Data Warehousing/Mining
1
Data Warehousing/Mining
Comp 150DW
Course Overview
Instructor: Dan Hebert
Data Warehousing/Mining
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Comp 150
Thursday 6:50
-
9:50 PM
Instructor
-
Mr. Dan Hebert
–
email
-
dhebert@mitre.org
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Location
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Halligan Hall, rm. 108
Data Warehousing/Mining
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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
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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
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Required Textbook
Data Mining Concepts and Techniques
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Jiawei Han & Micheline Kamber
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Morgan Kaufmann Publishers; ISBN: 1
-
55860
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489
-
8
Data Warehousing/Mining
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Reading Schedule
Lecture Date
Topic
Reading: Text Chapter
Janu
ary 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 Semanti
cs
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
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Reading Schedule (continued)
Lecture Date
Topic
Reading: Text Chapter
April 1
Concept Description: Characterization
and C
omparison
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
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Grading
Homework
30%
Project
10%
Midterm
25%
Final
35%
Data Warehousing/Mining
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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
Data Warehousing/Mining
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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
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Course Homepage
Course web page will be available
Lectures/homework assignments will also
be posted in my account
–
~dhebert/comp150dw
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