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Data Warehousing/Mining Comp 150DW Course Overview

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

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Data Warehousing/Mining Comp 150DW Course Overview

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  1. Data Warehousing/MiningComp 150DWCourse Overview Instructor: Dan Hebert

  2. Comp 150 • Thursday 6:50 - 9:50 PM • Instructor - Mr. Dan Hebert • email - dhebert@mitre.org • Location - Halligan Hall, rm. 108

  3. Course Description • Fundamental concepts and techniques of data warehousing and data mining • concepts, principles, architecture, design, implementation, and application of data warehousing and data mining • 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. Course Prerequisite • Comp 115 – Introduction to RDBMS • Familiarity with programming with C/C++ is assumed • 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. Required Textbook • Data Mining Concepts and Techniques • Jiawei Han & Micheline Kamber • Morgan Kaufmann Publishers; ISBN: 1-55860-489-8

  6. Reading Schedule

  7. Reading Schedule (continued)

  8. Grading • Homework 30% • Project 10% • Midterm 25% • Final 35%

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

  10. Project • Develop a data warehouse and perform data mining on it using Postgres as the underlying datastore • Additional details provided as the course progresses

  11. Midterm & Final • Open book, open notes • Opportunity during class for review of material covered prior to midterm and final

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

  13. Course Homepage • Course web page will be available • Lectures/homework assignments will also be posted in my account • ~dhebert/comp150dw

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