Course syllabus for Stochastic data processing and simulation

Course syllabus adopted 2021-02-12 by Head of Programme (or corresponding).

Overview

  • Swedish nameStatistisk databehandling
  • CodeTMS150
  • Credits7.5 Credits
  • OwnerMPENM
  • Education cycleSecond-cycle
  • Main field of studyMathematics
  • DepartmentMATHEMATICAL SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 20126
  • Open for exchange studentsYes

Credit distribution

0103 Project 7.5 c
Grading: TH		7.5 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

A basic course in mathematical statistics. Some programming experience in MATLAB, Python or R

Aim

The main goal of the course is to introduce the student to some important mathematical and statistical programming languages, via work on concrete mathematical and mathematical statistical problems.

Learning outcomes (after completion of the course the student should be able to)

  • be able to use these programming languages as natural tools in later courses;
  • have developed problem solving skills;
  • be able to move between analytical and numerical problem solving methods with
    the use of a computer;
  • be able to write mathematical reports using LaTeX.

Content

The core of the course are several projects in different areas of mathematical statistics and its applications (e.g., finance, bioinformatics).  Each project contains a number of problems to be solved in a given programming language, e.g., Matlab, Python, and R.  The projects are presented at lectures and programming languages are introduced during teacher led laboratories. The project reports are to be written in LaTeX.

Organisation

Lectures that introduce the projects. Demonstrations of computer programs. Supervision of projects.

Literature

Mainly handouts. But see the course homepage before the course starts.

Examination including compulsory elements

Written reports of the results of the work with the projects. The grading is based on how the problems in the projects are solved and reported.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.