Dokuz Eylül University Information Package / Courses Catalog

General Description

History

The MSc program in Data Science was established in 2019. In the 2019-2020 academic year, student admissions started. The aim of the program is to train experts who are familiar with the fundamentals of statistical thinking, know the power of machine learning and the use of the methods and technologies required for data science.Graduates can be employed as data scientist and data analyst in public, private sector and academic institutions.

Qualification Awarded

M.sc. In Data Science -- Non Thesis

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 regularions 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

1,5 years, 2 semesters per year, 16 weeks per semester, 90 ECTS in total.

Profile of the Programme

The program covers descriptive and inferential statistical methods and computer applications that will form the basis of statistical and informatics methodology required for data science.

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

Graduates can be employed as data scientist and data analyst in public, private sector and academic institutions.

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	-	SEMESTER 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	0	-	ELECTIVE COURSE	ELECTIVE	-	-	-	30
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
B	1	DSM 5097	M.SC. TERM PROJECT	COMPULSORY	0	0	0	15
B	0	-	ELECTIVE COURSE	ELECTIVE	-	-	-	15
TOTAL:								30

3 .Semester Elective:
Semester	No	Course Unit Code	Course Unit Title	Type of Course	T	P	L	ECTS

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) Non-Thesis Program is comprised of a courses (75 ECTS) and term project (15 ECTS), in total 90 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
Tınaztepe Kampüsü
35390 Buca / İzmir

DEGREE PROGRAMMES