ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

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.

Basic information

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.

Learning Outcomes

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

Course Content

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

Weekly Detailed Course Contents

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

Sources

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

Evaluation System

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

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Have sufficient background in mathematics, science and artificial intelligence engineering.
2)	Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions.
3)	Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose.
4)	Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction.
5)	Select and use modern techniques and tools necessary for engineering applications.
6)	Design and conduct experiments, collect data, and analyse and interpret results.
7)	Work effectively both as an individual and as a multi-disciplinary team member.
8)	Access information via conducting literature research, using databases and other resources
9)	Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning.
10)	Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field.
11)	Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level.
12)	Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age.
13)	Have a sense of professional and ethical responsibility.
14)	Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices.