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SYLLABUS

Computational statistics, 6 ECTS Credits

COURSE CATEGORY   Master´s Programme in Statistics and Data Mining MAIN FIELD OF STUDY   Statistik - STA SUBJECT AREA     COURSE CODE   732A38

AIM OF THE COURSE

After completing the course the students shall be able to: - show knowledge about powerful techniques for simulation, - demonstrate a basic understanding of major numerical principles for the fitting of statistical models to data. - carry out computer experiments involving Monte-Carlo techniques, i.e. the use of random number generation to simulate stochastic phenomena and model outputs. - adapt general principles of computing to specific statistical applications involving linear systems of equations.

CONTENTS

The course aims at enabling insightful selection of computational tools and algorithms in statistics. The course lays the foundation for professional work and research in which advanced computation and computer experiments involving simulation are employed to make inference about data and the performance of statistical methods. Basic principles of random number generation and simulation. Markov Chain Monte Carlo (MCMC) simulation. Numerical linear algebra and optimization for fitting of statistical models to data.

TEACHING

The teaching comprises lectures, computer exercises and seminars. The lectures are devoted to presentations of theories, concepts, and methods. Computer exercises in which the students have access to supervision provide practical experience of data analysis. Seminars are devoted to discussions of the computer exercises and student presentations. Literature readings. Language of instruction: English

EXAMINATION

Assignments encompassing computer-based data analysis. Obligatory attendance of the seminars. One final written examination. Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination. Students who have passed an examination may not retake it in order to improve their grades.

ADMISSION REQUIREMENTS

For acceptance to the course, the student must have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, and computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. The student shall have taken at least one course at advanced level in statistics and a course including including multiple linear regression. Also, the student should have passed a programming course covering at least 6 ECTS credits. Documented knowledge of English equivalent to Engelska B/Engelska 6 internationally recognized test, e.g. TOEFL (minimum scores: Paper based 575 + TWE-score 4.5, and internet based 90), IELTS, academic (minimum score Overall band 6.5 and no band under 5.5), or equivalent.

GRADING

The course is graded according to the ECTS grading scale A-F

CERTIFICATE

Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.

COURSE LITERATURE

The course literature is decided upon by the department in question.

OTHER INFORMATION

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Computational statistics Datorintensiva statistiska metoder   Department responsible for the course or equivalent: IDA - Department of Computer and Information             Registrar No: 1330/06-41   Course Code: 732A38           Exam codes: see Local Computer System       Subject/Subject Area : Statistik - STA                       Level   Education level     Subject Area Code   Field of Education   A1X   Advanced level     STA   SA   The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-11 Latest revision 2013-03-18