Syllabus Schedule Project A Project B Home

 

MGS 8040: Data Mining Fall 2017

 

Thursdays 5:30 9:45 PM

Buckhead Center

(200 Tower Place) Room 413

CRN: 82481

 

Date

Topic

Readings

Assignments Due

(Post to iCollege)

Overview / Understanding Data / Preliminary Analysis

Week 1: 8/17

CRISP-DM

Introduction DM Overview

Regression Review

Simple Regression

Shoe size data

 

Data and Cows in India

Notes

Notes Simple Regression

Notes Multiple Regression

Exercise

Solution to Exercise

Does p-value matter?

Multicollinearity

Interaction Effect

Week 2: 8/24

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

1.   Regression Analysis

Regression Dataset

 

(due 8/24 by 11:59 PM. To iCollege)

Week 3: 8/31

5:30 to 6:30 PM: Guest Lecture

Paulette Rice

Director, Statistical Modeling

Lexis Nexis

 

Data Cleaning

Dummy Variable Definition

 

Class Handout

Solution to Handout

 

Data Warehouse introduction

Books by Edward Tufte.

Gallery of Data Visualization

WHO visualization

SAS Programs for dummies, Regression and Scoring

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

 

3. SAS assignment

Folder Instructions

 

(both due 8/31) by 11:59 PM. Post to iCollege as separate Assignments).

Week 4: 9/7

Test 1 (5:30 7:00 PM)

 

7:15 PM - Intro to Logistic Regression,

Discriminant Analysis

4. Crosstabs, Dummy decisions

(due Saturday 9/2) by 11:59 PM so you get feedback before the test)

Predictive Modeling/Validation/Segmentation/Association

Week 5: 9/14

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

 

Week 6: 9/21

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

5. Discrim, KS

(due 9/21 by 11:59 PM)

 

SAS Programs for Regression/Scoring

Week 7: 9/28

Test 2 (5:30 7:00 PM)

 

7:15 8:15 PM: Guest Lecture

Dr. Vic Uzumeri

DeeperPoint

Frontiers in Big Data

 

8:30 9:45 PM: Association Techniques, Monitoring Reports, Review

 

6. Clustering

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

Week 8: 10/4

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

 

Conclusion

Project Reports Due [Guidelines]

Sample Final Project