Course Code	Semester	Course Name	LE/RC/LA	Course Type	Language of Instruction	ECTS
MB0038		Data Mining	2/2/0	DE	Turkish	5

Course Goals	The module aims to introduce the fundamental issues of data mining, to provide students with a broad undertanding of the algorithms developed to address different data mining tasks and apply them to real-world problems.
Prerequisite(s)	None
Corequisite(s)	None
Special Requisite(s)	-
Instructor(s)	Professor Ozan KOCADAĞLI
Course Assistant(s)	None
Schedule	Friday, 13.00-16.45
Office Hour(s)	Friday
Teaching Methods and Techniques	-Lectures, projects, readings, applications
Principle Sources	1. Garcia, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. New York: Springer. 2. Makhabel, B. (2015). Learning Data Mining with R. Birmimgham: Packt Publishing. 3. Torgo, L. (2017). Data Mining with R: Learning with Case Studies (2 b.). New York: Chapman and Hall/CRC. 4. Zhao, Y. (2014). Data Mining Applications with R. Amsterdam: Elsevier. 5. Github page
Other Sources	-

Course Schedules
Week	Contents	Learning Methods
1. Week	About this course	Oral Presentation, Handout
2. Week	Tools, Softwares, Programming Languages	Oral Presentation, Handout
3. Week	Tools, Softwares, Programming Languages	Oral Presentation, Handout
4. Week	Data Preprocessing: Normalization, Transformations, Missing Data Detection and Imputation	Oral Presentation, Handout
5. Week	Data Preprocessing: Discretization, Noise, Imbalanced Data, Sampling, Feature Selection	Oral Presentation, Handout
6. Week	Classification Algorithms	Oral Presentation, Handout
7. Week	Regression, Decision Trees	Oral Presentation, Handout
8. Week	Midterm Exam	Exam
9. Week	Custering Algorithms: Distance Metrics, Clustering Indexes, Visualization, K-means, Hierarchical Clustering	Oral Presentation, Handout
10. Week	Advanced Clustering	Oral Presentation, Handout
11. Week	Dimension Reduction, Model Selection	Oral Presentation, Handout
12. Week	Algorithms for Different Data Structures	Presentation
13. Week	Case Studies	Presentation
14. Week	Case Studies	Presentation
15. Week	Final Exam	Final Exam
16. Week	Final Exam	Final Exam
17. Week	Final Exam	Final Exam

Assessments
Evaluation tools	Quantity	Weight(%)
Midterm(s)	1	20
Project(s)	2	20
Final Exam	1	60

Program Outcomes PO-1 Interpreting advanced theoretical and applied knowledge in Mathematics and Computer Science. PO-2 Critiquing and evaluating data by implementing the acquired knowledge and skills in Mathematics and Computer Science. PO-3 Recognizing, describing, and analyzing problems in Mathematics and Computer Science; producing solution proposals based on research and evidence. PO-4 Understanding the operating logic of computer and recognizing computational-based thinking using mathematics as a discipline. PO-5 Collaborating as a team-member, as well as individually, to produce solutions to problems in Mathematics and Computer Science. PO-6 Communicating in a foreign language, and interpreting oral and written communicational abilities in Turkish. PO-7 Using time effectively in inventing solutions by implementing analytical thinking. PO-8 Understanding professional ethics and responsibilities. PO-9 Having the ability to behave independently, to take initiative, and to be creative. PO-10 Understanding the importance of lifelong learning and developing professional skills continuously. PO-11 Using professional knowledge for the benefit of the society.

Learning Outcomes LO-1 Students will gains the ability to learn and apply the basic knowledge of data mining. LO-2 Students will learn data preprocessing methods. LO-3 Students will know Data reduction methods. LO-4 Students will learn classification and clustering methods with supervised and unsupervised methods. LO-5 Student will have knowledge about association rules.

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11
LO 1
LO 2
LO 3
LO 4
LO 5