Iğdır University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
184403003103	DATA MINING	Elective	2	4	3

Level of Course Unit

Short Cycle

Objectives of the Course

It is aimed to make students gain abilities to obtain meaningful information applying data mining methods.

Name of Lecturer(s)

Learning Outcomes

1	Knows the basic terms of data mining.
2	Obtains meaningful information gathering sufficient data.
3	Uses a data mining software in a baseline level.

Mode of Delivery

Daytime Class

Prerequisites and co-requisities

None

Recommended Optional Programme Components

None

Course Contents

Data, information, knowledge and wisdom terms Data mining definition and methods Classifying Clustering Association Rules Analysis Text mining Application with MatLab

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	Introduction to data mining
2	Definition of data, information, knowledge and wisdom
3	Data preprocessing methods
4	Data preprocessing methods
5	Classifying
6	Classification methods
7	Clustering
8	Mid-term exam
9	Mid-term exam
10	Association Rules Analysis
11	Text mining
12	Wen mining
13	Data mining applications
14	Data mining applications

Recommended or Required Reading

Gökhan Silahtaroğlu, Veri Madenciliği. Yalçın Özkan, Veri Madenciliği Yöntemleri. Ayşe Oğuzlar, Temel Metin Madenciliği.

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Term (or Year) Learning Activities	Quantity	Weight
Midterm Examination	1	100
SUM		100
End Of Term (or Year) Learning Activities	Quantity	Weight
Final Examination	1	100
SUM		100
Term (or Year) Learning Activities		40
End Of Term (or Year) Learning Activities		60
SUM		100

Language of Instruction

Turkish

Work Placement(s)

None

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Midterm Examination	1	1	1
Final Examination	1	2	2
Attending Lectures	14	1	14
Practice	14	1	14
Self Study	14	2	28
Individual Study for Mid term Examination	1	10	10
Individual Study for Final Examination	1	20	20
TOTAL WORKLOAD (hours)			89

Contribution of Learning Outcomes to Programme Outcomes

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11
LO1	3
LO2			5				5
LO3			3		5		3				3

* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High