M.Sc. Programme in Statistics

Academic UnitDepartment of Statistics
Programme DirectorAli Hakan Büyüklü
TypeMaster Program
Level Of QualificationThis is a Second Cycle (Master’s Degree) Program
Qualification AwardedThe students who successfully complete the program are awarded the degree of Master of Science (M.S.) in M.Sc. Programme in Statistics
Mode Of StudyFull-Time
Specific Admission RequirementsThe general achievement score for all candidates who apply to this programs is calculated by the candidate assessment judges by taking the %60 of ALES the %20 of the CGPA of undergraduate program and the %20 of the entrance exam into account. Candidates are put in an order of achievement and accepted within the limits of the contingency. The admission requirement details a candidate should provide are stated in YTU Regulations on Graduate Studies Article 10 (4-a). For further information, please refer to: http://www.fbe.yildiz.edu.tr/haberler.php?id=121
Specific Arrangements For Recognition Of Prior LearningA student can only be exempt from maximum four previous elective courses in which they have been successful in being a special student, transfer from another university or another master program that they no longer have an enrollment.
Qualification Requirements And RegulationsThe graduate students in this program must complete a minimum of 21 local credits (7 courses), a seminar course and a thesis; they must be successful in all of the courses with a minimum achievement grade of CB, must have completed 90-120 ECTS credits and have scored a minimum CGPA of 2.50/4.00 to qualify for graduation.
Profile Of The ProgrammeThe aim of the master program is to provide the students to gain ability of access, evaluation and interpretation of knowledge carrying out scientific research. This program consists of eight courses those are totally at least twenty four credits, a seminar and a thesis study.
Occupational Profiles Of Graduates With ExamplesGraduates of the department can find job opportunities in both the private and public sector. They may apply for posts in the Turkish Statistical Institute, as well as other establishment such as Department of Planning. Other area of work; Research, Finance, Banking, ISE, planning department of the big firm. Thirty percent of our courses are conducted in English enabling our students to develop their language skills and increasing their opportunities to find employment in a variety of different sectors.
Access To Further Studies The graduates of this program can apply to Ph.D. programs to enhance their academic skills and their career.
Examination Regulations Assessment And Grading

(1) A student has to attend at least 70 per cent of the courses he has added. 

(2) In one semester, there must at least be two measurements of success. One of these must be a written exam by all means at the discretion of relevant faculty member. In case of on written examination, the other assessment could be an assignment, project, laboratory report or similar kinds of assessment.

(3)  At the end of the semester, a final exam on the entire course is administered. Achievement grade is calculated taking the work during the semester with a percentage between 40 and 60 and the final exam with a percentage between 60 and 40 into consideration. In case of failure, except for F0, resit exam is granted to the student.

(4) Achievement grades are defined as follows:


Percentage Points AchievementCoefficient
90-100AA 4.00
80-89 BA 3.50
70-79BB 3.00
60-69CB 2.50
50-59 CC 2.00
30-39DD 1.00

b) Grades not included in the Average Scores:

1) G: Pass/Successful,

2) K: Fail/Unsuccessful,

3) M: Exemption,

4) E: Incomplete

(5)  Minimum achievement grade to be successful in a course is CB (2.50).

(6) A student can only be successful in all courses if he has scored a minimum GPA of 2.50.

(7) The student who has scored CC, DC, DD, FD, FF and F0 are considered to have failed the course. These grades are included in his CGPA (AGNO). 

 (8) G (Pass/Successful) grade indicates that the student has been successful / satisfactory in a course or activity. K (Fail/Unsuccessful) grade indicates that the student has been unsuccessful / unsatisfactory in a course or activity. M (Exemption) grade indicates that the student have exemption for the previous program courses which are deemed equivalent to the courses offered in the program. Decision for the course exemption is made by the relevant faculty committee. G, K and M grades aren’t included in the CGPA (AGNO). E (Incomplete) grade indicates that the faculty member who carries out the course hasn’t entered the grade into the automation system. These grades are entered into the system by the decision of the execute board of the institute. 

Graduation RequirementsThe graduate students in this master program must complete a minimum of 21 local credits (7 courses), a seminar course and a thesis; they must be successful in all of the courses with a minimum achievement grade of CB, must have completed 120 ECTS credits and have scored a minimum GPA of 2.50/4.00 to qualify for graduation.

Program Outcomes

  1. To develop basic probability and statistics theories and knowledge on their practice which are based on competency of the bachelor level to expertise level
  2. To be able to use advanced theoretical and practical knowledge on theoretical and applied statistics
  3. To be able to define and analyze problems concerning the field and propose scientific solutions to them
  4. To be able to apply theoretical and applied statistics methods in real life and to discover self-potentials in application through an interdisciplinary approach
  5. To be able to conduct an independent study requiring expertise on almost each field in which Statistical Methods are used
  6. To be able to assess and renew knowledge and skills on applied statistics at the expertise level through a critical approach
  7. To be easily able to transfer theoretical and technical information both in a detailed way to experts and in a simple and understandable way to non-experts
  8. To be able to use national and international (English)academic sources in an effective way and to update self knowledge on the subject, to easily communicate with domestic and foreign colleagues, to follow the periodic literature, to systematically transfer information to groups from the field
  9. To be familiar with a common software used in statistics field and able to use at least one of them effectively
  10. To be able to behave in accordance with the social, scientific and ethical values in all the projects they are included, and to carry out projects developing in the framework social sensitivity
  11. To be able to effectively assess all processes by being globally equipped and aware and to have and use sufficient awareness on quality management, job safety and environment for the sake of the society
  12. To be able to link abstract thought that one has to concrete events and to transfer the solutions and examine and interpret the results scientifically by forming experiments and collecting data
  13. To be able to develop plans, policies and strategies about topics and systems in which Applied Statistics are used, and to evaluate and apply the given results within the framework of quality processes
  14. To be able to assess and discuss important people, events and phenomena which played a role in the development of the Science of Statistics in terms of their impacts on the development of other sciences
  15. To maintain a study in the field of Theoretical and Applied Statistics as an individual or in a teamwork, to be effective in any phases of independent study, to participate in the process of decision making, to make and carry out the necessary planning using the time efficiently
1.Year - Fall Semester
CodeReq. Title Lecture Practical Laboratory Local Credit ECTS
SEC001 Elective 130037.5
SEC0002 Elective 230037.5
SEC0003 Elective 330037.5
SEC0004 Elective 430037.5
30 Total:
1.Year - Spring Semester
CodeReq. Title Lecture Practical Laboratory Local Credit ECTS
SEC0005 Elective 530037.5
SEC0006 Elective 630037.5
SEC0007 Elective 730037.5
IST5001 Seminar01007.5
30 Total:
2.Year - Fall Semester
CodeReq. Title Lecture Practical Laboratory Local Credit ECTS
IST5000 M.Sc. Thesis010030
30 Total:
2.Year - Spring Semester
CodeReq. Title Lecture Practical Laboratory Local Credit ECTS
IST5000 M.Sc. Thesis010030
30 Total:
120 Program Total ECTS:
Elective 1, 2, 3, 4, 5, 6 and 7 Courses
CodeReq. Title Lecture Practical Laboratory Local Credit ECTS
IST5101 Bayesian Data Analysis 30037.5
IST5113 Computer applications for statistics30037.5
IST5105 Multivariate Statistical Analysis30037.5
IST5114 Mathematical Methods in Statistics30037.5
IST5115 Nonlinear Programming30037.5
IST5117 Advanced Methods of Marketing Research30037.5
IST5103 Application of Biostatistics30037.5
IST5116 Games an Decision Making30037.5
IST5110 Advanced Regression Analysis 30037.5
IST5106 Multivariate Statistical Methods30037.5
IST5119 Data Envelopment Analysis and Applications30037.5
IST5102 Queing Theory30037.5
IST5118 Applied Time Series 30037.5
IST5109 Advanced Actuarial Techniques 30037.5
IST5108 Survival and Event History Analysis30037.5
IST5112 Statistical Programming30037.5
IST5107 Econometric Models and Statistical Tools30037.5
IST5120 Artifical Intelligence30037.5
IST5104 Multilevel Statistical Models30037.5
IST5111 Statistical Validity and Reliability30037.5

Course & Program Outcomes Matrix

Program Outcomes
Code Title123456789101112131415
IST5113Computer applications for statistics343433355434433
IST5105Multivariate Statistical Analysis443434444334344
IST5117Advanced Methods of Marketing Research434444344343333
IST5110Advanced Regression Analysis 555544354242133
IST5106Multivariate Statistical Methods443444344343433
IST5119Data Envelopment Analysis and Applications345544342425534
IST5102Queing Theory345544342425534
IST5118Applied Time Series 233443344223344
IST5104Multilevel Statistical Models555544553354255
IST5111Statistical Validity and Reliability555544553354255
IST5109Advanced Actuarial Techniques 333422433333333
IST5103Application of Biostatistics23343454344-435
IST5114Mathematical Methods in Statistics555444333433443
IST5115Nonlinear Programming333534333324333
IST5116Games an Decision Making334434334334543
IST5108Survival and Event History Analysis333433345444435
IST5101Bayesian Data Analysis 555544354242133
IST5107Econometric Models and Statistical Tools232334314323323
IST5120Artifical Intelligence445342434345525
IST5112Statistical Programming345531245323315
IST5000M.Sc. Thesis444444444444444


TheoriticPracticalConceptionalPracticalQualification of Independent Working and Taking ResponsibilityLearning QualificationCommuncation and Social QualificationDomain-specific Qualification