DEGREE PROGRAMMES

: Data Science

General Description

History

Qualification Awarded

M.sc. In 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

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

Access to Further Studies

May apply to third cycle programmes.

Course Structure Diagram with Credits


T: Theoretical P: Practice L: Laboratory
B: Spring Semester G: Fall Semester H: Full Year
1 .Semester:
Semester No Course Unit Code Course Unit Title Type of Course T P L ECTS
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.Selma GÜRLER
E-mail : selma.erdogan@deu.edu.tr
Telephone : +90 232 - 3018571
Dokuz Eylül Üniversitesi Fen Fakültesi
Tınaztepe Kampüsü
35390 Buca / İzmir