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
  • Qualify networks
  • Recognize visualizing software
  • Analyze Big Data
  • Model real life problems as networks
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
Oral Exams
Midterm
Final 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
Theoretical 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
Oral Exam
Midterms
Final Exam
1
30
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science, Computer Science and Software Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

2

To be able to identify, define, formulate, and solve complex Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software engineering applications; to be able to use information technologies effectively.

5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex Software Engineering problems.

6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to be able to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Software Engineering.

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