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Lecturer(s)
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Urbánek Tomáš, Ing. Ph.D.
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Course content
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"Introduction to programming, basics of syntax "Variables and expressions "Basic data types and collections "Conditions, cycles and iterations "Function "Standard libraries "Python modular system "Working with files (I / O) "Object Oriented Programming "Python as a data analysis tool "Demonstrations of using advanced features
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Learning activities and teaching methods
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- Participation in classes
- 10 hours per semester
- Preparation for course credit
- 29 hours per semester
- Home preparation for classes
- 36 hours per semester
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| learning outcomes |
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| Knowledge |
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| Explains the concept of programming language syntax |
| Explains the concept of programming language syntax |
| Clarifies the differences between data types |
| Clarifies the differences between data types |
| Defines the principle of procedural programming |
| Defines the principle of procedural programming |
| Explains the principle of functions |
| Explains the principle of functions |
| Clarifies the advantages of data processing when using a programming language |
| Clarifies the advantages of data processing when using a programming language |
| Skills |
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| Uses the IDLE programming environment |
| Uses the IDLE programming environment |
| Installs additional modules |
| Installs additional modules |
| Uses the built-in functions of the Python programming language and standard libraries |
| Uses the built-in functions of the Python programming language and standard libraries |
| Grasps the basic constructions of the Python programming language |
| Grasps the basic constructions of the Python programming language |
| Develops a functional application |
| Develops a functional application |
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Recommended literature
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HILPISCH, Y. Python for Finance: Mastering Data-Driven Finance. 2nd. O'Reilly Media, 2018. ISBN 978-1492024330.
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LUTZ, M. Learning Python. Fifth edition. Beijing: O'Reilly,, 2013. ISBN 978-1449355739.
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MCKINNEY, W. Python for data analysis: data wrangling with pandas, NumPy, and IPython. Second edition.. Sebastopol, California: O'Reilly Media,, 2017. ISBN 978-1491957660.
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RAMALHO, L. Fluent Python. Sebastopol,. CA: O'Reilly, 2015. ISBN 978-1491946008.
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VANDERPLAS, J. T. Python data science handbook: essential tools for working with data.. Sebastopol, CA: O'Reilly Media, 2016. ISBN 978-1491912058.
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ZELLE, J. M. Python programming: an introduction to computer science. Third edition.. Portland, Oregon: Franklin, Beedle & Associates,, 2016. ISBN 978-1590282755.
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