Data Warehousing/Mining 1 Data Warehousing/Mining Comp 150DW Course Overview Instructor: Dan Hebert.
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Transcript of Data Warehousing/Mining 1 Data Warehousing/Mining Comp 150DW Course Overview Instructor: Dan Hebert.
Data Warehousing/Mining 1
Data Warehousing/Mining
Comp 150DWCourse Overview
Instructor: Dan Hebert
Data Warehousing/Mining 2
Comp 150
Thursday 6:50 - 9:50 PM Instructor - Mr. Dan Hebert
– email - [email protected]– Location - Halligan Hall, rm. 108
Data Warehousing/Mining 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
Data Warehousing/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
Data Warehousing/Mining 5
Required Textbook
Data Mining Concepts and Techniques– Jiawei Han & Micheline Kamber– Morgan Kaufmann Publishers; ISBN: 1-55860-489-8
Data Warehousing/Mining 6
Reading Schedule
Lecture Date Topic Reading: Text Chapter January 22 Introduction to Comp 150, Introduction 1 January 29 Data Warehouse and OLAP
Technology for Data Mining 2
February 5 Aggregation in SQL, Data Warehousing Introduction, Data Warehousing Design
Not In Book
February 19 President’s Day Schedule Shift– No Class
February 26
Data Warehouse Semantics Semistructured Data
Not In Book
March 4 Data Preprocessing 3 March 11 Data Mining Primitives, Languages,
and System Architectures – Midterm Review
4
March 18 Midterm Exam
Data Warehousing/Mining 7
Reading Schedule (continued)
Lecture Date Topic Reading: Text Chapter
April 1 Concept Description: Characterization and Comparison
5
April 8 Mining Association Rules in Large Databases
6
April 15 Classification and Prediction 7 April 14 Cluster Analysis 8 April 22 Mining Complex Types of Data 9 April 29 Applications and Trends in Data
Mining – Final Exam Review 10
May 6 Reading Period/Project Completion May 13 Final Exam
Data Warehousing/Mining 8
Grading
Homework 30% Project 10% Midterm 25% Final 35%
Data Warehousing/Mining 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
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Project
Develop a data warehouse and perform data mining on it using Postgres as the underlying datastore
Additional details provided as the course progresses
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Midterm & Final
Open book, open notes Opportunity during class for review
of material covered prior to midterm and final
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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
Data Warehousing/Mining 13
Course Homepage
Course web page will be available Lectures/homework assignments
will also be posted in my account– ~dhebert/comp150dw