ISTANBUL OKAN UNIVERSITY INFORMATION PACKAGE / COURSE CATALOGUE
| Information Systems (Master) (Without Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
General course introduction information
| Course Code: | EBIS517 | ||||||||
| Course Name: | Data Mining | ||||||||
| Course Semester: |
Fall Spring |
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| Course Credits: |
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| Language of instruction: | EN | ||||||||
| Course Requisites: | |||||||||
| Does the Course Require Work Experience?: | No | ||||||||
| Type of course: | Department Elective | ||||||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||||||
| Course Coordinator : | Dr.Öğr.Üyesi FERİDUN CEMAL ÖZÇAKIR | ||||||||
| Course Lecturer(s): | |||||||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | With this course, students will learn the information discovery processes in databases, data mining concept, methods and frequently used data mining algorithms and apply these algorithms in simple level. |
| Course Content: | Data; information and knowledge concepts; Introduction to data mining; Knowledge discovery in databases (KDD); Databases; OLTP; Data warehouses; Data cubes; OLAP; KDD- data select; KDD- data preprocessing (data cleaning – data transformation); Classification concepts (decision trees; ID3 and bayes algorithms; etc.); Cluster concepts (kmeans; k-medoids; dbscan algorithms; etc.); Association rules concepts (market basket; apriori algorithm; etc.); Case study with apriori algorithm. |
Learning Outcomes
The students who have succeeded in this course;
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Lesson Plan
| Week | Subject | Related Preparation |
| 1) | Data, information and information concepts | Projection, Computer |
| 2) | The concept of data mining and introduction to information discovery processes in databases | Projection, Computer, Resource Books |
| 3) | Databases, data warehouses, data models, OLTP and OLAP | Projection, Computer, Resource Books |
| 4) | Information discovery processes in databases: data selection and data preprocessing | Projection, Computer, Resource Books |
| 5) | Information discovery processes in databases: Data reduction | Projection, Computer, Resource Books |
| 6) | Data mining methods: Classification (Decision trees, Bayes, Naive Bayes) | Projection, Computer, Resource Books |
| 7) | Data mining methods: Classification (ID3) | Projection, Computer, Resource Books |
| 8) | Data mining methods: Clustering (AGNES, DIANA, K-Means, K-Medoids and DB-SCAN) | Projection, Computer, Resource Books |
| 9) | QUIZ | |
| 10) | Data mining methods: Association Rule (Support and Trust values) | Projection, Computer, Resource Books |
| 11) | Data mining methods: Association Rule (Market Basket) | Projection, Computer, Resource Books |
| 12) | Data mining methods: Association Rule (Apriori Algorithm) | Projection, Computer, Resource Books |
| 13) | Student Presentations (Data Mining Algorithms) | Projection, Computer, Resource Books |
| 14) | Student Presentations (Data Mining Algorithms) | Projection, Computer, Resource Books |
Sources
| Course Notes / Textbooks: | Veri Madenciliği Ders Notları - Feridun Özçakır Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 |
| References: | Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons - 2006 |
Course-Program Learning Outcome Relationship
| Learning Outcomes | 1 |
2 |
3 |
|---|---|---|---|
| Program Outcomes | |||
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |
Learning Activity and Teaching Methods
Assessment & Grading Methods and Criteria
| Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing) | |
| Homework | |
| Group project | |
| Presentation |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 42 | % 5 |
| Presentation | 1 | % 20 |
| Midterms | 1 | % 25 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
Workload and ECTS Credit Grading
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Presentations / Seminar | 1 | 10 | 10 |
| Homework Assignments | 5 | 2 | 10 |
| Quizzes | 3 | 1 | 3 |
| Midterms | 1 | 2 | 2 |
| Paper Submission | 1 | 4 | 4 |
| Final | 1 | 2 | 2 |
| Total Workload | 73 | ||