Qualification Awarded
One deserves a degree of Ph.D.
Specific Admission Requirements
Statistics Program Doctorate program acceptance and registration conditions; With a graduate diploma, it is required to have an ALES base score determined by the Muğla Sıtkı Koçman University Senate in the type of numerical score for Science. From one of the foreign languages such as English, German and; Must have a base score from ÜDS, KPDS, TOEFL or equivalent exams accepted by YÖK.
Qualification Requirements
Based on undergraduate and graduate qualifications, bringing the knowledge of statistical theories and applications to the level of expertise and reaching a level that can be used in practice
To be able to define the problems in the field of study, to analyze them, to solve them with scientific methods,
To be able to independently conduct a study that requires expertise in the field in which Statistical Methods are used,
Gains the ability to read, analyze, write academic publications, determine a scientific research topic, write and carry out projects.
To be able to use national and international academic resources effectively.
Designing statistical applications to meet the requirements and making them available in improvement studies
Gains the ability to present the results of scientific studies in written or oral form.
To be able to design solution methods for the problem and use appropriate tools for this purpose.
Takes responsibility individually and as a team member in multi-disciplinary projects, gains an open-minded, open-minded, constructive and self-confident working discipline.
To have knowledge and experience about software commonly used in the fields of statistics
Recognition of Prior Learning
For PhD education, applications of the graduates of Statistics, Mathematics, Econometrics, Industrial Engineering, Computer Engineering Departments are accepted.
History
The foundations of the Department of Statistics are based on the Department of Statistics and Computer Science, which was established in 1994 within the Faculty of Science and Letters of Muğla University. This department started its first education in 1995 and had its first graduates in 1999. The Department of Statistics and Computer Sciences, whose student admissions were stopped in 2001, changed its name in 2003 and resumed education as only the Department of Statistics. The Department of Statistics gave its first graduates in 2007. With the departure of the Faculty of Arts and Sciences, the Department of Statistics continues on its way under the Faculty of Science since 2010.
As of the Spring semester of the 2018-19 academic year, our department has started to receive doctoral students.
Profile of the Programme
As of the 2019-2020 academic year, there are 1 Professor, 7 Associate Professors, 4 Doctor Instructors, 4 Research Assistants in our department. For those who are admitted with a master's degree with thesis, excluding the time spent in scientific preparation, the doctorate program is eight semesters, regardless of whether they enroll for each semester, starting from the semester in which the courses related to the program they are enrolled in, and the maximum completion period is twelve semesters; For those admitted with a bachelor's degree, ten semesters and the maximum completion period is fourteen semesters. Students who have applied for a doctorate program with a bachelor's degree, who cannot complete their credit courses and / or thesis work within the maximum duration, and those who fail in their doctoral thesis, are awarded a master's diploma without a thesis upon their request, provided that they have fulfilled the required credit load, project and other similar requirements for the non-thesis master's degree.
Program Outcomes
1- |
Based on undergraduate and graduate qualifications, to bring the knowledge of statistical theories and applications to the level of expertise and to reach a level that can be used in application areas. |
2- |
To be able to define the problems in the field of study, to analyze them, to solve them with scientific methods, |
3- |
To be able to independently conduct a study that requires expertise in the field in which statistical methods are used. |
4- |
to be able to make an academic publication, reading, analyzing, making decision about research subject, writing and executing projects |
5- |
To be able to use efficiently the national and international academic resources. |
6- |
To design statistical applications to afford requirements and making improvable studies on them |
7- |
To be able to present the results of scientific studies with writing and orally. |
8- |
To design solution techniques for problems and using appropriate tools for it |
9- |
Takes responsibility individually and as a team member in multi-disciplinary projects, gaining an open-minded, open-minded, constructive and self-confident working discipline. |
10- |
To have knowledge and experience in software commonly used in the fields of statistics |
Exam Regulations & Assesment & Grading
Students are obliged to attend all theory and practice courses and exams in education programs. Attendance is monitored and recorded by the instructor who teaches the course. At least one midterm and a final exam are given each semester. Student assessment methods can take different forms for each course. Assessment is usually done through book open or closed exams, reports, homework, quiz, seminar presentations or oral exams. The instructor may also take into account the attendance status of the student in addition to the course performance and exams while grading. Courses that do not require a midterm and final exam are determined by the department. In such cases, the semester grade is given according to the student's performance during the semester. Exams; It can be written, oral, written-applied and oral-applied. At least one midterm exam is held for each course in the relevant semester. Contribution rates for midterm and final grade are decided by EYK at the beginning of each semester upon the recommendation of the relevant faculty member or lecturers with a doctorate degree whose qualifications are determined by the Senate. Students can take the proficiency exam twice a year, once in the fall and spring semesters. Students who are admitted with a master's degree have to take the proficiency exam until the end of the fifth semester and the student admitted with a bachelor's degree until the end of the seventh semester. Doctoral qualifying exam is held in two parts, written and oral. The questions asked in the oral and written exams and the evaluation are recorded. Written exam success can be evaluated by grades.
Graduation Requirements
The doctorate program required to obtain a doctorate degree; For students admitted with a master's degree, it consists of at least 7 graduate courses, seminar courses, specialization field courses, proficiency exam, thesis proposal and thesis work. The minimum total credit required to graduate from the program is 180 ECTS for students admitted with a master's degree.
The doctorate program consists of fourteen courses, specialization field course, seminar course, proficiency exam, thesis proposal and thesis study, provided that a total of not less than 84 ECTS for students admitted with a bachelor's degree. The minimum total graduation credits of the program is 240 ECTS for students admitted with a bachelor's degree.
Occupational Profiles of Graduates
Statistics department graduates are employed in many fields related to their profession in the public and private sectors. In addition, many graduates work as researchers in their fields.
Access to Further Studies
Graduates who have successfully completed the doctorate program can apply to higher education institutions in the same or similar fields in the country or abroad for an academic position, or for a specialist position in departments suitable for their field of study in public institutions.
Mode of Study
Program Teaching Objectives
- To create basic professional knowledge in statistics, to develop problem solving skills, to have an analytical and holistic perspective and to make analytical thinking a principle.
- To be able to transfer knowledge to the field of application, to use methods, techniques and devices related to the profession. To apply what they have learned at national and international level and to offer solutions to problems.
- Designing research, experimenting, planning, conducting and analyzing the results and interpreting them. To be able to use information technologies and software packages competently at the required level in the field of statistics.
- Students who will graduate as statisticians have the statistical knowledge and skill level required by the age To be able to think analytically and to constantly renew itself; To be able to find solutions to the professional problems they will encounter; It is aimed that the graduates of the department will meet the expectations and needs of the society and the business world and also provide students with national / international professional qualifications.
Programme Director
Prof.Dr. Dursun AYDIN
ECTS Coordinator
Associate Prof.Dr. Eralp DOĞU
Course Structure Diagram with Credits
1. Year
- 1. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
FBE5090
|
Research Methods and Scientific Ethics
|
Required
|
2
|
0
|
2
|
|
FBE5500
|
Research Methods and Scientific Ethics
|
Elective
|
3
|
0
|
6
|
|
İST5505
|
GOAL PROGRAMMING
|
Elective
|
3
|
0
|
6
|
|
İST5511
|
ECONOMETRIC MODELS
|
Elective
|
3
|
0
|
6
|
|
İST5515
|
GENERAL LINEAR MODELS
|
Elective
|
3
|
0
|
6
|
|
İST5525
|
Statistical Software and Data Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5531
|
SAMPLING THEORY
|
Elective
|
3
|
0
|
6
|
|
İST5535
|
REGRESSION THEORY
|
Elective
|
3
|
0
|
6
|
|
İST5537
|
VARIATE PROCESSES
|
Elective
|
3
|
0
|
6
|
|
İST5541
|
ARTIFICIAL NEURAL NETWORKS
|
Elective
|
3
|
0
|
6
|
|
İST5545
|
TIME SERIES ANALYSIS
|
Elective
|
3
|
0
|
6
|
|
İST5547
|
ADVANCED BAYESIAN APPROACHES
|
Elective
|
3
|
0
|
4
|
|
İST5549
|
PROBABILITY THEORY
|
Elective
|
3
|
0
|
6
|
|
İST5551
|
Nonparametric Estimation Methods
|
Elective
|
3
|
0
|
6
|
|
İST5555
|
Categorical Data Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5557
|
Fuzzy Statistical Methods
|
Elective
|
3
|
0
|
6
|
|
İST5559
|
Survival Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5563
|
Measure Theory
|
Elective
|
3
|
0
|
6
|
|
İST5565
|
Advanced Hypothesis Testing
|
Elective
|
3
|
0
|
6
|
|
İST5567
|
Advanced Experimental Design
|
Elective
|
3
|
0
|
4
|
|
İST5569
|
Semiparametric Regression
|
Elective
|
3
|
0
|
6
|
|
İST5571
|
Cluster Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5573
|
Fundamentals of Machine Learning
|
Elective
|
3
|
0
|
6
|
|
İST5575
|
Nonparametric Regression
|
Elective
|
3
|
0
|
6
|
|
İST6001
|
ADVANCED PROBABILITY THEORY
|
Required
|
3
|
0
|
6
|
|
İST6090
|
Seminar
|
Required
|
0
|
2
|
6
|
|
İST6701
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
| | | | | | |
|
1. Year
- 2. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST5502
|
SIMULATION TECHNIQUES AND MODELLING
|
Elective
|
3
|
0
|
6
|
|
İST5504
|
FUZZY LOGIC
|
Elective
|
3
|
0
|
6
|
|
İST5510
|
LINEAR PROGRAMMING
|
Elective
|
3
|
0
|
6
|
|
İST5512
|
SOFT COMPUTING METHODS
|
Elective
|
3
|
0
|
6
|
|
İST5516
|
GENERALISED LINEAR MODELS
|
Elective
|
3
|
0
|
6
|
|
İST5518
|
GRAPH THEORY AND APPLICATIONS
|
Elective
|
3
|
0
|
6
|
|
İST5520
|
HYPOTHESIS TESTS
|
Elective
|
3
|
0
|
6
|
|
İST5524
|
STATISTICAL QUALITY CONTROL
|
Elective
|
3
|
0
|
6
|
|
İST5526
|
DECISION-MAKING AND GAME THEORY
|
Elective
|
3
|
0
|
6
|
|
İST5528
|
MATHEMATICAL STATISTICS
|
Elective
|
3
|
0
|
6
|
|
İST5536
|
SUBSTANTIAL DATA ANALYSIS
|
Elective
|
3
|
0
|
6
|
|
İST5538
|
INTEGER PROGRAMMING
|
Elective
|
3
|
0
|
6
|
|
İST5540
|
DATA MINING
|
Elective
|
3
|
0
|
6
|
|
İST5544
|
OPERATIONAL RESEARCH
|
Elective
|
3
|
0
|
6
|
|
İST5546
|
APPLIED STATISTICS IN NATURAL AND SOCIAL SCIENCES
|
Elective
|
3
|
0
|
4
|
|
İST5552
|
Machine Learning Methods
|
Elective
|
3
|
0
|
6
|
|
İST5554
|
Advanced Time Series
|
Elective
|
3
|
0
|
6
|
|
İST5556
|
Large Data Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5558
|
Dynamic Programming
|
Elective
|
3
|
0
|
6
|
|
İST5560
|
Statistical Programming
|
Elective
|
3
|
0
|
6
|
|
İST5561
|
MULTIVARIATE STATISTICAL METHODS
|
Elective
|
3
|
0
|
6
|
|
İST5562
|
Linear Statistical Models
|
Elective
|
3
|
0
|
6
|
|
İST5564
|
Nonlinear Regression
|
Elective
|
3
|
0
|
6
|
|
İST5566
|
Nonlinear Time Series Analysis
|
Elective
|
3
|
0
|
6
|
|
İST5570
|
Advanced Optimization Techniques
|
Elective
|
3
|
0
|
6
|
|
İST5572
|
Deep Learning
|
Elective
|
3
|
0
|
6
|
|
İST6002
|
ADVANCED MATHEMATICAL STATISTICS
|
Required
|
3
|
0
|
6
|
|
İST6702
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
| | | | | | |
|
2. Year
- 1. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6703
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6810
|
Preparation For the Qualification Examination
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
2. Year
- 2. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6704
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6811
|
Thesis Proposal
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
3. Year
- 1. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6705
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6812
|
PhD.Thesis (1. TIK)
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
3. Year
- 2. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6706
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6813
|
PhD.Thesis (2. TIK)
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
4. Year
- 1. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6707
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6814
|
PhD.Thesis (3. TIK)
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
4. Year
- 2. Term
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
İST6708
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
İST6815
|
PhD. Thesis (Thesis Defense)
|
Required
|
0
|
0
|
24
|
|
| | | | | | |
|
|
Evaluation Questionnaires
Course & Program Outcomes Matrix
1. Year
- 1. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Research Methods and Scientific Ethics | | | | | | | | | | |
Research Methods and Scientific Ethics | | | | | | | | | | |
GOAL PROGRAMMING | 5 | 4 | 3 | 5 | 3 | 5 | 2 | 5 | 5 | 2 |
ECONOMETRIC MODELS | 5 | 4 | 5 | 4 | 3 | 5 | 4 | 5 | 3 | 3 |
GENERAL LINEAR MODELS | 5 | 4 | 5 | 3 | 5 | 5 | 3 | 4 | 5 | 5 |
Statistical Software and Data Analysis | 4 | 4 | 3 | 5 | 4 | 4 | 3 | 5 | 5 | 3 |
SAMPLING THEORY | 3 | 5 | 2 | 4 | 3 | 5 | 4 | 3 | 5 | 4 |
REGRESSION THEORY | 5 | 4 | 4 | 3 | 4 | 5 | 3 | 5 | 3 | 4 |
VARIATE PROCESSES | 4 | 5 | 3 | 5 | 3 | 4 | 5 | 2 | 4 | 5 |
ARTIFICIAL NEURAL NETWORKS | 5 | 3 | 5 | 4 | 2 | 3 | 5 | 5 | 4 | 3 |
TIME SERIES ANALYSIS | 5 | 3 | 5 | 5 | 2 | 3 | 5 | 5 | 2 | 5 |
ADVANCED BAYESIAN APPROACHES | 3 | 5 | 5 | 4 | 5 | 2 | 5 | 4 | | 4 |
PROBABILITY THEORY | 4 | 5 | 3 | 5 | 3 | 4 | 5 | 2 | 4 | 5 |
Nonparametric Estimation Methods | 4 | 5 | 3 | 3 | 2 | 4 | 3 | 3 | 2 | 3 |
Categorical Data Analysis | 2 | 2 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 |
Fuzzy Statistical Methods | 4 | 4 | 3 | 5 | 4 | 4 | 3 | 5 | 5 | 3 |
Survival Analysis | 3 | 3 | 2 | 3 | 4 | 3 | 2 | 3 | 4 | 2 |
Measure Theory | 5 | 5 | 3 | 3 | 4 | 5 | 3 | 3 | 2 | 3 |
Advanced Hypothesis Testing | 4 | 5 | 4 | 3 | 3 | 4 | 4 | 3 | 3 | 4 |
Advanced Experimental Design | 3 | 3 | 2 | 2 | 4 | 3 | 2 | 2 | 2 | 2 |
Semiparametric Regression | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 3 |
Cluster Analysis | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 3 | 3 |
Fundamentals of Machine Learning | 3 | 3 | 3 | 3 | 5 | 3 | 3 | 3 | 5 | 3 |
Nonparametric Regression | 4 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 2 | 4 |
ADVANCED PROBABILITY THEORY | 5 | 5 | 3 | 3 | 2 | 5 | 3 | 3 | 3 | 3 |
Seminar | 5 | 4 | 4 | 4 | 5 | 4 | 3 | 3 | 4 | 2 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
| | | | | | | | | | |
|
1. Year
- 2. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
SIMULATION TECHNIQUES AND MODELLING | 4 | 5 | 3 | 5 | 4 | 4 | 5 | 2 | 4 | 3 |
FUZZY LOGIC | 5 | 3 | 4 | 4 | 5 | 3 | 4 | 4 | 4 | 5 |
LINEAR PROGRAMMING | 3 | 5 | 4 | 5 | 4 | 5 | 3 | 5 | 4 | 4 |
SOFT COMPUTING METHODS | 5 | 3 | 4 | 5 | 4 | 3 | 5 | 5 | 4 | 5 |
GENERALISED LINEAR MODELS | 4 | 5 | 3 | 5 | 4 | 4 | 5 | 3 | 3 | 4 |
GRAPH THEORY AND APPLICATIONS | 3 | 5 | 5 | 5 | 3 | 3 | 5 | 4 | 4 | 3 |
HYPOTHESIS TESTS | 5 | 4 | 3 | 5 | 3 | 5 | 2 | 5 | 5 | 2 |
STATISTICAL QUALITY CONTROL | 5 | 4 | 5 | 2 | 4 | 2 | 5 | 5 | 3 | 5 |
DECISION-MAKING AND GAME THEORY | 3 | 4 | 4 | 3 | 4 | 3 | 3 | 4 | 4 | 4 |
MATHEMATICAL STATISTICS | 4 | 5 | 3 | 5 | 3 | 4 | 5 | 2 | 4 | 5 |
SUBSTANTIAL DATA ANALYSIS | 3 | 5 | 2 | 4 | 3 | 5 | 4 | 3 | 5 | 4 |
INTEGER PROGRAMMING | 5 | 4 | 5 | 3 | 5 | 5 | 3 | 4 | 5 | |
DATA MINING | 4 | 5 | 3 | 5 | 4 | 2 | 4 | 5 | 5 | 4 |
OPERATIONAL RESEARCH | 4 | 4 | 5 | 2 | 4 | 5 | 3 | 3 | 5 | 4 |
APPLIED STATISTICS IN NATURAL AND SOCIAL SCIENCES | 4 | 5 | 3 | 5 | 3 | 4 | 5 | 2 | 4 | 5 |
Machine Learning Methods | 3 | 3 | 2 | 5 | 5 | 3 | 2 | 5 | 5 | 2 |
Advanced Time Series | 4 | 5 | 3 | 4 | 5 | 4 | 3 | 4 | 4 | 3 |
Large Data Analysis | 4 | 3 | 3 | 5 | 5 | 4 | 3 | 5 | 5 | 3 |
Dynamic Programming | 3 | 3 | 2 | 4 | 3 | 3 | 2 | 4 | 4 | 2 |
Statistical Programming | 4 | 4 | 3 | 5 | 4 | 4 | 3 | 5 | 5 | 3 |
MULTIVARIATE STATISTICAL METHODS | 3 | 5 | 5 | 4 | 5 | 5 | 5 | 4 | 3 | 4 |
Linear Statistical Models | 5 | 5 | 4 | 3 | 3 | 5 | 4 | 3 | 3 | 4 |
Nonlinear Regression | 5 | 4 | 4 | 2 | 3 | 5 | 4 | 2 | 3 | 4 |
Nonlinear Time Series Analysis | 5 | 5 | 4 | 2 | 3 | 5 | 4 | 2 | 3 | 4 |
Advanced Optimization Techniques | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 3 | 4 |
Deep Learning | 3 | 3 | 2 | 5 | 4 | 3 | 2 | 5 | 5 | 2 |
ADVANCED MATHEMATICAL STATISTICS | 5 | 5 | 4 | 3 | 4 | 5 | 4 | 3 | 3 | 4 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
| | | | | | | | | | |
|
2. Year
- 1. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
Preparation For the Qualification Examination | 5 | 5 | 4 | 5 | 5 | 5 | 5 | 4 | 4 | 5 |
| | | | | | | | | | |
|
2. Year
- 2. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
Thesis Proposal | 5 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 |
| | | | | | | | | | |
|
3. Year
- 1. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
PhD.Thesis (1. TIK) | 5 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 |
| | | | | | | | | | |
|
3. Year
- 2. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
PhD.Thesis (2. TIK) | 5 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 |
| | | | | | | | | | |
|
4. Year
- 1. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
PhD.Thesis (3. TIK) | 5 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 |
| | | | | | | | | | |
|
4. Year
- 2. Term
Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 |
Special Studies | | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 |
PhD. Thesis (Thesis Defense) | 5 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 5 |
| | | | | | | | | | |
|
|