Data Mining

Course Code:

ACM 476

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

6

Course Language:

İngilizce

Course Objectives:

Fundamentals of data mining, data, information and knowledge, knowledge discovery in databases, the traditional statistical methods, neural networks, decision trees, Bayesian theorem, association rules, data warehouses, business applications, and advanced techniques to know and understand

Course Content:

The course provides an overview of leading data mining methods and applications. The topics covered include: data, information and knowledge, knowledge discovery in databases, traditional statistics, artificial neural networks, decision trees, Bayesian learning, association rules, data warehousing, commercial tools, feature selection and advanced techniques.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Discussion, 4: Lab Work

Course Evaluation Methods:

A: Testing, B: Presentation C: Homework D: Project E: Laboratory

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods		Assessment Methods
Have a good knowledge about the concept of data mining.	7,8		1,2,3		A,B,C
What is data mining models and techniques to learn.	7,8		1,2,3		A,B,C
Implements descriptive statistical techniques on statistical a package program.	7,8		1,4		A,E
Knows about forecast models.	7,8		1,4		A,E
Knows about classication analysis.	7,8		1,4		A,E
Knows about association rules.	7,8		1,4		A,E
Have a good knowledge about web mining.	7,8		1, 4		A,C,E

Course Flow

Learning Outcomes	Program Learning Outcomes	Teaching Methods		Assessment Methods
Have a good knowledge about the concept of data mining.	7,8		1,2,3		A,B,C
What is data mining models and techniques to learn.	7,8		1,2,3		A,B,C
Implements descriptive statistical techniques on statistical a package program.	7,8		1,4		A,E
Knows about forecast models.	7,8		1,4		A,E
Knows about classication analysis.	7,8		1,4		A,E
Knows about association rules.	7,8		1,4		A,E
Have a good knowledge about web mining.	7,8		1, 4		A,C,E

Recommended Sources

RECOMMENDED SOURCES
Textbook	DATA MINING Concepts and Techniques, Jiawei HAN- Micheline KAMBER, Morgan Kaufman Pub.,2001
Additional Resources	DATABASE SYSTEMS, Thomas CONNOLLY-Carolyn BEGG, Pearson Education, 4. Edition

Material Sharing

MATERIAL SHARING
Documents
Assignments
Exams

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-term	1	70
Project	1	20
Homework	1	10
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	15	3	45
Hours for off-the-classroom study (Pre-study, practice)	15	3	45
Mid-term	1	9	9
Project	1	9	9
Homework	3	6	18
Presentation	1	3	3
Final examination	1	9	9
Total Work Load			138
Total Work Load / 25 (h)			5.52
ECTS Credit of the Course			6

None

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