University of Illinois at Chicago
College of Business Administration
Department of Information & Decision Sciences 
IDS 472    Statistics for IS and Data Mining  ("SISDM")
Semester    Spring, 2000 (Term #992)
Instructor    Prof. Stanley L. Sclove
Textbook    Weiss & Indurkhya,  Predictive DataMining  (PDM) 

Course Homepage
Organization and Administration
Syllabus
Course Calendar
Bibliography     (FYI only:   no readings assigned except the textbook)
Suggestions for IDS Majors


The  tasks  in the course are:
three homework assignments;  two exams;  and   a final project.
Homework A
 Due Mon., 31-Jan.
Homework B
Due Mon., 21-Feb.
Homework C
Due Wed., 5-Apr.       Solutions
Project
due by 12:30 p.m. on Wednesday, 3-May


Data

Alpha radial tires data     Excelworkbook
Assets-Sales-Profits       Excel workbook
Iris data            ASCII      Excel Workbook
Heart Data       Excelworkbook

Notes  (Three sections:   Background; Particular Topics;  Notes on the Chapters)

Notes:  Background
Review   of Business Statistics I-II
Notes onLinear Algebra



Notes:  Particular Topics
The lectures will focus on these topics:
StratifiedSampling
     Exercise:     Instructions StratifiedSampling        Spreadsheet
ClusterAnalysis
CART
LogisticRegression
Path   Analysis
Neural   Networks

Notes: on the textbook
Notation
Notes on the Chapters
You are to read steadily through the textbook, with the aid ofthese notes on the chapters, which are commentaries to the textbook.
Chapter 1:   What is Data Mining?
Notes       HTML    MSWord


Chapter2:   Statistical Evaluation for Big Data
Notes       HTML    MSWord


Chapter 3:  Preparing the Data
Notes    MSWord
Outlier Detection:     AnExample 
Chapter 4:   Data Reduction
Notes        HTMLMSWord
"Against All Odds: Inside Statistics" Program 10: Multidimensional Datawill be viewed.
NOVA: "We Know Where You Live" on geodemographic databases underlyingdirect-mail marketing will be viewed.
Notes   on "The Clustering of America"


Exam #1      Wed., 1-March
Preview
Solutions:    HTML MS Word


Addendum to Chapter 4: ClusterAnalysis

Chapter 5:   Looking for Solutions

Notes   Tree for irisdata
Chapter 6:   What's Best for Data Reductionand Mining ?     Notes


Chapter 7:   Art or Science? Case Studies inData Mining        Notes


Review Questions
Exam #2      Mon.,17-April        Solutions
Project
Due by 12:30 p.m., Wednesday, 3-May


Links
 MinitabWeb Resources Site
 Prof. Sclove's homepage
 IDS 470Homepage

Copyright   ©     1999            Stanley   Louis   Sclove
Created:    21 Oct 1999       Updated:  17 Apr 2000