IE 360 | Course Introduction and Application Information

Course Name

Network Science and Applications

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 360	Fall/Spring	3	0	3	5

Prerequisites

	IE 240 To succeed (To get a grade of at least DD)
and	IE 252 To succeed (To get a grade of at least DD)
or	MATH 240 To succeed (To get a grade of at least DD)
or	ISE 204 To succeed (To get a grade of at least DD)

Course Language

English

Course Type

Service Course

Course Level

First Cycle

Mode of Delivery

Teaching Methods and Techniques of the Course

National Occupation Classification

Course Coordinator

Course Lecturer(s)

Assistant(s)

Course Objectives	In this course, basic concepts and applications of Network Science is aimed to be introduced in a general framework. During the previous years, the increasingly complex systems of life that takes place are more and more widespread. The social, economic and other visual presentations have been quickly become available. The aim of this course is to introduce an alternative approach to analyze the development of new structures with the help of the new cross-disciplinary science and concepts called Network Science.
Learning Outcomes	The students who succeeded in this course; Describe the characteristics of Network Science Qualify networks Recognize visualizing software Analyze Big Data Model real life problems as networks
Course Description	Network Science definitions, metrics, real life examples, Gephi
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to Course	Chapter 1 and lecture files
2	The Characteristics of Network Science	Chapter 2-5
3	Graph Theory, Random Graphs, Small World Effect	Chapter 6-7
4	Watts and Strogatz Model, Scale Free Networks	Chapter 8 and Chapter 10
5	Qualifying Networks, Centrality Measures, Hierarchy	Lecture notes
6	Social Networks, Definition, Evolution, Examples	Lecture notes and files
7	Presentations
8	Complex Networks, Industrial and Economic Development	Chapter 11-13
9	Visualizing Networks	Chapter 14-15
10	Visualizing Software, Gephi, R, NetworkX	Chapter 16
11	Big Data and Network Analysis Tools	Chapter 17-18
12	Evolving Networks, Epidemics on Scale-Free Networks	Chapter 22
13	An example: Flavor Ingredients Network	Lecture Notes and Files
14	Presentation of term projects 1
15	Presentation of term projects 2
16	Review of the Semester

Course Notes/Textbooks	A-L. Barabási , Network Science, http://barabasi.com/networksciencebook, available online
Suggested Readings/Materials

Weighting of Semester Activities on the Final Grade		60
Weighting of End-of-Semester Activities on the Final Grade		40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours	16		0
Study Hours Out of Class	1	20	20
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	2	10	20
Presentation / Jury	1	32	32
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	30	30
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
2	parent-1	-	-	-	-	-
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10	asasas	-	-	-	-	-
11	asdasd asdasd asd	-	-	-	-	-
12	ekl_ing	-	-	-	-	-
13	damla duzenle	-	-	-	-	-
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16	test	-	-	-	-	-