YZM5550 Business IntelligenceBahçeşehir UniversityDegree Programs INFORMATION TECHNOLOGIES (TURKISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
INFORMATION TECHNOLOGIES (TURKISH, NONTHESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
YZM5550 Business Intelligence Fall
Spring
3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: Turkish
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
Recommended Optional Program Components: None.
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.

Learning Outcomes

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

Course Content

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).

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks: Will be given weekly.
References: Yok - None.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 5
Homework Assignments 5 % 15
Project 3 % 20
Midterms 1 % 20
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Application 14 3 42
Study Hours Out of Class 14 3 42
Midterms 1 22 22
Final 1 41 41
Total Workload 189

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution