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Lecturer(s)
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Gruber Ansgar, Dr. rer. nat.
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Vohnoutová Marta, Ing.
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Course content
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Content of lectures 1. Linux - the basic platform 2. Jupyter Notebook and Jupyterhub - documentation and programming in one 3. Itertools - library for combinatorics 4. Numpy - a tool for numerical mathematics and multidimensional matrices 5. Scipy - algorithms for interpolation and integration 6. Pandas - a tool for data analysis 7. Matplotlib and Seaborn - frameworks for creating graphs 8. Parallel Python in a nutshell - optimization of calculations 9. Fundamentals of object programming in Python Content of practicing Work with data obtained from the real environment of the university or companies. Applying discussed Python libraries to data processing.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Demonstration, Individual preparation for exam
- Class attendance
- 56 hours per semester
- Preparation for classes
- 10 hours per semester
- Preparation for exam
- 9 hours per semester
- Preparation for credit
- 25 hours per semester
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Learning outcomes
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The aim of the course is to learn how to use the extensive capabilities of Python libraries, mainly with a focus on numerical and scientific calculations, big data, biological data analysis and also on displaying data in the form of various graphs. The libraries that the teaching will be focused on are: - Itertools - Numpy - Scips - Matplotlib - Seaborne - Pandas Work with applied Python libraries will be focused on gaining practical experience, and the processed data will be taken over mainly from practice based on the requirements of the individual teams of the University of South Bohemia. The course will also include a semester project, which will always be intended for teams of students. The individual teams will have to defend the prepared Semester project in order to be awarded credit.
By the end of this course, students will: 1. Understand Python's advanced capabilities for iterative processes and data handling 2. Use visualization libraries to effectively communicate data insights. 3. Leverage scientific computing libraries for mathematical modeling and problem-solving. 4. Implement parallel computing for optimizing performance. 5. Apply Python in biological data analysis and bioinformatics.
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Prerequisites
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UAI 735I/UAI 673/UAI 655 Python Basics/Introduction to Python for AI or similar
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Assessment methods and criteria
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Student performance assessment, Combined exam, Analysis of the qualification work
The condition for credits is 80% attendance (or appologized) and 80% of homeworks (We will create a credit system of 100 points max - it means that you must have 80 points from homeworks minimum to be. Elaboration of a team semester project [The situation can be can checked in moodle.]
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Recommended literature
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