EMTM 554: Data Mining Syllabus
Before Class
- Homework 1
- Make sure you have a copy of JMP from the Statistics
course. If you do not, or need a version for a macintosh
or have other problems, please contact the EMTM office so
they can have it ready for you on the first program
weekend.
Lecture 1: Overview of Data Mining
- What is Data Mining, and what is it used for?
- Examples, including your experience
- Data Warehousing
- Homework 1
- Readings
- Text: Chapt 1 Introduction
- Mastering data mining - Chapts 1 and 2 Data mining in context, Why master the art? (Berry and Linoff)
- The Long Tail (Chris Anderson)
- Text: Chapt 3 Data Warehouse and OLAP Technology
- Supplemental: Discovering Knowledge in Data - Chapt 1 (Larose)
- WebCafe:
introDBM.ppt, DataWarehousing.ppt
Lecture 2: Methods
- Visualization: PTDD
- Personalization: collaborative filtering
- Market segmentation: clustering
- Prediction: Decision trees and regression methods
- JMP for Data Mining (The good, the bad, and the ugly)
- Homework 2
- Readings
- Text: Chapter 6, sections 1,2,3,6,7,9,11 - Classification and Regression
- Text: Chapter 7.4.1 k-means - clustering
- Supplemental: Information Visualization in Data Mining and Knowledge Discovery
Chapt 2 (Color Plates are separate)
- Supplemental: Discovering Knowledge in Data (Larose) chapt 6 & 7 Decision trees, Neural networks
- WebCafe:
methods.ppt
Lecture 3: Evaluation
- Evaluation: prediction and pitfalls
- Gazelle.com: correlation and causality
- Homework 3
- Readings
- WebCafe:
evaluation.ppt, gazelle.ppt
Lecture 4: The DBM process & tools; CRM
- The DBM process, CRISP-DM
- DBM Tools and Industries, vertical and horizontal
- Homework 4
- Readings
- Mastering data Mining (Berry and Linoff) Chapt. 3 Data
Mining Methodology: The Virtuous Cycle Revisited
- Supplemental: Knowing What to Sell, When, and to Whom (Kumar, Venkatesan and Reinartz)
- Supplemental: The Dark Side of Customer Analytics (Davenport and Harris)
- Supplemental: Berry and Linhoff on churn
- WebCafe:
process.ppt, tools.ppt, costing.ppt
Lecture 5: Text mining
- Text mining: IR and IE, easy and hard
- Homework 5
- Readings
- Text: 10.4 Text Mining
- Text Mining (Weiss et al.) - Chapt 1
- Supplemental Text Mining (Weiss et al.) - Chapt 7
- Supplemental Text: 10.5 web mining
- WebCafe:
search.ppt, textmining.ppt
Lecture 6: Strategic Marketing; Summary and Quiz
- Strategic marketing (CapitalOne)
- Course summary
- special topics (by request)
- maybe: advanced methods: SVMs, belief nets
- future directions for KDD
- Project presentations
- Course evaluation
- Quiz
- Homework: final project
- Readings
- WebCafe:
strategy.ppt, summary.ppt
return home
ungar@cis.upenn.edu