INFORMATION TECHNOLOGIES (ENGLISH, NON-THESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5101	Data Mining	Fall	3	0	3	8

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. TEVFİK AYTEKİN
Course Lecturer(s):	Assist. Prof. TEVFİK AYTEKİN
Recommended Optional Program Components:	None
Course Objectives:	This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth

Learning Outcomes

The students who have succeeded in this course;
I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase.
II. Be proficient with leading data mining software, including WEKA
III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor
IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.
V. Understand the mathematical statistics foundations of the algorithms outlined above
VI. Evaluate current research and advanced topics in data mining.

Course Content

1. Frequent Item Set Detection
2. Association Rule Mining
3. Clustering
4. Classification

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Data Mining	None
2)	Frequent Item Set Mining	None
3)	Various frequent item set algorithms: Apriori, FPGrowth	None
4)	Association Rule Mining	None
5)	Classification	None
6)	Bayesian classification	None
7)	Midterm Exam 1	Review all the topics
8)	Rule based classification	None
9)	Cluster Analysis	None
10)	k-means	None
11)	k-medoids	None
12)	Hierarchical clustering	None
13)	Cluster quality	None
14)	Combining Multiple Clusterings	None

Sources

Course Notes / Textbooks:	Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	5	% 10
Midterms	1	% 40
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	14	56
Project	16	48
Midterms	3	15
Final	7	35
Total Workload		196

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Uses basic Software Engineering knowledge and competencies.
2)	Applies the software development ability that is necessary for software engineering applications.
3)	Uses data structures and applies information about algorithm development.
4)	Develops system programs on operating systems.
5)	Defines computer organization, design and architectures.
6)	Creates the structure of computer networks and network security.
7)	Uses business intelligence, data mining and data analysis tools, applies techniques about them.
8)	Develops database applications and WEB based programs.
9)	Defines, analyzes, designs and manages information technologies projects.
10)	Uses and develops technology-based environments and tools in education.
11)	Detects, identifies and solves information technology needs of the business environment.
12)	Uses the capabilities of information technologies within the rules of professional responsibility and ethics.