MUSIC TECHNOLOGIES | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
COP4408 | OBASE Business Intelligence | Spring | 3 | 0 | 3 | 5 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. ADEM KARAHOCA |
Course Lecturer(s): |
Prof. Dr. ADEM KARAHOCA |
Recommended Optional Program Components: | None |
Course Objectives: | The course provides the student with an introduction to the basic and more advanced concepts of Business Intelligence and discusses the architectures of possible solutions. |
The students who have succeeded in this course; 1. Define basic concepts and categories of business intelligence and business intelligence market 2. Describe data warehouse architectures 3. Define relational models, construct normalized data models and identify queries to data sources with SQL. 4. Discuss case studies in terms of business intelligence concepts 5. Specify data mining and clustering methods 6. Describe neural networks 7. Define decision trees 8. Identify business intelligence front end applications 9. Prepare project presentations |
Concepts of business intelligence, data warehousing, rdbms concepts, modeling the dimensions and creating the aggregations, panel - case studies, introduction to data mining unsupervised methods, supervised methods, business intelligence front end |
Week | Subject | Related Preparation |
1) | Introduction to Business Intelligence | |
2) | Data Warehousing | |
3) | RDBMS Concepts I | |
4) | RDBMS Concepts II | |
5) | Modeling the Dimensions and Creating the Aggregations | |
6) | Modeling the Dimensions and Creating the Aggregations | |
7) | Panel - Case Study: Migros | |
8) | Introduction to Data Mining Unsupervised Methods | |
9) | Introduction to Data Mining Unsupervised Methods | |
10) | Supervised Methods | |
11) | Supervised Methods | |
12) | Business Intelligence Front End | |
13) | Project Presentations | |
14) | Panel – Case Study: Turkcell |
Course Notes / Textbooks: | Corporate Information Factory, W. H. Inmon, Claudia Imhoff, Ryan Sousa, 2001, 0471399612 Business intelligence : a managerial approach, E. Turban, R. Sharda, J.E. Arnsson, D. King, 2007, 013234761X The Data Warehouse Toolkit, R. Kimball, M. Ross, 1996, 0471153370 |
References: | Yok |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 1 | % 10 |
Project | 1 | % 35 |
Midterms | 1 | % 15 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 25 | |
PERCENTAGE OF FINAL WORK | % 75 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 3 | 5 | 15 |
Project | 1 | 20 | 20 |
Midterms | 1 | 18 | 18 |
Final | 1 | 20 | 20 |
Total Workload | 115 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |