Course Name Code Semester T+U Hours Credit ECTS
Data Mining ISE 302 6 2 + 1 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Course Coordinator Dr.Öğr.Üyesi TUĞRUL TAŞCI
Course Lecturers Dr.Öğr.Üyesi TUĞRUL TAŞCI,
Course Assistants
Course Category
Course Objective It is aimed to teach data mining technics and their applications in this course
Course Content Introduction to data minig, their definitions, backrground, operations, algorithms. Data mining applications and problems. Text minig, web mining. Examples.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Expose relations in data heaps automatically. Lecture, Question-Answer, Drilland Practice, Testing, Homework,
Week Course Topics Preliminary Preparation
1 General definitions.
2 Application areas of data mining
3 Data warehouses and OLAP
4 Data mining models
5 Classification -decision trees
6 Classification statistic algorithms
7 Classification distance algorithms
8 Classification artificial neural networks
9 Association rules and relation analysis
10 Clustering- hierarchical methods
11 Partitioning methods
12 Density based algorithms
13 Grid based algorithms
14 Web mining
Resources
Course Notes Lecture notes
Course Resources Silahtaroğlu,G., Veri Madenciliği, Papatya Yayınevi,2008
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X
9 X
10
11 X
12 X
Evaluation System
Semester Studies Contribution Rate
1. Proje / Tasarım 15
2. Proje / Tasarım 20
3. Proje / Tasarım 25
1. Ara Sınav 40
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 2 32
Hours for off-the-classroom study (Pre-study, practice) 16 4 64
Mid-terms 1 5 5
Quiz 1 3 3
Assignment 1 3 3
Performance Task (Application) 1 20 20
Final examination 1 5 5
Total Workload 132
Total Workload / 25 (Hours) 5.28
dersAKTSKredisi 5