Qualification Awarded
Master of Science Degree
Specific Admission Requirements
1) Having an undergraduate degree from a national university or from an abroad university the degree of which is accepted by YÖK (Council of Higher Education) 2) Having a standard score of at least 55 from ALES or at least an equivalent score from the exams recognized by council of inter university
Qualification Requirements
The programme consists of a minimum of 7 courses delivered within the graduate programme of the department and in related fields, one seminar course, and thesis, with a minimum of 21 local credits. Students must register for thesis work and the Specialization Field course offered by his supervisor every semester following the semester, in which the supervisor is appointed. A student who has completed work on the thesis within the time period, must write a thesis, using the data collected, according to the specifications of the Graduate School Thesis Writing Guide. The thesis must be defended in front of a jury.
Recognition of Prior Learning
Recognition of prior learning is at the beginning stage in the Turkish Higher Education System. Mugla Sıtkı Koçman University and hence the Department of Statistics is no exception to this. However, exams of exemption are organised at the start of each term at the University for courses compulsory in the curriculum, such as Foreign Languages and Basic Computing. The students who have completed the learning process for these courses on his/her own or through other means, and believe that they have achieved the learning outcomes specified are given the right to take the exemption exam. The students who achieve a passing grade from these exams are held exempt from the related course in the curriculum, and this grade is entered into the transcript of the student.
History
The Department of Statistics and Computer Science was founded as a major within the Faculty of Arts and Science in 1994. The department turned into Statistics Depertment in 2007. There are two formal education programs in the Department of Statistics, primary and secondary education. Moreover, there is also Master's program in our Department.
Profile of the Programme
The Department of Statistics offers graduate courses to its own graduate students and to graduate students in other departments. In the Statistics Department the work done on theses is based on research. Depending on the topic selected, the thesis topic could involve research into linear and nonlinear models, econometry, biostatistics, statistical quality control, regression, experimental designs, multivariate statistical analysis, fuzzy anlaysis.
Program Outcomes
| 1- |
To develop one's knowledge about statistical theory and its applications at the proficiency level based on competencies of undergraduate level |
| 2- |
To be able to use the acquired advanced level of knowledge in the fields of theoretical and applied statistics |
| 3- |
To be able to identify problems, analyze them and produce solutions based on scientific methods |
| 4- |
To be able to apply methods of theoretical and applied statistics in real life by an interdisciplinary approach |
| 5- |
To be able to conduct an study which needs some expertise in the fields where statistical methods are used |
| 6- |
To be able to assess cricitically advanced level knowledge and skills gained in applied statistics |
| 7- |
To be able to communicate easily theoretical and technical knowledge with the relevant people |
| 8- |
To be able to use national and international academic references |
| 9- |
To have knowledge and experience about the software packages commonly used in statistics |
| 10- |
To be able to design methods of solution specific to problem and use appropriate tools in doing so. |
| 11- |
To take responsibility as a individual or a member of team in the applied and theoretical studies |
Exam Regulations & Assesment & Grading
The Master Degree programme consists of a minimum of seven courses, with a minimum of 21 national credits. Each course is assessed via a midterm exam and a final end-of-term exam, with contributions of 40%, 60% respectively. Student must achieve a CGPA of at least 2.5 out of 4.00 and prepared and successfully defended a thesis are given Master Degree in the field of Mathematics.
Graduation Requirements
The Master Degree programme consists of a minimum of seven courses, with a minimum of 21 national credits, a qualifying examination, a dissertation proposal, and a dissertation. The seminar course and thesis are non-credit and graded on a pass/fail basis. The total ECTS credits of the programme is 240 ECTS. Students must register for thesis work and the Specialization Field course offered by his supervisor every semester following the semester, in which the supervisor is appointed. A student who has completed work on the thesis within the time period, must write a thesis, using the data collected, according to the specifications of the Graduate School Thesis Writing Guide. The thesis must be defended in front of a jury.
Occupational Profiles of Graduates
If the graduates have formation and get KPSS Marks, they can be appointed as statisticians or civil servant sin government institutions. The graduatues also can find jobs in financial institutions such as banks. On computer sector they can work in diferent positions. The students who are in graduate education can be researcher and researcher assistants in universities.
Access to Further Studies
Graduates who succesfully completed Master degree may apply to both in the same or related disciplines in higher education institutions at home or abroad to get a position in academic staff or to governmental R&D centres to get expert position.
Mode of Study
Graduate Education
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
|
Project Development and Management
|
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
|
|
|
İST5701
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
| | | | | | | |
|
|
1. Year
- 2. Term
|
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
|
İST5090
|
Seminar
|
Required
|
0
|
2
|
6
|
|
|
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
|
|
|
İST5702
|
Specialization Field Course
|
Required
|
4
|
0
|
6
|
|
| | | | | | | |
|
|
2. Year
- 1. Term
|
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
|
İST5703
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
|
İST5801
|
M.Sc. Thesis
|
Required
|
0
|
0
|
24
|
|
| | | | | | | |
|
|
2. Year
- 2. Term
|
Course Unit Code
|
Course Unit Title
|
Course Type
|
Theory
|
Practice
|
ECTS
|
Print
|
|
İST5704
|
Special Studies
|
Required
|
4
|
0
|
6
|
|
| | | | | | | |
|
| |
Evaluation Questionnaires
Course & Program Outcomes Matrix
|
1. Year
- 1. Term
| Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 | Py11 |
| Research Methods and Scientific Ethics | | | | | | | | | | | |
| Project Development and Management | | | | | | | | | | | |
| 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 | 5 |
| 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 | 3 | 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 | 3 |
| Categorical Data Analysis | 2 | 2 | 3 | 3 | 3 | 2 | 3 | 3 | 3 | 3 | 3 |
| Fuzzy Statistical Methods | 4 | 4 | 3 | 5 | 4 | 4 | 3 | 5 | 5 | 3 | 5 |
| Survival Analysis | 3 | 3 | 2 | 3 | 4 | 3 | 2 | 3 | 4 | 2 | 3 |
| Measure Theory | 5 | 5 | 3 | 3 | | 5 | 3 | 3 | 2 | 3 | 3 |
| Advanced Hypothesis Testing | 4 | 5 | 4 | 3 | 3 | 4 | 4 | 3 | 3 | 4 | 3 |
| Advanced Experimental Design | 3 | 3 | 2 | 2 | 4 | 3 | 2 | 2 | 2 | 2 | 2 |
| Semiparametric Regression | 4 | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 3 | 3 |
| Cluster Analysis | 3 | 3 | 3 | 3 | 4 | 3 | 3 | 3 | 3 | 3 | 3 |
| Fundamentals of Machine Learning | 3 | 3 | 3 | 3 | 5 | 3 | 3 | 3 | 5 | 3 | 3 |
| Nonparametric Regression | 4 | 4 | 4 | 3 | 3 | 4 | 4 | 3 | 2 | 4 | 3 |
| Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 5 |
| | | | | | | | | | | | |
|
|
1. Year
- 2. Term
| Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 | Py11 |
| Seminar | 5 | 4 | 4 | 4 | 5 | 4 | 3 | 3 | 4 | 2 | 5 |
| 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 | 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 | | 5 | 2 | 4 | 5 | |
| Machine Learning Methods | 3 | 3 | 2 | 5 | 5 | 3 | 2 | 5 | 5 | 2 | 5 |
| Advanced Time Series | 4 | 5 | 3 | 4 | 5 | 4 | 3 | 4 | 4 | 3 | 4 |
| Large Data Analysis | 4 | 3 | 3 | 5 | 5 | 4 | 3 | 5 | 5 | 3 | 5 |
| Dynamic Programming | 3 | 3 | 2 | 4 | 3 | 3 | 2 | 4 | 4 | 2 | 4 |
| Statistical Programming | 4 | 4 | 3 | 5 | 4 | 4 | 3 | 5 | 5 | 3 | 5 |
| 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 | 3 |
| Nonlinear Regression | 5 | 4 | 4 | 2 | 3 | 5 | 4 | 2 | 3 | 4 | 2 |
| Nonlinear Time Series Analysis | 5 | 5 | 4 | 2 | 3 | 5 | 4 | 2 | 3 | 4 | 2 |
| Advanced Optimization Techniques | 3 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 3 | 4 | 3 |
| Deep Learning | 3 | 3 | 2 | 5 | 4 | 3 | 2 | 5 | 5 | 2 | 5 |
| Specialization Field Course | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 5 |
| | | | | | | | | | | | |
|
|
2. Year
- 1. Term
| Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 | Py11 |
| Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 5 |
| M.Sc. Thesis | | | | | | | | | | | |
| | | | | | | | | | | | |
|
|
2. Year
- 2. Term
| Ders Adı | Py1 | Py2 | Py3 | Py4 | Py5 | Py6 | Py7 | Py8 | Py9 | Py10 | Py11 |
| Special Studies | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 4 | 5 |
| | | | | | | | | | | | |
|
| |