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The introductory Python course provides the students with the programming basics needed for data processing
and visualization. Focus on scientific applications allows the students to use the acquired knowledge right away for
both study purposes and practical applications. Python is currently one of the most popular languages widely used
in science. Thanks to its simple syntax it is well suited for beginners.
Last update: Kudrnová Hana, Mgr. (21.04.2021)
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Learn the basics of programming in the Python language with a focus on mathematical and physical applications, mainly data processing and visualization. Last update: Belda Michal, doc. Mgr., Ph.D. (20.04.2021)
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For credit, a student may either submit three shorter Python programs over the course of the semester. Alternatively, one longer program may be submitted at the end of the semester. Last update: Belda Michal, doc. Mgr., Ph.D. (20.04.2021)
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Python Software Foundation: Python Documentation. https://www.python.org/doc/ Pilgrim, M.: Dive into Python 3. https://diveintopython3.problemsolving.io/ Last update: Belda Michal, doc. Mgr., Ph.D. (20.04.2021)
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The course is realized as a lecture and practical exercises (bring your own laptop if you can). Last update: Belda Michal, doc. Mgr., Ph.D. (20.04.2021)
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Introduction to Python: language basics, history and versions (2 and 3), comparison to other languages; Python philosophy (short readable code, batteries included)
IPython console, Jupyter notebooks; integrated development environments and Python distributions; short simple single-purpose scripts
Python building blocks: syntax, variables, data types, builtins; procedural programming basics - loops, conditions, functions; syntactic sugar - do more with less code
Libraries: builtin libraries and modules, extensions.
Scientific computing: NumPy and SciPy libraries for processing vector and matrix data, statistics; processing tabular data with pandas
Input/Output: formatting, file formats, reading and writing files; specialized libraries for data used in math and physics
Visualization: creating graphs using matplotlib, seaborn and pandas
Object-oriented programming: classes, objects, attributes, methods, encapsulation, inheritance; error handling
Code optimization: NumPy, cython, parallelization
Graphical User Interface: basics of GUI using builtin libraries Last update: Belda Michal, doc. Mgr., Ph.D. (20.04.2021)
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