Course Schedule

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

Week 1

Tuesday (October 16): 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
• Data, data everywhere, The Data deluge, The Economist, 2/10.
• 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:
  • Big Data: The Management Revolution, HBR, 10/12.
  • 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.
  • Competing with IT: The Role of Shared IT-Business Understanding, CACM, 2007.

Resources:

• Online Data Mining Portals: KDnuggets, The Data Mine

Thursday (October 18): 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. Principles of Query Formulation
6. Database Definition and Manipulation in MS ACCESS

Readings:

• TB: Chapter 2
• Skim: CRISP-DM Process Model

Resources:

• MS Access 2010 Tutorials
• MS Access 2007 Quick (Interface) Tutorial: 1, 2.
• Interactive SQL Tutorial

Assignments:

• Download and Complete the MS Access Lab Tutorial.
• Assignment 1 -- Data Manipulation Using Access (due Thursday, October 25)

Week 2

Tuesday (October 24): Data Warehousing, OLAP and Multidimensional Data Analysis

Topics (Session Slides: 1, 2):

1. The Case for Datawarehousing
2. Multidimensional Databases
3. On-Line Analytical Processing
4. Demo - Pivot Tables

Readings:

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

Related Links/Resources:

• TDWI (The Data Warehousing Institute)
• Video: Using Pivot Tables (in Access 2007)
• BI Resources, The BI Verdict, IW
• Main Vendors: Oracle (Hyperion) Enterprise Performance Management, IBM (Cognos) BI, Teradata

Thursday (October 25): Data Exploration

Topics (Session Slides):

1. Data Exploration
2. Review of Descriptive Statistics and Probability Concepts

Readings:

• Chapters 3 & 4
• Basic Probability Notes

Week 3

Tuesday (October 30): Association Rule Mining

Topics (Session Slides):

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

Readings:

• TB: Chapter 13

Related Links (FYI):

• The $1 Million Netflix Challenge
• Planet Money (Podcast): Can A Public Radio Shirt Be Cool?
• Collaborative Filtering (a related concept)
• When recommendations go bad: Walmart & the "Planet of the Apes" fiasco

Thursday (November 1): Association Rule Mining (Continued)

Topics:

1. Apriori Algorithm
2. Rule Evaluation
3. Sequential patterns
4. Mining for Association Rules using XLminer

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 2 -- Association Mining (due Tuesday, November 6)

Week 4

Tuesday (November 6): 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 14

Related Links (FYI):

• A Tutorial on Clustering Algorithms

Thursday (November 8): Cluster Analysis (Continued)

Topics:

1. Cluster Evaluation
2. Clustering using XLminer
3. Demo: Synthetic Dataset

Assignments:

• Assignment 3 -- Cluster Analysis (due Thursday, November 15)

Week 5

Tuesday (November 13): Midterm Exam

Thursday (November 15): Predictive Modeling - Classification and Regression Trees

Topics (Session Slides):

1. General Approach to Solving Classification Problems
2. Tree Induction

Readings:

• Textbook: Chapter 9

Related Links (FYI):

• Demo: Decision Trees for Fun and Profit

Other:

• Solution to Assignment 3 on Cluster Analysis: 14.2_Pharmaceuticals (Spreadsheet), 14.3_Cereals (Spreadsheet)

Week 6

Tuesday (November 20): Model Evaluation

Topics (Session Slides):

1. More on Classification
2. Overfitting and Underfitting
3. Accuracy & Recall
4. Classification (Confusion) Matrix
5. Building Decision Tree Models in XLMiner
6. Response Modeling (Handout)

Readings:

• Textbook: Chapter 5

Thursday (November 22): No Class - Happy Thanksgiving!

Week 7

Tuesday (November 27) and Thursday (November 29): Predictive Modeling Using Regression

Topics (Session Slides):

1. Response Modeling (ctd)
2. Review of OLS Regression
3. Variable Selection and Stepwise Regression
4. Logistic Regression
5. Model Evaluation and Interpretation

Readings:

• Chapters 6 & 10

Week 8

Tuesday (December 4): Predictive Modeling Using Neural Networks & Ensamble Methods

Topics (Session Slides: Neural Networks, Ensambles)

1. Introduction to Neural Networks
2. Neural Networks vs. Regression
3. Ensamble Methods

Readings:

• Chapter 11

Related Links (FYI):

• Introduction to Neural Networks
• Classification-based: Forecasting Fraudulent Financial Statements using Data Mining (IJCI 2006).

Tuesday (December 11 @4PM): Final Exam

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