1. Data Warehousing/Mining 1 Data Warehousing/Mining Comp 150DW Course Overview Instructor: Dan Hebert

2. Data Warehousing/Mining 2 Comp 150 v Thursday 6:50 - 9:50 PM v Instructor - Mr. Dan Hebert – email - dhebert@mitre.org – Location - Halligan Hall, rm. 108

3. Data Warehousing/Mining 3 Course Description v Fundamental concepts and techniques of data warehousing and data mining – concepts, principles, architecture, design, implementation, and application of data warehousing and data mining v Topics : Data warehousing and OLAP technology for data mining, data preprocessing, data mining primitives, languages and systems, descriptive data mining, both characterization and comparison, association analysis, classification and prediction, cluster analysis, mining complex types of data, and applications and trends in data mining

4. Data Warehousing/Mining 4 Course Prerequisite v Comp 115 – Introduction to RDBMS – Familiarity with programming with C/C++ is assumed v Students should be comfortable with: – relational model basics – relational algebra – SQL – Views – Security – conceptual database design and ER models – schema refinement and normal forms – physical database design and tuning

5. Data Warehousing/Mining 5 Required Textbook v Data Mining Concepts and Techniques –Jiawei Han & Micheline Kamber –Morgan Kaufmann Publishers; ISBN: 1-55860-489-8

6. Data Warehousing/Mining 6 Reading Schedule

7. Data Warehousing/Mining 7 Reading Schedule (continued)

8. Data Warehousing/Mining 8 Grading v Homework30% v Project10% v Midterm25% v Final35%

9. Data Warehousing/Mining 9 Homework v Assigned weekly (each Wednesday) – Due at the start of lecture the following Wednesday v Late policy: – Homework turned in up to one week after the due date - 20% penalty. – Homework turned in anytime later - 100% penalty v Typical homework assignment – Exercises from the text – “Hands-on” problems that involve building data warehouses and performing data mining u Working with PostgresQL

10. Data Warehousing/Mining 10 Project v Develop a data warehouse and perform data mining on it using Postgres as the underlying datastore v Additional details provided as the course progresses

11. Data Warehousing/Mining 11 Midterm & Final v Open book, open notes v Opportunity during class for review of material covered prior to midterm and final

12. Data Warehousing/Mining 12 Computing Environment v All students will have a computer account on psql.cs.tufts.edu – Account will work on all workstations in the SUN lab v Commercial RDBMS utilized will be PostgreSQL –For information - http://www.postgresql.org/index.html

13. Data Warehousing/Mining 13 Course Homepage v Course web page will be available v Lectures/homework assignments will also be posted in my account – ~dhebert/comp150dw