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Lecturer(s)
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Machulka Radek, Mgr. Ph.D.
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Course content
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* Editing, execution, and management of source code; working with documentation * Data analysis, numerical and statistical methods, multidimensional arrays * Interpolation, data fitting, linear algebra, random number generation * Data visualization and real-time monitoring * Optimization techniques; parallel and distributed computing * Communication protocols and data acquisition * Working with data formats * Development of user interfaces * Machine learning
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Demonstration
- Attendace
- 39 hours per semester
- Homework for Teaching
- 13 hours per semester
- Preparation for the Exam
- 39 hours per semester
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Learning outcomes
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This course introduces students to advanced programming techniques with a focus on their application in scientific and laboratory settings. Emphasis is placed on data acquisition, numerical processing, statistical analysis, and effective data visualization. Students will also explore optimization strategies, including parallel processing, use of lower-level programming languages, distributed computing, and GPU acceleration. While Python serves as the primary programming language, the course emphasizes general principles and methodologies for solving practical, real-world problems across various scientific domains.
Upon successful completion of the course, students will acquire the skills necessary to develop practical software tools commonly used in scientific and laboratory practice. These skills are often essential for conducting research and completing thesis projects.
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Prerequisites
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Students are expected to have a basic understanding of programming and algorithm design, equivalent to the completion of the course SLO/UPROG.
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Assessment methods and criteria
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Final project
Active participation in class. Completion of assigned homeworks.
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Recommended literature
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