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
Elective
Course Level
First Cycle
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.
Course Description The students who succeeded in this course;
  • Describe the characteristics of Network Science
  • Learn how to qualify networks
  • Recognize and use visualizing software
  • Learn how to analyze Big Data
Course Content Network Science definitions, metrics, real life examples, Gephi

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

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

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Homework / Assignments
2
30
Presentation / Jury
1
30
Project
Seminar / Workshop
Portfolios
Midterms / Oral Exams
Final / Oral Exam
1
40
Total

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

ECTS / WORKLOAD TABLE

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

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest