Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
4	BENG546	DATA MINING	3+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	BIOENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Explaining the basic concepts of data mining Showing how to use data mining software for solving practical problems
Course Content	The course presents an introduction for life sciences students to popular data mining approaches. The key processes in data mining will be covered: types of attributes, common data set structures, data preprocessing (cleaning, transformation and reduction), feature selection, sampling, using different statistical and machine learning techniques (supervised and unsupervised methods) and visualization (histogram, box-plot, ROC).
Course Methods and Techniques	Ders anlatımı öğretim üyesi tarafından yapılacaktır. Konular, sunumlar eşliğinde aktarılacak, ders sırasında karşılıklı soru-cevap, tartışma ve örnek analiz yöntemleriyle öğrenci katılımı sağlanacaktır. Öğrenciler, verilen makaleler ve ders materyalleri üzerinden bireysel okuma ve analiz yapacaklardır.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Associate Prof.Dr. DUYGU SAÇAR DEMİRCİ duygu.sacar@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Introduction to Data Mining
Course Notes	To Be Announced weekly via Canvas

Course Category

Mathematics and Basic Sciences

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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
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	1	% 20
Sunum/Seminer	2	% 40
Final examination	1	% 40
Total	4	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
F2F Dersi	14	3	42
Sunum için Hazırlık	2	15	30
Sunum	2	2	4
Proje	1	20	20
Okuma	10	4	40
Araştırma	14	5	70
Ders dışı çalışma	10	1	10
Final Sınavı	1	15	15
Total Work Load			Number of ECTS Credits 7,5 231

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

No	Learning Outcomes
1	Describe the types, quality and influence of data.
2	Describe preprocessing and feature selection methods.
3	Explain visualization techniques.
4	Design a data mining workflow to solve a real problem.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to Data Mining
2	Data
3	Preprocessing
4	Feature Selection
5	Classification I
6	Classification II
7	Fall/Spring Break
8	Clustering I
9	Clustering II
10	Presentations
11	Performance Evaluation
12	Visualization
13	Anomaly Detection
14	Mining Real Data
15	Project presentations
16	Final

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77950&lang=en