Course Schedule

(Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9)

Week 1

Thursday (September 24): Course Introduction

Topics (Session Slides):

1. Getting acquainted
2. Course overview, goals, and administrivia
3. Introduction to Data Mining
4. Overview/goals of data mining (DM) and knowledge discovery (KD)
5. Myths about data mining

Readings:

• TB: Chapters 1 & 2
• A Golden Vein, The Economist, 1/04.
• Cases:
  • Diamonds in the Data Mine, HBR, 5/03
  • How Verizon cut Customer Churn, Financial Express, 10/03.
• Hard Hats for Data Miners: Myths and Pitfalls of Data Mining, DMReview, 5/05.
• Skim: Competing on Analytics, HBR, 1/06. Staring You in the Face: The path to new products might start with the customer data you've already collected, WSJ, 9/08.

Other:

• SAS Data Mining Certificate at Fisher
• Online Data Mining Portals: KDnuggets, The Data Mine

Week 2

Tuesday (September 29): The Data Mining Process - Data Extraction and Manipulation

Topics (Session Slides):

1. Overview of the Data Mining Process
2. The Relational Data Model and Relational DBMS
3. Enterprise Reporting
4. Relational Algebra
5. SQL: The Relational Query Language

Readings:

• TB: Chapter 2
• CRISP-DM Process Model

Resources:

• MS Access (2003) Primer
• MS Access 2007 Quick (Interface) Tutorial: 1, 2.
• Interactive SQL Tutorial

Thursday (October 1): Data Extraction and Manipulation

Topics:

1. Database Schemas and Instances
2. Principles of Query Formulation
3. Database Definition and Manipulation in MS ACCESS
4. Query formulation (Demo DB)

Readings:

• Skim through chapters 1-4 in the online access tutorial book.

Assignments:

• Download and Complete the MS Access Lab Tutorial.
• Assignment 1 -- Data Manipulation Using Access (due Tuesday, October 6)

Week 3

Tuesday (October 6): Data Warehousing

Topics (Session Slides):

1. The Case for Datawarehousing
2. Building a datawarehouse

Readings:

• H. Watson and B. Haley, "Datawarehousing: Managerial Considerations," Communications of the ACM, Vol. 41, No. 9 (Sept. 1998), Pages 32-37.

Related Links/Resources (FYI):

• The Data Warehousing Information Center

Thursday (October 8): OLAP and MDDB

Topics (Session Slides):

1. Multidimensional Databases
2. On-Line Analytical Processing
3. Demo - Pivot Tables

Readings:

• An Introduction to OLAP Multidimensional Terminology and Technology

Assignments:

• Assignment 2 -- Data Partitioning, Binning, and Summarization (due Thursday, October 15)

Related Links (FYI):

• The OLAP Report
• Video: Using Pivot Tables (in Access 2007)
• Oracle (Hyperion) Enterprise Performance Management

Week 4

Tuesday (October 13): Data Exploration

Topics (Session Slides):

1. Data Types
2. Data Summarization and Visualization
3. Measures of Association
4. Basic probability concepts

Readings:

• TB: Chapter 3
• Probability Notes

Thursday (October 15): Association & Market-Basked Analysis

Topics (Session Slides):

1. Market Basket Analysis and Other Applications
2. Frequent Itemset and Association Rule Mining
3. Rule Support & Confidence

Readings:

• TB: Chapter 11

Related Links (FYI):

• The $1 Million Netflix Challenge
• Collaborative Filtering (a related concept)
• When recommendations go bad: Walmart & the "Planet of the Apes" fiasco

Week 5

Tuesday (October 20): Association Rule Mining (Continued)

Topics:

1. Apriori Algorithm
2. Rule Evaluation
3. Sequential patterns

Readings:

• R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. 20th Int. Conf. Very Large Data Bases (VLDB), 1994. (FYI; only skim)

Assignments:

• Assignment 3 -- Association Mining (due Tuesday, October 27)

Thursday (October 22): Association Rule Mining (Continued)

Topics:

1. Mining for Association Rules using XLminer
2. Mining for Association Rules using SAS EM

Resources:

• Getting Started with SAS Enterprise Miner 5.3
• Data Mining Using SAS Enterprise Miner: A Case Study Approach (DMCS), Chapter 5

Week 6

Tuesday (October 27): Cluster Analysis

Topics (Session Slides):

1. Segmentation and Personalization
2. Similarity Measures
3. The K-means algorithm (Excel Spreadsheet Demo)
4. Hierarchical Clustering
5. Cluster Validation and Interpretation

Readings:

• TB: Chapter 12

Related Links (FYI):

• A Tutorial on Clustering Algorithms

Thursday (October 29): Cluster Analysis (Continued)

Topics:

1. Clustering using XLminer
2. Clustering using SAS EM

Resources:

• Data Mining Using SAS Enterprise Miner: A Case Study Approach (DMCS), Chapter 4

Assignments:

• Assignment 4 -- Cluster Analysis (due Thursday, November 5)

Week 7

Tuesday (November 3): Midterm Exam

Thursday (November 5): Classification

Topics (Session Slides):

1. General Approach to Solving Classification problems
2. Decision Tree Induction

Readings:

• Textbook: Chapter 7

Related Links (FYI):

• Demo: Decision Trees for Fun and Profit

Week 8

Tuesday (November 10): Model Evaluation

Topics (Session Slides):

1. Decision Trees (continued)
2. Accuracy measures
3. Lift Charts
4. Building Decision Tree Models in XLMiner

Other:

Solution to Assignment 4: 12.2_Pharmaceuticals (Spreadsheet), 12.3_Cereals (Spreadsheet)

Thursday (November 12): Model Evaluation (Continued)

Topics:

1. Lift Charts
2. Response Modeling

Week 9

Teusday (November 17): Predictive Modeling Using Regression

Topics (Session Slides):

1. Introduction to OLS Regressions
2. Simple and Multiple Regression
3. Variable Selection and Stepwise Regression

Readings:

• Chapter 5

Related Links (FYI):

• Regression with SAS

Thursday (November 19): Predictive Modeling Using Logistic Regression & Neural Networks

Topics (Session Slides: Logistic Regression, Neural Networks):

1. Logistic Regression
2. Introduction to Neural Networks
3. Neural Networks vs. Regression
4. Model Evaluation

Readings:

• Chapters 8 & 9

Related Links (FYI):

• Introduction to Neural Networks

Assignments:

• Assignment 5 -- Predictive Modeling (due Tuesday, December 1)

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