SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
70610MEEOS-CME0386	Knowledge Management Principles and Business Intelligence	1	Fall	3		3	6

Course Type :	Elective Course II
Cycle:	Master TQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Language of Instruction:	English
Prerequisities and Co-requisities:	N/A
Mode of Delivery:
Name of Coordinator:	Öğretim Görevlisi Dr. FARHAD PANAHIFAR
Dersin Öğretim Eleman(lar)ı:
Dersin Kategorisi:

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:
Course Content:

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
Skills *(Describe as Cognitive and/or Practical Skills.)*
Competences *(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)*

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)
Program Öğrenme Çıktıları (PÖÇ)
1) Complements and applies knowledge with scientific methods using uncertain, limited or incomplete data; use information from different disciplines together.
2) Has awareness of the new and developing applications of his/her profession, examines and learns them when needed.
3) Defines and formulates problems related to the field, develops new and/or original ideas and methods; develops and applies innovative methods to solve them.
4) Has the necessary skills and competencies to perform his/her profession in the most effective way and to constantly improve himself/herself.
5) Observes social, scientific and ethical values in the stages of data collection, interpretation, announcement and in all professional activities.
6) Has advanced theoretical and applied knowledge in the field of artificial intelligence, as well as comprehensive knowledge of current techniques and methods and their limitations.
7) Reaches knowledge broadly and deeply by application and development in the field of artificial intelligence, evaluates, interprets and applies knowledge.
8) Can work effectively in disciplinary and multi-disciplinary teams, lead such teams and develop solution approaches in complex situations; can work independently and take responsibility.
9) Defines the problem, accesses data, uses knowledge from different disciplines, designs researches, designs system and process, develops solution methods in order to solve current problems in the field of artificial intelligence.
10) Conveys the processes and results of his/her studies systematically and clearly in written or verbal form in national and international environments in that field or outside the field.
11) Knows the social, environmental, health, safety, legal aspects of artificial intelligence applications, project management and business life applications, and is aware of the constraints they impose on artificial intelligence applications.
12) Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

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

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Complements and applies knowledge with scientific methods using uncertain, limited or incomplete data; use information from different disciplines together.
2)	Has awareness of the new and developing applications of his/her profession, examines and learns them when needed.
3)	Defines and formulates problems related to the field, develops new and/or original ideas and methods; develops and applies innovative methods to solve them.
4)	Has the necessary skills and competencies to perform his/her profession in the most effective way and to constantly improve himself/herself.
5)	Observes social, scientific and ethical values in the stages of data collection, interpretation, announcement and in all professional activities.
6)	Has advanced theoretical and applied knowledge in the field of artificial intelligence, as well as comprehensive knowledge of current techniques and methods and their limitations.
7)	Reaches knowledge broadly and deeply by application and development in the field of artificial intelligence, evaluates, interprets and applies knowledge.
8)	Can work effectively in disciplinary and multi-disciplinary teams, lead such teams and develop solution approaches in complex situations; can work independently and take responsibility.
9)	Defines the problem, accesses data, uses knowledge from different disciplines, designs researches, designs system and process, develops solution methods in order to solve current problems in the field of artificial intelligence.
10)	Conveys the processes and results of his/her studies systematically and clearly in written or verbal form in national and international environments in that field or outside the field.
11)	Knows the social, environmental, health, safety, legal aspects of artificial intelligence applications, project management and business life applications, and is aware of the constraints they impose on artificial intelligence applications.
12)	Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 5.00
Project	1	% 20.00
Midterms	1	% 20.00
Semester Final Exam	1	% 50.00
Active Participation in Class	1	% 5.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	0	0	0
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	0	0	0
Total Workload of Teaching & Learning Activities	-	-	0
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	0	0	0
Semester Final Exam	0	0	0
Total Workload of Assesment & Evaluation Activities	-	-	0
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			0
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6

Course Notes / Textbooks:
References: