Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
6	BLM308	Data Mining	3+0+0	3	4

Course Details

Language of Instruction	Turkish
Level of Course Unit	Bachelor's Degree
Department / Program	Computer Engineering
Type of Program	Formal Education
Type of Course Unit	Compulsory
Course Delivery Method	Face To Face
Objectives of the Course	With this course, students will learn the information discovery processes in databases, data mining concept, methods and frequently used data mining algorithms and apply these algorithms in simple level.
Course Content	Data; information and knowledge concepts; Introduction to data mining; Knowledge discovery in databases (KDD); Databases; OLTP; Data warehouses; Data cubes; OLAP; KDD- data select; KDD- data preprocessing (data cleaning – data transformation); Classification concepts (decision trees; ID3 and bayes algorithms; etc.); Cluster concepts (kmeans; k-medoids; dbscan algorithms; etc.); Association rules concepts (market basket; apriori algorithm; etc.); Case study with apriori algorithm.
Course Methods and Techniques	1. Lecture, 2. Question and Answer, 3. Discussion and Brainstorming, 4. Research and Project Based Learning, 5. Presentation Preparation, 6. Group Work, 7. Problem Solving
Prerequisites and co-requisities	None
Course Coordinator	Asist Prof. Feridun C. ÖZÇAKIR feridun.ozcakir@gedik.edu.tr
Name of Lecturers	Asist Prof.Dr. Feridun C. ÖZÇAKIR feridun.ozcakir@gedik.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	I. Witten – E. Frank; Data Mining, Morgan Kaufmann Academic Press; 2000 Mohammed .J. Zaki, Wagner Meira; Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge Jiawei Han - Micheline Kamber - Jian Pei; Data Mining: Concepts and Techniques, 3rd Edition; Morgan Kaufmann - 2011
	Data Mining: Concepts and Techniques, 3rd Edition Jiawei Han, Micheline Kamber, Jian Pei - Morgan Kaufmann - 2011
	Vize ve Final sınavları

Course Category

Mathematics and Basic Sciences	%30
Engineering	%10
Engineering Design	%10
Social Sciences	%0
Education	%0
Science	%0
Health	%0
Field	%60

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

In-Term Studies	Quantity	Percentage
Mid-terms	1	% 25
Assignment	3	% 6
Attendance	1	% 5
Project	1	% 14
Final examination	1	% 50
Total	7	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Course Duration	14	3	42
Hours for off-the-c.r.stud	14	1,5	21
Assignments	3	0,5	1,5
Presentation	1	22	22
Mid-terms	1	2	2
Final examination	1	2	2
Total Work Load			Number of ECTS Credits 4 90,5

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Learning and explaining the basic concepts of Data Mining (Data, information, knowledge, knowledge discovery processes from databases).
2	Learning Data Mining Methods and Algorithms such as Classification, Clustering, Association Rule, etc. and being able to apply them on data.
3	Learning and applying a data mining package program.
4	Students will be able to research different data mining methods and algorithms, create data analysis models specific to the algorithms they are researching, and apply these algorithms on data.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Data, Databases, Data warehouses, Data models, OLTP and OLAP	Related References	Related References
2	E/R model, Relational model, Big data, New generation databases, Information and Knowledge concepts	Related References	Related References
3	Introduction to the concept of data mining and knowledge discovery in databases (KDD) processes. Data mining package applications (Knime, Anaconda - Orange, etc.)	Related References	Related References
4	Knowledge discovery in databases (KDD) processes: Data selection and Data preprocessing	Related References	Related References
5	Knowledge discovery in databases (KDD) processes in databases: Data reduction	Related References	Related References
6	Data mining methods: Classification (Decision trees, ID3)	Related References	Related References
7	Data mining methods: Classification (Bayesian, Naive Bayes)	Related Sources	Related Sources
8	Data mining methods: Clustering (AGNES, DIANA, K-Means, K-Medoids, DB-SCAN)
9	Data mining methods: Association-Rule (Support and Confidence values)	Related Sources	Related Sources
10	Data mining methods: Association-Rule (Market Basket)	Related References	Related References
11	Data mining methods: Association-Rule (Apriori Algorithm)	Related References	Related References
12	Student Presentations (Data Mining Algorithms: CART, CHAID, C4.5 C5)	Related References	Related References
13	Student Presentations (Data Mining Algorithms: SVM, RF-DT, BIRCH)	Related References	Related References
14	Student Presentations (Data Mining Algorithms: KNN, FP Growth, ECLAT)	Related References	Related References

Contribution of Learning Outcomes to Programme Outcomes

	P1	P4	P5	P11
C1	3
C2	3
C3		4
C4		3	3	2

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=243222&curProgID=5607&lang=en