The course Introduction to computational science provides a broad overview of the fundamentals of computational
science and more broadly the use of computational tools in natural science. Concepts are introduced with the aim
of providing a general understanding of the field and showing the range of available tools and options. Examples
of specific software are provided with focus on open-source software. Practical hands-on exercises offer the
opportunity to practice the concepts introduced in the course and to gain experience in the use of these tools.
Last update: Kopecký Vladimír, RNDr., Ph.D. (16.02.2022)
Course completion requirements
Class credit is given for completing practical exercises and homework. The final grade will be based on a midterm test (25 %), a final test (25 %), and oral examination (50 %).
Last update: Houfek Karel, doc. RNDr., Ph.D. (02.05.2023)
Literature
Nell Dale, John Lewis, Computer science illuminated, Jones & Bartlett Learning, 2020
Joakim Sundnes, Introduction to Scientific Programming with Python, Springer International Publishing, 2020
Lecture notes and other provided material
Last update: Kopecký Vladimír, RNDr., Ph.D. (16.02.2022)
Course assessment methods and requirements for successful completion, grading scheme
The requirements for the exam correspond to the course syllabus to the extent that was given in the lectures.
Last update: Houfek Karel, doc. RNDr., Ph.D. (02.05.2023)
Syllabus
Modern computer architecture, personal computers, workstations, supercomputers
Operating systems, history, currently available systems, user interface types, command line
Networking, the Internet, encrypted communication, using remote computers
Fundamentals of programming and software development, types of programming languages, programming paradigms, program flow control, data structures
Computer algebra systems and symbolic manipulation
Numerical computation
Interactive computing, notebook-style interface
Data processing and plotting
Sharing code and data, version control systems, repositories
Computer graphics, vector and raster images
Desktop publishing, preparing publication-quality documents, presentations, plots, and graphics
Tools for live online collaboration
Parallelization and high-performance computing
Machine learning, artificial intelligence
Last update: Maršálek Ondřej, RNDr., Ph.D. (03.10.2023)
Learning outcomes
By the end of this course, students will be able to use and apply in practice the following tools of computational science:
Modern computer architecture, personal computers, workstations, supercomputers
Operating systems, history, currently available systems, user interface types, command line
Networking, the Internet, encrypted communication, using remote computers
Fundamentals of programming and software development, types of programming languages, programming paradigms, program flow control, data structures
Computer algebra systems and symbolic manipulation
Numerical computation
Interactive computing, notebook-style interface
Data processing and plotting
Sharing code and data, version control systems, repositories
Computer graphics, vector and raster images
Desktop publishing, preparing publication-quality documents, presentations, plots, and graphics
Tools for live online collaboration
Parallelization and high-performance computing
Machine learning, artificial intelligence
Last update: Houfek Karel, doc. RNDr., Ph.D. (07.07.2026)
