At the end of this course, the students; 1) Will have learned CRISP_DM, which is a standard for applications of data mining, and also how to apply various phases and methods of data mining. 2) Will learn how to use one of the software tools called WEKA.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
There is no recommended optional programme component for this course.
COURSE DEFINITION
Measurement and data, data analysis and uncertainty, models and patterns, searching and optimization, classification and regression, finding patterns and rules, applications and projects
COURSE CONTENTS
WEEK
TOPICS
1st Week
Introduction: What is data minig?
2nd Week
Some fundamental concepts
3rd Week
Some simple examples
4th Week
The CRISP_DM standard
5th Week
Data preprocessing
6th Week
Introduction to data analysis
7th Week
WEKA software
8th Week
MIDTERM
9th Week
Decision rules and decision trees
10th Week
Decision rules and decision trees
11th Week
Linear regression models
12th Week
Linear classification models
13th Week
Clustering methods
14th Week
Applications/Project presentations
RECOMENDED OR REQUIRED READING
Data Mining, I.H. Witten, E. Frank, Morgan Kaufmann, 2005.
Discovering Knowledge in Data: An Introduction to Data Mining, D.T. Larose, John Wİley & Sons, 2005.
Principles of Data Mining, D.J. Hand, H. Manila, P. Smith, MIT Press, 2001.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Lecture,Questions/Answers,Problem Solving,Other
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
30
Assignment
5
10
Project
1
10
Total(%)
50
Contribution of In-term Studies to Overall Grade(%)
50
Contribution of Final Examination to Overall Grade(%)
50
Total(%)
100
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
