression

Shoe size data

Height - Multicollinearity

Data and Cows


Notes – Simple Regression

Notes – Multiple Regression


Solution to Exercise

Does p-value matter?


Interaction Effect

1.   Regression Analysis

Regression Dataset


(due Sunday, 3/10 by 11:59 PM. To iCollege)

Week 2: 3/12

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, 3/17) by 11:59 PM. Post to iCollege as separate Assignments).


Spring Break

Week 3: 3/26

5:30 to 6:30 PM: Lexis Nexis

Ashley Bentley, Birane Seck


Data Preparation

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 3/31 by 11:59 PM)

Week 4: 4/2

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: 4/9

Validation – KS Test


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

Gregg Weldon, CAO,

Analytics IQ Inc.  

Logistic Reg, Classification Trees.


Please print files below, bring to class Logistic Regression

Logistic Reg SAS program

Classification Trees

5. Discrim, KS

(due 4/14 by 11:59 PM)


SAS Programs for Regression/Scoring

Week 6: 4/16

Effectiveness of models – A review of methods



   Cluster Analysis

   SPSS Output (Cluster)

Research Paper on Model Effectivenss

Factor Analysis

Clustering Paper

Week 7: 4/23

Test 2 (5:30 – 7:00 PM)

Association Techniques, Monitoring Reports, Review


Week 8: 4/30

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

Guest Lecture: Rithika Gaddam, Leap Credit


Project Reports Due [Guidelines]

Sample Final Project