Course syllabus for Stochastic data processing and simulation

Course syllabus adopted 2026-02-26 by Head of Programme (or corresponding).

Overview

  • Swedish nameStatistisk databehandling
  • CodeTMS150
  • Credits7.5 Credits
  • OwnerTKTEM
  • 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

    59114

  • Open for exchange students

    Yes

Credit distribution

Module
Sp1
Sp2
Sp3
Sp4
Summer
Not Sp
Examination dates
0103 Project 7.5 c
Grading: TH		7.5 c

In programmes

Examiner

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

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

Course specific prerequisites


Knowledge equivalent to an introductory course in probability theory and basic mathematical statistics is required. Prior experience of using a high-level programming language for technical or scientific computation is required.

Aim


The aim of the course is to provide students with the ability to analyse and simulate stochastic and statistical models using computational methods. The course integrates concepts from probability and mathematical statistics with numerical implementation, analysis, and interpretation of results. The course also aims to develop the student’s ability to communicate mathematical and statistical reasoning clearly in written form.

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

  • apply fundamental concepts from probability theory and mathematical statistics in the analysis of stochastic models relevant to data processing and simulation
  • implement statistical and stochastic methods to carry out simulations and data analyses
  • interpret and assess numerical results in relation to underlying mathematical assumptions
  • combine analytical reasoning and computational approaches in the solution of statistical problems
  • document and communicate mathematical and statistical work clearly and coherently in written form using appropriate mathematical notation.

Content


The course covers computational and analytical work in stochastic modelling, simulation, and statistical data processing within mathematical statistics and its applications. The content includes work with probabilistic models, simulation techniques, and statistical methods, with emphasis on the relationship between theory, computation, and interpretation of results.

Organisation


Teaching may include lectures, computer-based teaching activities, and supervised work

Literature

The course literature will be announced no later than 12 weeks before the start of the course

Examination including compulsory elements


Written and oral examination based on project work. 

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 about disability study support.