Course Name Code Semester T+U Hours Credit ECTS
Data Mining BSM 619 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Doctorate Degree
Course Type Optional
Course Coordinator Doç.Dr. NİLÜFER YURTAY
Course Lecturers
Course Assistants
Course Category
Course Objective It is aimed to teach data mining technics and thier 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, Group Study, Testing, Homework, Project / Design,
Week Course Topics Preliminary Preparation
1 General definitions
2 Data warehouses and OLAP
3 Data mining models
4 Classification -decision trees, Classification statistic algorithms
5 Classification distance algorithms, artificial neural networks
6 Association rules and relation analysis
7 Clustering- hierarchical methods
8 Partitioning methods, Density based algorithms
9 Grid based algorithms
10 Data mining in servis sector
11 Data mining in health sector
12 Data mining in production sector
13 Web mining
14 Data mining in education sector
Course Notes
Course Resources Silahtaroğlu,G., Veri Madenciliği, Papatya Yayınevi,2008
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study
2 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
3 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
4 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
5 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
6 ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process X
7 gain high level ability to define the required information and data X
8 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
9 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
11 aware of new and developing application of profession and ability to analyze and study on those applications X
12 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 50
1. Kısa Sınav 10
1. Ödev 30
2. Kısa Sınav 10
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 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Quiz 2 10 20
Assignment 1 10 10
Performance Task (Laboratory) 1 10 10
Final examination 1 10 10
Total Workload 156
Total Workload / 25 (Hours) 6.24
dersAKTSKredisi 6