Information Package / Course Catalogue
Data Mining Applications
Course Code: MIS527
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 7
Objectives of the Course

To introduce our students to various data mining techniques and to inform them about the applications of real life problems.

Course Content

Introduction to Data Mining, Data Mining Definitions, Data Mining Techniques, Data Mining Techniques, Operations and Algorithms, Data Mining Applications, Data Mining Problems, Text Mining, Web Mining, Sample Applications.

Name of Lecturer(s)
Learning Outcomes
1.Ability to reach the knowledge expansion and depth by doing scientific research in the field of engineering, knowledge evaluation, interpretation and application skills
2.Ability to complete and apply knowledge using scientific methods using limited or incomplete data; the ability to integrate knowledge of different disciplines
3.Developing methods to solve and solve engineering problems and applying innovative methods in solutions
4.Ability to develop new and original ideas and methods; the ability to develop innovative solutions in system, component or process design
5.Comprehensive information on modern techniques and methods applied in engineering and their boundaries
6.Ability to design and apply analytical, modeling and experimental based research; the ability to analyze and interpret complex situations in this process
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
General definitions
Week 2 - Theoretical
Data Mining Application Areas and Examples
Week 3 - Theoretical
Data Warehouses and OLAP
Week 4 - Theoretical
Data Mining Models
Week 5 - Theoretical
Classification-Decision Trees
Week 6 - Theoretical
Classification-Statistical Algorithms
Week 7 - Theoretical
Classification-based algorithms
Week 8 - Intermediate Exam
Midterm
Week 9 - Theoretical
Classification-Artificial Neural Networks
Week 10 - Theoretical
Association Rules and Relationship Analysis
Week 11 - Theoretical
Clustering-Hierarchical Methods
Week 12 - Theoretical
Partitioning Methods
Week 13 - Theoretical
Density Based Algorithms Grid Based Algorithms
Week 14 - Theoretical
Web mining
Week 15 - Final Exam
Final
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory163396
Project201020
Individual Work160348
Midterm Examination15510
Final Examination1156
TOTAL WORKLOAD (hours)180
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
4
4
4
4
4
4
4
4
4
OÇ-2
4
5
5
5
4
4
4
4
OÇ-3
4
4
4
4
5
5
5
4
3
OÇ-4
4
4
4
4
4
4
4
4
OÇ-5
5
5
5
5
4
4
5
5
4
OÇ-6
4
5
5
5
5
5
5
4
4
Adnan Menderes University - Information Package / Course Catalogue