Course: Programming Basics

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Course title Programming Basics
Course code MUSKM/1ZP
Organizational form of instruction Lesson
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 3
Language of instruction Czech
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Urbánek Tomáš, Ing. Ph.D.
Course content
"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

Learning activities and teaching methods
unspecified
learning outcomes
Knowledge
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
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
Recommended literature
  • HILPISCH, Y. Python for Finance: Mastering Data-Driven Finance. 2nd. O'Reilly Media, 2018. ISBN 978-1492024330.
  • LUTZ, M. Learning Python. Fifth edition. Beijing: O'Reilly,, 2013. ISBN 978-1449355739.
  • MCKINNEY, W. Python for data analysis: data wrangling with pandas, NumPy, and IPython. Second edition.. Sebastopol, California: O'Reilly Media,, 2017. ISBN 978-1491957660.
  • RAMALHO, L. Fluent Python. Sebastopol,. CA: O'Reilly, 2015. ISBN 978-1491946008.
  • VANDERPLAS, J. T. Python data science handbook: essential tools for working with data.. Sebastopol, CA: O'Reilly Media, 2016. ISBN 978-1491912058.
  • ZELLE, J. M. Python programming: an introduction to computer science. Third edition.. Portland, Oregon: Franklin, Beedle & Associates,, 2016. ISBN 978-1590282755.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester