Course Details

DATA MINING

Course Information

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

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
Prerequisites and co-requisities	None
Course Coordinator	Asist Prof.Dr. MÜŞERREF DUYGU SAÇAR DEMİRCİ duygu.sacar@agu.edu.tr
Name of Lecturers	None
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	Introduction to Data Mining Data Preprocessing Feature Selection Visualization Classification Clustering

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

Veri yok

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
F2F Dersi	1	3	3
Teslim İçin Hazırlık	1	10	10
Sunum	1	15	15
Proje	1	5	5
Okuma	1	3	3
Final Sınavı	1	22	22
Total Work Load			Number of ECTS Credits 2 58

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