Dokuz Eylül University Information Package / Courses Catalog

General Description

History

Computer Science Doctorate Program has started 100% English education in 2019-2020 academic year within the Institute of Science of the Dokuz Eylul University.The basis of the program consists of computer mathematics, large-scale data analysis and intelligent systems.

Qualification Awarded

Ph.D. in Computer Science

Level of Qualification

Third Cycle (Doctorate Degree)

Specific Admission Requirements

Second Cycle (Master's Degree) diploma; minimum 80/100 or 3/4 Cumulative Grade Point Average, minimum score of 60 from the National Central Graduate Education Entrance Examination (ALES) in the related field; a minimum score of 70 from the graduate school entrance exam; a minimum score of 55 from the nation-wide Foreign Language Exam ( YDS ) or equivalent score from an exam accredited by YÖK.

Specific Arrangements for Recognition of Prior Learning (Formal, Non-Formal and Informal)

According to the Regulations of Dokuz Eylül University for Graduate Schools, students may be accepted for graduate transfer with the approval of the Department Directorate and decision of the Board of Directors of the Graduate School in case the student fulfills the graduate transfer regulations decided by the General Council of the Graduate School. Previously taken courses at another graduate programme with a successful grade may be recognized by the related programmes with the written request of the students including course contents and the transcript, and by the recommendation of the Department Directorates and by the decision of the Board of Directors. The courses taken by the outgoing Exchange students may have the recognition at the school either as compulsory or elective by the decision of the Board of Directors.

Qualification Requirements and Regulations

4 years, 2 semesters per year, 16 weeks per semester, 240 ECTS in total.

Profile of the Programme

The main aim of the program is to train qualified academicians and experts who are equipped to do scientific research and development under the titles of current computer science and can make theoretical contributions in different fields.Within the scope of the Ph.D. Program in Computer Science, the fields of Data Science and Mining, Soft Computing, Fuzzy Decision Making, Artificial and Computational Intelligence, Intelligent Decision Support Systems, Bioinformatics, Network Analysis, Machine Learning and Information Security are focused on.

Key Learning Outcomes

1	Possess the necessary knowledge for making analysis, synthesis, modeling, theoretical and practical work in computer science, and for choosing, improving and implementing the appropriate methods.
2	Reach information by conducting scientific research in Information Technology and use the advanced theoretical and practical knowledge obtained, properly.
3	Plan and conduct academic research and share the scientific work at national and international levels, both verbally and in writing.
4	Develop and apply innovative ideas, methods or approaches to solve computer science problems.
5	Become master in algorithmic solution methods and state-of-the-art programming languages.
6	Work individually, as a team member or leader in disciplinary or inter-disciplinary studies and take responsibilities.
7	Have awareness in progress related to the profession, follow new trends and develop ideas and opinions about them.
8	Use a foreign language to establish effective communication verbally and in writing for the profession.
9	Have the necessary awareness for the social and environmental dimensions of Information Technology applications at both global and communal levels.
10	Is aware of the social, legal, ethical and moral values and conduct research and applied studies within the framework of these values.

Occupational Profiles of Graduates with Examples

It is aimed that the graduates of the program will be able to work successfully in the fields of information technologies, software development, research and development, engineering, systems and process analysis with the knowledge, skills and experience acquired during their education.

Access to Further Studies

May apply to post doctorate programmes.

Course Structure Diagram with Credits

Semester	No	Course Unit Code	Course Unit Title	Course Unit Type	T	P	L	ECTS
In addition to the compulsory courses of the programme, students register to elective courses appropriate for their thesis topic, on the consent of their supervisor. If necessary, on the consent of their supervisor, they can also register to courses from other programmes (from DEU or other universities). It is obligatory to register MAT5101 Applied Mathematics (3 0 0) or MAT5102 Numerical and Approximate Methods (3 0 0) with approval of the supervisor. In the case of one of these courses taken during the M.Sc. education, these are not obligatory.
T: Theoretical P: Practice L: Laboratory B: Spring Semester G: Fall Semester H: Full Year
ALL COURSES
Z	1	CSE 5083	ANALYSIS OF ALGORITHMS	ELECTIVE	3	0	0	8
Z	2	CSC 5052	COMMUNICATION NETWORKS AND VULNERABILITY	ELECTIVE	3	0	0	8
Z	3	CSE 6013	ADVANCED EVOLUTIONARY COMPUTATION	ELECTIVE	3	0	0	8
Z	4	CSC 5050	INFORMATION AGGREGATION METHODS WITH FUZZY APPROACHES	ELECTIVE	3	0	0	8
Z	5	CSC 5048	ADVANCED COMBINATORIAL OPTIMIZATION : PROBLEMS AND METHODS	ELECTIVE	3	0	0	8
Z	6	CSC 5034	R PROGRAMMING FOR DATA SCIENCE	ELECTIVE	3	0	0	8
Z	7	CSC 5024	ARTIFICIAL INTELLIGENCE OPTIMIZATION ALGORITHMS	ELECTIVE	3	0	0	8
Z	8	CSC 5025	APPLIED MATRIX ALGORITHMS	ELECTIVE	3	0	0	8
Z	9	CSC 5057	BASICS OF SYSTEM PROGRAMMING	ELECTIVE	3	0	0	7
Z	10	CSC 5042	THEORETICAL ASPECTS OF ALGORITHMS DESIGN AND ANALYSIS	ELECTIVE	3	0	0	8
Z	11	CSC 5055	APPLIED MODELING WITH FUZZY LOGIC	ELECTIVE	3	0	0	8
Z	12	CSC 5040	PRACTICAL DATABASE PROGRAMMING WITH C#	ELECTIVE	3	0	0	8
Z	13	CSC 5038	FUZZY CLUSTERING AND CLASSIFICATION TECHNIQUES	ELECTIVE	3	0	0	8
Z	14	CSC 5053	FUZZY DATA ANALYSIS	ELECTIVE	3	0	0	8
Z	15	CSE 5065	SECURITY AND PRIVACY ENGINEERING	ELECTIVE	3	0	0	8
Z	16	STA 5012	NONLINEAR OPTIMIZATION	ELECTIVE	3	0	0	8
Z	17	CSC 5016	INFORMATION SECURITY MANAGEMENT	ELECTIVE	3	0	0	8
Z	18	STA 6015	DATA MINING TECHNIQUES IN STATISTIC	ELECTIVE	3	0	0	8
Z	19	EEE 5129	RANDOM VARIABLES AND STOCHASTIC PROCESSES	ELECTIVE	3	0	0	9
Z	20	MAT 5101	APPLIED MATHEMATICS	ELECTIVE	3	0	0	9
Z	21	CSC 5018	ADVANCED SOFTWARE AND APPLICATIONS SECURITY	ELECTIVE	3	0	0	8
Z	22	CSC 5046	INTELLIGENT SYSTEM APPLICATIONS USING FUZZY LOGIC	ELECTIVE	3	0	0	8
Z	23	STA 6016	SOFT COMPUTING TECHNOLOGIES	ELECTIVE	3	0	0	8
Z	24	CSC 5027	SPECIAL TOPICS IN COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING	ELECTIVE	3	0	0	8
Z	25	MAT 5102	NUMERICAL AND APPROXIMATE METHODS	ELECTIVE	3	0	0	9
Z	26	CSC 5029	SOCIAL NETWORK ANALYSIS	ELECTIVE	3	0	0	8
Z	27	CSC 5020	DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS	ELECTIVE	3	0	0	8
Z	28	CSC 5028	ALGEBRAIC GRAPH THEORY	ELECTIVE	3	0	0	8
Z	29	CSC 5030	COMPUTATIONAL NETWORK DESIGN	ELECTIVE	3	0	0	8
Z	30	CSC 5036	TRANSPORTATION INFORMATION SYSTEMS	ELECTIVE	3	0	0	8
Z	31	CSC 5035	SMART CITY INFORMATION SYSTEMS	ELECTIVE	3	0	0	8
Z	32	CSC 5051	CYBER-PHYSICAL SYSTEMS	ELECTIVE	3	0	0	8
Z	33	CSC 5031	GRAPH THEORY TECHNIQUES IN MATHEMATICAL MODELLING	ELECTIVE	3	0	0	8
Z	34	CSC 5033	INTRODUCTION TO INTELLIGENT SYSTEMS	ELECTIVE	3	0	0	8
Z	35	CSC 5044	EXTRAMAL PROBLEMS AND SPECIAL GRAPHS	ELECTIVE	3	0	0	8
Z	36	CSC 5021	ADVANCED INFORMATION SECURITY	ELECTIVE	3	0	0	8
Z	37	CSC 5019	SOFTWARE REQUIREMENTS ENGINEERING	ELECTIVE	3	0	0	8
Z	38	CSC 5017	SECURE SOFTWARE DESIGN AND PROGRAMMING	ELECTIVE	3	0	0	8
Z	39	CSC 5015	QUANTITATIVE METHODS IN RISK MANAGEMENT	ELECTIVE	3	0	0	8
Z	40	CSC 5014	ADVANCED SCIENTIFIC COMPUTING	ELECTIVE	3	0	0	8
Z	41	CSC 5010	FUZZY MULTICRITERIA DECISION TECHNIQUES	ELECTIVE	3	0	0	8
Z	42	CSC 5004	INFORMATION AND ENTROPY	ELECTIVE	3	0	0	7
Z	43	CSC 5009	PRACTICAL PROGRAMMING WITH MATHEMATICAL MODELLING	ELECTIVE	3	0	0	8
Z	44	CSC 5005	MATLAB PROGRAMMING WITH APPLICATIONS	ELECTIVE	3	0	0	8
Z	45	CSC 5003	SYSTEM ANALYSIS AND DESIGN	ELECTIVE	3	0	0	7
B	46	CSC 6098	PH.D.RESEARCH	EXPERTNESS	3	0	0	9
B	47	CSC 6094	PH.D. SEMINAR	SEMINAR	0	3	0	5
B	48	CSC 6099	PH.D.THESIS	THESIS	0	0	0	150
B	49	FBE 6668	PHILOSOPHY OF SCIENCE AND ETHICS	REQUIRED	3	0	0	5

Examination Regulations, Assessment and Grading

Related items of Dokuz Eylul University Regulations of Graduate Education and Exams and related items of Institute of Natural and Applied Sciences Regulations of Education and Code of Practicing Exams are applied for the exams and course grades. The course evaluation criteria are defined for each course by the instructor(s) of the corresponding course and are given in the Course Description Form found in the information package.

Graduation Requirements

Third Cycle (Doctor of Philosophy) Programme is comprised of courses (at least 67 ECTS),Ph.D.Seminar (5 ECTS), Ph. D. Research (18 ECTS) / Ph. D. Thesis (150 ECTS) courses, thesis proposal, doctoral qualifying examination and thesis examination with a total credit of 240 ECTS. Students must have minimum Cumulative Grade Point Average (CGPA) of 2.50 / 4.00 and completed all the courses with at least CB / S / TP grades.

Mode of Study (Full-Time, Part-Time, E-Learning )

Full-time

Programme Director or Equivalent

Head of the Department : Prof.Dr. Çağın Kandemir Çavaş
E-Mail : cagin.kandemir@deu.edu.tr
Phone : 301 95 00
Address: DEU Faculty of Science

DEGREE PROGRAMMES