| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 7 | BENG448 | BIODATA MINING | 3+0+0 | 3 | 5 |
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Language of Instruction
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English
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Level of Course Unit
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Bachelor's Degree
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Department / Program
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BIOENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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Applying data mining approaches to extract data from major biological databases
Applying classification and clustering methods
Gaining experience on analyzing real biological data
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Course Content
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BioData Mining course is intended for students with little or no programming experience. Throughout the course, emphasis will be placed on practical applications in bioengineering, providing students with the skills to extract meaningful insights from diverse biological datasets. The basic principles of data mining will be introduced and through a course project, students will apply data mining approaches on a real problem.
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Course Methods and Techniques
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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Associate Prof.Dr. DUYGU SAÇAR DEMİRCİ
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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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
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In-Term Studies
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Yarıl yılSonu Sınavı/Dönem Projesinin Başarı Notuna Katkısı
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1
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%
40
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Sunum/Seminer
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1
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%
20
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Final examination
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1
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%
40
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Total
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3
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%
100
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ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
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F2F Dersi
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14
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3
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42
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Proje
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1
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30
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30
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Okuma
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10
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2
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20
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Araştırma
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12
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2
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24
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Kişisel Çalışma
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2
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14
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28
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Total Work Load
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Number of ECTS Credits 5
144
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction to Data Mining
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| 2 |
Data
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| 3 |
Introduction to Biological Databases
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| 4 |
Data Preprocessing Techniques
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| 5 |
Classification I
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| 6 |
Classification II
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| 7 |
Clustering Techniques I
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| 8 |
Clustering Techniques II
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| 9 |
Spring Break
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| 10 |
Feature Selection
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| 11 |
Visualization
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| 12 |
Anomaly Detection
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| 13 |
Practical Applications
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| 14 |
Student project presentations
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| 15 |
Student project presentations
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| 16 |
Final Exam
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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=76564&lang=en