Dokuz Eylül University Information Package / Courses Catalog

General Description

History

Established within the Faculty of Arts and Sciences, the Department of Statistics started postgraduate education in the 1994-1995 academic year. Under the Department of Statistics, Master's degree in Statistics , Master's degree in Statistics Scientific Preparation, Master's Degree in Data Science (with and without Thesis), Doctorate degree in Statistics programs are carried out. The Master Program in Data Science was established in 2019. Student admission has started in the 2019-2020 academic year. The aim of the program is to provide people who aim to pursue careers in fields such as Statistics, Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Big Data, and the use of the methods and technologies required for data science, with the necessary statistical knowledge and skills.

Qualification Awarded

M.Sc., Data Science

Level of Qualification

Second Cycle (Master's Degree)

Specific Admission Requirements

First Cycle (Bachelor's Degree) diploma; minimum score of 55 from the National Central Graduate Education Entrance Examination (ALES) in the related field; a minimum score of 65 from the graduate school entrance exam.

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

2 years , 2 semesters per year, 16 weeks per semester, 120 ECTS in total

Profile of the Programme

The master program in Data Science includes developing descriptive and inferential statistical methods and programming skills that will form the basis of statistics and informatics methodology required for data science. In addition, the program provides the necessary equipment for those who aim to pursue a career in fields such as Statistics, Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Big Data, by combining Statistics applications and programming knowledge and skills.

The language of instruction of the master program in Data Science is 100% in Turkish. In addition, our department has bilateral agreements with various universities within the scope of ERASMUS and FARABI programs. There are 21 lecturers in our department.

Key Learning Outcomes

1	To be able to get a fundamental basis in informatics and statistical methodology necessary for Data Science.
2	To be able to collect and manage data to devise solutions.
3	To be able to import, tidy, and process data to perform analysis.
4	To be able to choose appropriately from exploratory and inferential methods for analyzing data, and interpret the results contextually.
5	To be able to create a wider range of visual and numerical data summaries and carry out basic inferential procedures using statistical programming languages.
6	To be able to construct complex statistical models, evaluate the fit of such models to the data, and apply these models in real-world contexts.
7	To be able to formulate simple algorithms to solve problems, and can code them in a high-level language appropriate for data science work (e.g., Python, R, Java).

Occupational Profiles of Graduates with Examples

Our graduates can be employed in Research and Planning departments of Public and Private Organizations, at Finance and Insurance Companies as an expert or managers, and also in software-informatics departments, they can get career opportunities at many levels as data scientists, system analysts, process analyst, database manager, operations manager, market researcher, business analyst, software developer, etc.

Access to Further Studies

May apply to third cycle programmes.

Course Structure Diagram with Credits

Semester	No	Course Unit Code	Course Unit Title	Type of Course	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 MAT 5083 Introduction to Applied Mathematics or DSM 5001 Probability and Statistics in Data Science, and one of the courses DSM 5003 R for Statistical Computing and Programming or DSM 5004 Python for Data Science with approval of the supervisor. In the case of one of these courses taken during the M.Sc. education, these are not obligatory. The registration must be noticed to the Institute during course registration period by the supervisor.
T: Theoretical P: Practice L: Laboratory B: Spring Semester G: Fall Semester H: Full Year
1 .Semester:
G	1	FBE 5555	SCIENTIFIC RESEARCH TECHNIQUES AND PUBLICATION ETHICS	COMPULSORY	3	0	0	5
G	0	-	ELECTIVE COURSE	ELECTIVE	-	-	-	25
TOTAL:								30

1 .Semester Elective:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS
G	1	DSM 5001	PROBABILITY AND STATISTICS FOR DATA SCIENCE	ELECTIVE	3	0	0	8
G	2	DSM 5003	R FOR STATISTICAL COMPUTING AND PROGRAMMING	ELECTIVE	3	0	0	8
G	3	DSM 5005	DATA PROCESSING AND MANAGEMENT	ELECTIVE	3	0	0	8
G	4	DSM 5007	SUPERVISED STATISTICAL LEARNING	ELECTIVE	3	0	0	8
G	5	MAT 5083	INTRODUCTION TO APPLIED MATHEMATICS	ELECTIVE	3	0	0	9
G	6	DSM 5011	APPLICATIONS OF COMPUTATIONAL LINGUISTICS	ELECTIVE	3	0	0	8
G	7	DSM 5013	BIG DATA TECHNOLOGIES AND APPLICATIONS	ELECTIVE	3	0	0	8
G	8	DSM 5015	FUZZY LOGIC IN STATISTICS	ELECTIVE	3	0	0	8
G	9	DSM 5009	DETERMINISTIC OPTIMIZATION METHODS	ELECTIVE	3	0	0	8

2 .Semester:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS
B	1	DSM 5098	M.SC.RESEARCH	COMPULSORY	2	0	0	3
B	2	DSM 5096	M.SC.SEMINAR	COMPULSORY	0	2	0	3
B	0	-	ELECTIVE COURSE	ELECTIVE	-	-	-	24
TOTAL:								30

2 .Semester Elective:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS
B	1	DSM 5002	INFERENTIAL STATISTICAL METHODS FOR DATA SCIENCE	ELECTIVE	3	0	0	8
B	2	DSM 5004	PYTHON FOR DATA SCIENCE	ELECTIVE	3	0	0	8
B	3	DSM 5006	EXPLORATORY DATA ANALYSIS AND DATA VISUALIZATION	ELECTIVE	3	0	0	8
B	4	DSM 5016	IMAGE PROCESSING	ELECTIVE	3	0	0	8
B	5	DSM 5010	DATA ANALYTICS FOR OPERATIONS MANAGEMENT	ELECTIVE	3	0	0	8
B	6	DSM 5012	APPLIED TIME SERIES ANALYSIS	ELECTIVE	3	0	0	8
B	7	DSM 5014	DEEP LEARNING METHODS AND APPLICATIONS	ELECTIVE	3	0	0	8
B	8	DSM 5008	UNSUPERVISED STATISTICAL LEARNING	ELECTIVE	3	0	0	8

3.Semester:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS
G	1	DSM 5099	M.SC.THESIS	COMPULSORY	0	0	0	30
TOTAL:								30

4.Semester:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS
B	1	DSM 5099	M.SC.THESIS	COMPULSORY	0	0	0	30
TOTAL:								30

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

Second Cycle (Master's Degree) Programme with thesis is comprised of courses (at least 54 ECTS), a seminar (3 ECTS), M. Sc. Research (3 ECTS) and M. Sc. Thesis (60 ECTS), in total 120 ECTS credits. 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 Department:Prof.Dr.Burcu Hüdaverdi
E-mail : istatistik@deu.edu.tr
Telephone : +90 232 - 3018510
Dokuz Eylül Üniversitesi Fen Fakültesi
İstatistik Bölümü
Tınaztepe Kampüsü
35390 Buca / İzmir

DEGREE PROGRAMMES