Lecture 00 – Course Information
Muhammad Tariq Siddique
https://sites.google.com/site/mtsiddiquecs/dm
Contents
1
Introduction
2
Course Material
3
Schedule
4
Guidelines
Gentle Reminder
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About This Course
Course Code
CSC-480
Course Title
Data Mining
Credit Hours
3+0
Abbreviation
DM
Pre-requisite
SEN-351 Advanced Databases
Type of Course
Elective
Course
Description
This course will introduce the students to the basic concepts of data
mining and examine methods that have emerged from both fields of
statistics and artificial intelligence. The course will survey data mining
applications, techniques and models proven to be of value in
recognizing patterns and making predictions from a domain
perspective. Topics include decision trees, classification, association,
partitioning, clustering, and text mining. The course will provide handson experimentation of data mining algorithms using easy-to-use
software and online repositories.
Lecture (Time Table)
Day/Time
08:30 – 09:25
09:30 – 10:25
10:30 – 11:25
11:30 – 12:25
Monday
Tuesday
Wednesday
Thursday
Friday
CSC-480(T)
BS(CS)-7B
LB-3
CSC-480(T)
BS(CS)-7B
LB-3
12:30 – 01:25
Course Material
Textbooks
Jiawei Han and Micheline
Kamber, Data Mining:
Concepts and Techniques
Third Edition, Elsevier,
2012
Pang-Ning Tan, Michael
Steinbach, Vipin Kumar,
Introduction to Data mining,
Addison-Wesley, 2005
Course Material
Reference Books
Charu C. Agarwall, Data Mining:
The Textbook, Springer, 2015.
Markus Hofmann and Ralf
Klinkenberg, RapidMiner:
Data Mining Use Cases
and Business Analytics
Applications, CRC Press
Taylor & Francis Group,
2014
Course Assessment
Final Examination
50%
Midterm Examination
20%
Assignments
10%
Project
10%
Quizzes
10%
Total
100%
Course Roadmap
Weekly Course Schedule
Weeks
Topics
Week01
Introduction
Week02
Knowing your Data
Week03
Data Preprocessing – I
Week04
Data Preprocessing – II
Week05
Association Rules
Week06
Association Rules
Week07
Week08
Week09
MIDTERM EXAMINATION
Weekly Course Schedule
Weeks
Topics
Week10
Classification
Week11
Classification
Week12
Clustering
Week13
Clustering
Week14
Research in Data mining
Week15
Case Study
Week16
Project Presentation
Week17
Revision
Week18
FINAL EXAMINATION
Self-Regulation
Be Punctual to
Classes and Labs
Study and revise
the lectures and
practice the
tutorials
Progress Monitoring
•Attendance
•Marks/Grades (quizzes,
exams)
•Assignments submissions
Everything is Only Once