MATHEMATICS (TURKISH, PHD) | |||||
PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
YZM5550 | Business Intelligence | Fall | 3 | 0 | 3 | 12 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | Tr |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Prof. Dr. MEHMET ALPER TUNGA |
Course Lecturer(s): |
Dr. Öğr. Üyesi SERKAN AYVAZ |
Course Objectives: | Participants will learn the usage based on business intelligence, data mining, business intelligence methods will contribute, open-source and commercial develop business intelligence solutions, and application will be introduced. |
The students who have succeeded in this course; 1. Describe the concepts of business intelligence 2. Use the reporting tools 3. Define the contributions of data mining 4. Use basic ETL tools |
The content of this course is composed of introduction to business intelligence, database management systems, the data warehouse models and architectures - the application, data warehouses Datamarts, data mining - 0 (preprocessing), data mining - 1 (driven methodology and algorithms), data mining - 2 (Guided algorithms continued), data mining - 3 (non-guided algorithms). |
Week | Subject | Related Preparation | |
1) | Introduction to Business Intelligence | ||
2) | Database management systems – 1 | ||
3) | Database management systems – 2 | ||
4) | The data warehouse models and architectures - the application | ||
5) | Data warehouses Datamarts | ||
6) | Data Mining - 0 (preprocessing) | ||
7) | Data Mining - 0 (preprocessing) + Midterm Exam | ||
8) | Data Mining - 1 (driven methodology and algorithms) | ||
9) | Data Mining - 2 (Guided algorithms continued) | ||
10) | Data Mining - 3 (non-guided algorithms) | ||
11) | Project Presentations – 1 | ||
12) | Project Presentations – 2 | ||
13) | Project Presentations – 3 | ||
14) | Overall assessment and closing |
Course Notes: | Will be given weekly. |
References: | Yok - None. |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 14 | % 5 |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | % 0 | |
Homework Assignments | 5 | % 15 |
Presentation | % 0 | |
Project | 3 | % 20 |
Seminar | % 0 | |
Midterms | 1 | % 20 |
Preliminary Jury | % 0 | |
Final | 1 | % 40 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 14 | 3 | 42 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 14 | 3 | 42 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 0 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Quizzes | 0 | 0 | 0 |
Preliminary Jury | 0 | 0 | 0 |
Midterms | 1 | 22 | 22 |
Paper Submission | 0 | 0 | 0 |
Jury | 0 | 0 | 0 |
Final | 1 | 41 | 41 |
Total Workload | 189 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |