DEGREE PROGRAMMES

: Statistics (English)

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 Degree in Statistics Master Degree in Statistics Scientific Preparation, Master Degree in Data Science (with and without Thesis), Doctorate Degree in Statistics programs are carried out. The graduate program of the Department of Statistics is 100% in English. The program aims to train students who can independently collect data and produce information using both theoretical and applied statistical concepts at the graduate level. Educational opportunities are offered to have a statistical perspective and to contribute to the solution of problems arising in other disciplines. The aim of the program is to equip people who aim to pursue a career in fields such as Statistics, Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Big Data, and experts who want to do academic work, with advanced Statistics theory and programming knowledge and skills. The compulsory and elective courses of the program cover the theory and applications of Statistics, as well as an intensive computer programming content. Basically, it is aimed that students gain the skills of accessing information, and improve their ability to evaluate and interpret the information they have obtained.

Qualification Awarded

Ph.D. in Statistics

Level of Qualification

Third Cycle (Doctorate Degree)

Specific Admission Requirements

Second Cycle Degree in the same or in related disciplines. Acceptable score on ALES (Academic Personnel and Graduate Education Entrance Exam) (at least 60) or Equivalent GRE, GMAT score. Acceptable score on Language Proficiency Tests. Acceptable weighted score based on the first or second cycle (with thesis) cumulative grade point average (GPA) and ALES score. However, ALES requirement is waived for the graduates of doctorate /arts proficiency/medical residency/ dental residency/ veterinary residency/ pharmaceutical residency programs as well as for the admissions to the graduate programs offered by the Conservatory and the departments of Fine Arts Faculty, where students are admitted only by the Artistic Aptitude Test. Final admission is based on the evaluation of the related academic unit committee. International student admission requirements are decided by the Graduate School Executive Committee.

Foreign language proficiency requirement is a score of at least 55 points (taken within the last 5 years) in YDS, e-YDS and YÖKDİL (English) from the central foreign language exam conducted by ÖSYM, or an equivalent score from any of the exams whose equivalence is accepted by the ÖSYM Executive Board ( https://denklik.yok.gov.tr/yabanci-dil-esdegerliği).

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 aim of the doctorate program in Statistics is to equip students with theoretical and practical statistical information and values that should be possessed by decision makers with analytical and critical thinking skills. 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 the knowledge and skills of Statistics theory and computer programming. It aims to graduate students who can follow the innovations in science and technology and constantly renew their knowledge of statistics with the awareness of lifelong learning.

The language of instruction of the doctorate program in Statistics is 100% in English. 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 have an advanced level theory and application backround in statistics.
2   To have an advanced level literature knowledge about his/her dissertation .
3   To publish at least one research paper about his/her dissertation in a SCI or SCI-EXP journal.
4   To propose a new method or an approach for a problem related with his/her dissertation.
5   To be able to superwise a research which is about his/her dissertation.
6   To understand and interpret new statistical methods and approaches in a systematic manner.
7   To understand the interaction with statistics and other disciplines and to use his/her statistical backround while evaluating new ideas.
8   To defend ideas about his/her statistical research area in a professional community.
9   To have advanced communication skills in at least one foreing language as verbal, visual or written.
10   By improving the sense of statistics science in a community, to contribute the process of being an information society.

Occupational Profiles of Graduates with Examples

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

Access to Further Studies

May apply to post doctorate programmes.

Course Structure Diagram with Credits

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 the compulsory courses 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. For Ph. D. students it is obligatory to take minimum 2 non-credit courses among "Ph. D. Programs Common Elective Courses" and be successful with grade (B) within 4 semesters of course taking period.
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 Course Unit Type T P L ECTS
G 1 MAT 5102 NUMERICAL AND APPROXIMATE METHODS COMPULSORY 3 0 0 9
G 2 STA 5053 PROBABILITY AND STATISTICAL INFERENCE - I COMPULSORY 3 0 0 8
G 0 - ELECTIVE COURSE ELECTIVE - - - 13
TOTAL:   30
 
2 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
B 1 FBE 6668 PHILOSOPHY OF SCIENCE AND ETHICS COMPULSORY 3 0 0 5
B 2 STA 5056 PROBABILITY AND STATISTICAL INFERENCE - II COMPULSORY 3 0 0 9
B 3 STA 6098 PH.D. RESEARCH COMPULSORY 3 0 0 9
B 0 - ELECTIVE COURSE ELECTIVE - - - 7
TOTAL:   30
 
3 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
G 1 STA 6094 PH.D. SEMINAR COMPULSORY 0 3 0 5
G 2 STA 6098 PH.D. RESEARCH COMPULSORY 3 0 0 9
G 0 - ELECTIVE COURSE ELECTIVE - - - 16
TOTAL:   30
 
4 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
B 1 STA 6099 PH.D. THESIS COMPULSORY 0 0 0 30
TOTAL:   30
 
5 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
G 1 STA 6099 PH.D. THESIS COMPULSORY 0 0 0 30
TOTAL:   30
 
6 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
B 1 STA 6099 PH.D. THESIS COMPULSORY 0 0 0 30
TOTAL:   30
 
7 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
G 1 STA 6099 PH.D. THESIS COMPULSORY 0 0 0 30
TOTAL:   30
 
8 .Semester:
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
B 1 STA 6099 PH.D. THESIS COMPULSORY 0 0 0 30
TOTAL:   30
 
FLEXIBLE ELECTIVE COURSE ACCORDING TO ECTS
Semester No Course Unit Code Course Unit Title Course Unit Type T P L ECTS
Z 1 STA 5021 STATISTICAL SOFTWARE İN DATA ANALYSIS ELECTIVE 2 2 0 7
Z 2 STA 5059 STATISTICAL RESEARCH METHODOLOGY I ELECTIVE 3 0 0 7
Z 3 STA 5069 BIO-STATISTICS ELECTIVE 3 0 0 7
Z 4 STA 5071 STATISTICAL QUALITY CONTROL ELECTIVE 3 0 0 7
Z 5 STA 5075 REGRESSION ANALYSIS ELECTIVE 3 0 0 7
Z 6 STA 5079 OPERATIONS RESEARCH IN LOGISTICS ELECTIVE 3 0 0 7
Z 7 STA 5083 DISCRETE OPTIMIZATION ELECTIVE 3 0 0 7
Z 8 STA 5091 FINANCIAL MATHEMATICS ELECTIVE 3 0 0 7
Z 9 STA 5093 MATHEMATICAL MODELING AND LINEAR OPTIMIZATION ELECTIVE 3 0 0 7
Z 10 STA 6001 ADVANCED STATISTICAL INFERENCE - I ELECTIVE 3 0 0 8
Z 11 STA 6003 ADVANCED PROBABILITY THEORY ELECTIVE 3 0 0 8
Z 12 STA 6007 ADVANCED SAMPLING THEORY ELECTIVE 3 0 0 8
Z 13 STA 6011 MULTIVARIATE ANALYSIS ELECTIVE 3 0 0 8
Z 14 STA 6043 OPTIMIZATION THEORY ELECTIVE 3 0 0 8
Z 15 STA 6045 LIFE CONTINGENCIES-I ELECTIVE 3 0 0 8
Z 16 STA 6051 DYNAMIC PROGRAMMING ELECTIVE 3 0 0 8
Z 17 STA 6053 HEURISTIC SEARCH METHODS ELECTIVE 3 0 0 5
Z 18 STA 5023 CATEGORICAL DATA ANALYSIS ELECTIVE 3 0 0 7
Z 19 STA 5025 POWER ANALYSIS OF STATISTICAL METHODS ELECTIVE 3 0 0 7
Z 20 STA 6015 DATA MINING TECHNIQUES IN STATISTIC ELECTIVE 3 0 0 8
Z 21 FBE 6093 ACADEMIC WRITING AND PRESENTATION TECHNICS ELECTIVE 1 1 0 5
Z 22 FBE 6075 PLANNING AND EVALUATION IN LEARNING ELECTIVE 1 1 0 5
Z 23 FBE 6079 MATHEMATICAL WRITING WITH LATEX ELECTIVE 1 1 0 5
Z 24 FBE 6071 INNOVATION, INVENTION AND CREATIVITY ELECTIVE 1 1 0 5
Z 25 FBE 6073 PROGRESS AND LEARNING ELECTIVE 1 1 0 5
Z 26 FBE 6077 SCIENTIFIC RESEARCH AND ETHICS IN PUBLISHING ELECTIVE 1 1 0 5
Z 27 STA 5095 STATISTICAL PROCESS DESIGN AND IMPROVEMENT ELECTIVE 3 0 0 7
Z 28 STA 5013 TOTAL QUALITY MANAGEMENT AND AUDITING ELECTIVE 3 0 0 7
Z 29 STA 5015 STOCHASTIC PROCESSES ELECTIVE 3 0 0 8
Z 30 STA 5009 SAMPLING AND SURVEY DESIGN ELECTIVE 3 0 0 8
Z 31 STA 5007 RESAMPLING METHODS ELECTIVE 3 0 0 8
Z 32 STA 5027 STATISTICAL DEPENDENCE AND COPULA THEORY IN FINANCE ELECTIVE 3 0 0 7
Z 33 STA 6009 ASSESSMENT OF INFLUENCE IN REGRESSION ELECTIVE 3 0 0 8
Z 34 STA 6013 ASYMPTOTIC THEORY FOR TIME SERIES ELECTIVE 3 0 0 8
Z 35 STA 5031 LINEAR STATISTICAL MODELS ELECTIVE 3 0 0 8
Z 36 FBE 6095 TWENTIETH CENTURY PHILOSOPHICAL MOVEMENTS ELECTIVE 2 0 0 5
Z 37 FBE 6097 HISTORY OF SCIENCE AND PHILOSOPHY OF SCIENCE ELECTIVE 2 0 0 5
Z 38 STA 5107 META ANALYSIS ELECTIVE 3 0 0 8
Z 39 STA 5105 LINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 8
Z 40 STA 5103 COMPUTATIONAL STATISTICS ELECTIVE 3 0 0 8
Z 41 STA 5101 EXPLORATORY DATA ANALYSIS WITH R ELECTIVE 3 0 0 8
Z 42 STA 5109 RELIABILITY THEORY AND METHODS ELECTIVE 3 0 0 8
Z 43 STA 5054 NONPARAMETRIC STATISTICS ELECTIVE 3 0 0 8
Z 44 STA 5112 STATISTICAL METHODS IN MACHINE LEARNING ELECTIVE 3 0 0 8
Z 45 STA 5060 STATISTICAL RESEARCH METHODOLOGY - II ELECTIVE 3 0 0 8
Z 46 STA 5068 STATISTICAL DESIGN OF EXPERIMENTS ELECTIVE 3 0 0 8
Z 47 STA 5074 SIX SIGMA IN QUALITY MANAGEMENT ELECTIVE 2 2 0 8
Z 48 STA 5078 NONLINEAR TIME SERIES ANALYSIS ELECTIVE 3 0 0 8
Z 49 STA 5082 GAME THEORY ELECTIVE 3 0 0 8
Z 50 STA 5084 NETWORK ANALYSIS ELECTIVE 3 0 0 8
Z 51 STA 5086 INVENTORY THEORY ELECTIVE 3 0 0 8
Z 52 STA 5088 STATISTICAL SIMULATION ELECTIVE 3 0 0 8
Z 53 STA 5094 LOSS MODELS ELECTIVE 3 0 0 8
Z 54 STA 6002 ADVANCED STATISTICAL INFERENCE - II ELECTIVE 3 0 0 9
Z 55 STA 6004 ADVANCED EXPERIMENTAL DESIGN ELECTIVE 3 0 0 8
Z 56 STA 6046 LIFE CONTINGENCIES-II ELECTIVE 3 0 0 8
Z 57 STA 6052 MATHEMATICAL RISK THEORY ELECTIVE 3 0 0 8
Z 58 FBE 7000 SPECIAL TOPICS ELECTIVE 3 0 0 5
Z 59 STA 5012 NONLINEAR OPTIMIZATION ELECTIVE 3 0 0 8
Z 60 STA 5024 ALTERNATIVE REGRESSION METHODS ELECTIVE 3 0 0 8
Z 61 STA 6016 SOFT COMPUTING TECHNOLOGIES ELECTIVE 3 0 0 8
Z 62 STA 5026 ROBUST ESTIMATION METHODS AND HYPOTHESIS TESTING ELECTIVE 3 0 0 8
Z 63 FBE 6072 DATA PROCESSING ELECTIVE 1 1 0 5
Z 64 FBE 6076 HISTORY OF NATURAL SCIENCES ELECTIVE 1 1 0 5
Z 65 FBE 6074 PREPARING PROJECT PROPOSAL ELECTIVE 1 1 0 5
Z 66 STA 5032 ADVANCED STATISTICAL QUALITY CONTROL ELECTIVE 3 0 0 8
Z 67 STA 5090 SPECIAL TECHNIQUES IN QUALITY MANAGEMENT ELECTIVE 3 0 0 8
Z 68 STA 5104 QUEUING THEORY ELECTIVE 3 0 0 8
Z 69 STA 5102 DECISION THEORY ELECTIVE 3 0 0 8
Z 70 STA 6018 ADVANCED STOCHASTIC PROCESSES ELECTIVE 3 0 0 8
Z 71 STA 5106 GENERALIZED LINEAR MODELS ELECTIVE 3 0 0 8
Z 72 STA 6054 ADVANCED TIME SERIES ANALYSIS ELECTIVE 3 0 0 8
Z 73 FBE 6078 PHILOSOPHY OF ENVIRONMENT AND TECHNOLOGY ELECTIVE 2 0 0 5
Z 74 FBE 6080 ETHICS OF SCIENCE ELECTIVE 2 0 0 5
Z 75 STA 5108 ORDERED RANDOM VARIABLES ELECTIVE 3 0 0 7
Z 76 STA 5110 SURVIVAL ANALYSIS ELECTIVE 3 0 0 7
Z 77 STA 5058 MULTIVARIATE STATISTICS ELECTIVE 3 0 0 8
 

Examination Regulations, Assessment and Grading

Related items of Dokuz Eylül 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 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