Computer Modelling and Simulation Methods 510-IS1-3MSK-23
Profile of studies: general academic
Mode of studies: full‑time
Type of course: elective
Field: natural sciences; discipline: computer science
Year / semester: 3 / —
Prerequisites (sequential system of courses and examinations): none
Introductory courses: Mathematical Analysis, Differential and Difference Equations, Probabilistic Methods and Statistics
Lecture: 15 hours
Laboratory: 30 hours
Teaching methods:
– Expository methods: lecture, description with demonstration
– Activating methods: laboratory exercises
ECTS credits: 4
Student workload (workload balance):
Participation in classes:
– lecture: 15 hours
– laboratory: 30 hours
Preparation for classes: 8 hours
Review of literature: 4 hours
Reports, assignments, homework: 18 hours
Preparation for laboratory assessment: 8 hours
Preparation for the exam: 8 hours
Exam duration: 2 hours
Consultation hours: 6 hours
Quantitative indicators:
– student workload associated with classes requiring direct contact with the instructor: 53 hours, 2.1 ECTS
– student workload not requiring direct contact with the instructor: 46 hours, 1.9 ECTS
Type of course
Prerequisites
Mathematical Analysis 2
Mathematical Analysis 3
Probabilistic Methods and Statistics
Python Programming
Differential and Difference Equations
Course coordinators
Learning outcomes
(Knowledge) The student knows:
– the stages of system modelling — KP6_WG1, KP6_WG4
– the process of constructing a simulation model — KP6_WG4, KP6_WG8
– methods of modelling and computer simulation — KP6_WG1, KP6_WG4, KP6_WG8
(Skills) The student is able to:
– analyse a selected process or phenomenon and construct its mathematical model — KP6_UW2
– design an algorithm for a given problem and implement it in a selected programming language — KP6_UW7, KP6_UK3
– develop a complete simulation model of a selected phenomenon/process, perform a computer simulation, and experiment with parameter selection — KP6_UW7, KP6_UK2, KP6_UK3, KP6_UW21
(Social competences) The student:
– approaches problem‑solving in system modelling creatively — KP6_UU1, KP6_KK1, KP6_KO1
Assessment criteria
General form of assessment: examination
Laboratory: tests or project/class reports
Bibliography
Primary literature (core reading)
1. Gągolewski, M. (2021). Data Wrangling with R. Springer.
2. Grolemund G., Hands-On Programming with R, O’Reilly (the book is available on the website https://rstudio-education.github.io/hopr/index.html) access [2026-04-18].
3. Gągolewski, M. (2016). Programming in R: Data Analysis, Computations, Simulations. PWN Scientific Publishers.
Recommended supplementary literature:
4. Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Pearson.
– One of the most widely used textbooks on simulation modelling.
5. Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw‑Hill.
– A classic reference for simulation methodology and statistical foundations.
6. Ross, S. M. (2014). Introduction to Probability Models (11th ed.). Academic Press.
– Standard reference for stochastic modelling and probabilistic systems.
7. Fishman, G. S. (2001). Discrete-Event Simulation: Modeling, Programming, and Analysis. Springer.
– Rigorous treatment of simulation theory and implementation.
8. Shannon, R. E. (1975). Systems Simulation: The Art and Science. Prentice‑Hall.
– Foundational text on simulation as a modelling methodology.
9. Higham, D. J., & Higham, N. J. (2016). Mathematical Modelling: A Case Studies Approach. SIAM.
– Modern introduction to modelling with practical examples.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: