Türkçe
Graduate School Of Natural And Applied Sciences İstatistik Doktora

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

0 - Etkisi Yok, 1 - En Düşük, 2 - Düşük, 3 - Orta, 4 - Yüksek, 5 - En Yüksek

1. Year - 1. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Research Methods and Scientific Ethics          
Research Methods and Scientific Ethics          
GOAL PROGRAMMING5435352552
ECONOMETRIC MODELS5454354533
GENERAL LINEAR MODELS5453553455
Statistical Software and Data Analysis4435443553
SAMPLING THEORY3524354354
REGRESSION THEORY5443453534
VARIATE PROCESSES4535345245
ARTIFICIAL NEURAL NETWORKS5354235543
TIME SERIES ANALYSIS5355235525
ADVANCED BAYESIAN APPROACHES3554525434
PROBABILITY THEORY4535345245
Nonparametric Estimation Methods4533243323
Categorical Data Analysis2233323333
Fuzzy Statistical Methods4435443553
Survival Analysis3323432342
Measure Theory5 33453323
Advanced Hypothesis Testing4543344334
Advanced Experimental Design3322432222
Semiparametric Regression4333343333
Cluster Analysis3333433333
Fundamentals of Machine Learning3333533353
Nonparametric Regression4443344324
ADVANCED PROBABILITY THEORY5533253333
Seminar5444543342
Special Studies5445454544
           
1. Year - 2. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
SIMULATION TECHNIQUES AND MODELLING4535445243
FUZZY LOGIC5344534445
LINEAR PROGRAMMING3545453544
SOFT COMPUTING METHODS5345435545
GENERALISED LINEAR MODELS4535445334
GRAPH THEORY AND APPLICATIONS3555335443
HYPOTHESIS TESTS5435352552
STATISTICAL QUALITY CONTROL5452425535
DECISION-MAKING AND GAME THEORY3443433444
MATHEMATICAL STATISTICS4535345245
SUBSTANTIAL DATA ANALYSIS3524354354
INTEGER PROGRAMMING5453553455
DATA MINING4535424554
OPERATIONAL RESEARCH4452453354
APPLIED STATISTICS IN NATURAL AND SOCIAL SCIENCES4535345245
Machine Learning Methods3325532552
Advanced Time Series4534543443
Large Data Analysis4 35543553
Dynamic Programming3324332442
Statistical Programming4435443553
MULTIVARIATE STATISTICAL METHODS3554555434
Linear Statistical Models5543354334
Nonlinear Regression5442354234
Nonlinear Time Series Analysis5542354234
Advanced Optimization Techniques3343434334
Deep Learning3325432552
ADVANCED MATHEMATICAL STATISTICS 5543454334
Special Studies5445454544
           
2. Year - 1. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
Preparation For the Qualification Examination5545555445
           
2. Year - 2. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
Thesis Proposal5545454445
           
3. Year - 1. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
PhD.Thesis (1. TIK)5545454445
           
3. Year - 2. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
PhD.Thesis (2. TIK)5545454445
           
4. Year - 1. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
PhD.Thesis (3. TIK)5545454445
           
4. Year - 2. Term
Ders AdıPy1Py2Py3Py4Py5Py6Py7Py8Py9Py10
Special Studies5445454544
PhD. Thesis (Thesis Defense)5545454445
           
 

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