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To become an educational institution for master program and developing statistics and data science international standard that contribute to science and technology, particularly in the fields of Computing, Business and Industry, Economics and Finance, Social and Population, and Environment and Health.
Program Educational Objectives (PEO) reflects the achievements of graduates of the study program. The PEO of the Master Program of Statistics is to produce quality Masters in Statistics so that they can have a career as lecturers, researchers, and practitioners in the field of Statistics and Data Science with the following characteristics:
Literature review, scientific writing training, revision
International Seminar
(writing paper, submission and revision, attending and presenting)
The study load for the Master Programme in Statistics is 38 credits (SKS), equivalent to 68.4 ECTS, and is normally completed over four semesters. The curriculum consists of 25 credits of compulsory courses, 6 credits of elective courses, 2 credits for the Thesis Proposal, and 5 credits for the Master’s Thesis. In the Regular Track, the study load is distributed across four semesters: 14 credits in Semester I, 12 credits in Semester II, 7 credits in Semester III, and 5 credits in Semester IV. In addition to the formal curriculum, students undertake supplementary academic activities supporting thesis preparation, scientific publication, and English proficiency. These activities comprise thesis support work, international seminar and publication activities, and TOEFL preparation, corresponding to an additional workload of 14 credit unitsThe detailed distribution of courses for each semester is presented below and is also available on the official curriculum webpage of the Master Programme in Statistics.
ITS Thesis Writing Guidelines 2021
This section presents a range of academic achievements and scholarly works produced by Master’s Program of Statistics students during their studies in the Department of Statistics. It includes scientific publications, journal articles, seminar contributions, and other academic outputs that reflect students’ learning, research activities, and contributions to the advancement of statistical knowledge.