1	Course Title:	DATA MINING
2	Course Code:	BLPS2313
3	Type of Course:	Optional
4	Level of Course:	Short Cycle
5	Year of Study:	2
6	Semester:	3
7	ECTS Credits Allocated:	3
8	Theoretical (hour/week):	2
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Öğr. Gör. AYŞE BAŞTUĞ KOÇ
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	aysebastugkoc@uludag.edu.tr, +902242942677, Bursa Uludağ Üniversitesi Gemlik Asım Kocabıyık MYO Bilgisayar Programcılığı-Gemlik/Bursa
18	Website:
19	Objective of the Course:	It is to introduce various data mining techniques to our students and to give information about their applications in real life problems.
20	Contribution of the Course to Professional Development	Thanks to this course, data belonging to real-life problems will be interpreted with data mining methods and will help process improvements.

21

Learning Outcomes:

1 Gain knowledge and skills to learn and apply the basic concepts, techniques, and tools of Data Mining.; 2 Learns data preprocessing-(Data cleaning, merging) methods.; 3 Learns data reduction methods.; 4 It can make important unknown inferences from the dataset.; 5 Learns classification and clustering methods with supervised and unsupervised methods.; 6 Gains knowledge of association rules.; 7 Gains knowledge about Data Mining applications and can develop applications.;

22	Course Content:

Week	Theoretical	Practical
1	Introduction to Data Mining
2	Data Mining Concepts and Data Preprocessing Techniques
3	Data Reduction
4	Data Warehouses and Olap
5	Data Mining Process
6	Classification Methods
7	Classification Methods
8	An overview and Midterm
9	Regression Models
10	Clustering Methods
11	Association Rules
12	Current Technology and Tools Used in Data Mining
13	Text Mining and Web Mining
14	Data Mining Application Areas and Examples

23	Textbooks, References and/or Other Materials:	Data Mining – Concepts, Models, Methods and Algorithms, Mehmed Kantardzic, 2019. Silahtaroğlu,G., Veri Madenciliği, Papatya Yayınevi,2008. Lecture Notes.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	A midterm and a final exam will be held to check the students' learning in the course.
Information

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ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	2	28
Practicals/Labs	0	0	0
Self Study and Preparation	14	2	28
Homeworks, Performances	0	2	28
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	3	3
Others	0	0	0
Final Exams	1	3	3
Total WorkLoad			90
Total workload/ 30 hr			3
ECTS Credit of the Course			3

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 LO1 4 4 2 3 5 5 5 1 1 1 1 LO2 4 5 2 4 5 5 5 1 1 1 1 LO3 3 4 2 5 3 3 3 1 1 1 1 LO4 4 5 2 4 5 4 2 1 1 1 1 LO5 3 4 2 5 5 3 3 1 1 1 1 LO6 3 3 2 4 4 3 3 1 1 1 1 LO7 4 5 3 4 5 3 3 1 1 1 1

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High