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Lecturer(s)
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Tomášek Pavel, Ing. Ph.D.
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Course content
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1. Introduction to the organization of teaching, basic concepts, algorithmization; 2. Directory structure, command line/terminal; 3. Programming languages, source code, compilation, interpretation, syntax, semantics, development environments; 4. Basic data types; 5. Basic constructs, variables; 6. Operators, expressions; 7. Working with standard input and output; 8. Introduction to algorithms for numerical calculations and working with text; 9. Basic data structures, functions; 10. Introduction to algorithms for searching and sorting; 11. Program testing and debugging; 12. Modular structure of programs; 13; Other useful tools for program development; 14. Source code commenting, documentation.
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Learning activities and teaching methods
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- Participation in classes
- 28 hours per semester
- Home preparation for classes
- 28 hours per semester
- Preparation for course credit
- 34 hours per semester
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| learning outcomes |
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| Knowledge |
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| define basic data types |
| define basic data types |
| explain the basic terminology of software development |
| explain the basic terminology of software development |
| describe the operators used in the Python programming language |
| describe the operators used in the Python programming language |
| explain the basic constructs used in the Python programming language |
| explain the basic constructs used in the Python programming language |
| list appropriate tools and development environments for creating and modifying program code |
| list appropriate tools and development environments for creating and modifying program code |
| Skills |
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| write simple algorithms for numerical calculations and working with text |
| write simple algorithms for numerical calculations and working with text |
| ability to understand simple program code |
| ability to understand simple program code |
| read values and text from the standard input and write a message to the standard output |
| read values and text from the standard input and write a message to the standard output |
| test/debug a program |
| test/debug a program |
| use a development environment |
| use a development environment |
| execute a code written in the Python programming language |
| execute a code written in the Python programming language |
| teaching methods |
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| Knowledge |
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| Exercises on PC |
| Exercises on PC |
| Dialogic (Discussion, conversation, brainstorming) |
| Dialogic (Discussion, conversation, brainstorming) |
| E-learning |
| E-learning |
| Individual work of students |
| Individual work of students |
| Lecturing |
| Lecturing |
| Teamwork |
| Teamwork |
| Skills |
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| Exercises on PC |
| Exercises on PC |
| E-learning |
| E-learning |
| Individual work of students |
| Individual work of students |
| Teamwork |
| Teamwork |
| Practice exercises |
| Practice exercises |
| assessment methods |
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| Knowledge |
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| Analysis of works made by the student (Technical products) |
| Analysis of works made by the student (Technical products) |
| Conversation |
| Conversation |
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Recommended literature
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HASLWANTER, Thomas. An Introduction to Statistics with Python: With Applications in the Life Sciences. Cham: Springer, 2016. ISBN 9783319283159.
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MATTHES, Eric. Python Crash Course: A Hands-On, Project-Based Introduction to Programming. San Francisco: No Starch Press, 2019. ISBN 978-1-59327-928-8.
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PECINOVSKÝ, Rudolf. Python: knihovny pro práci s daty pro verze 3.11. Praha: Grada Publishing, 2023. ISBN 978-80-271-0659-2.
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PECINOVSKÝ, Rudolf. Python: Kompletní příručka jazyka pro verzi 3.11. Praha: Grada Publishing, 2022. ISBN 978-80-271-3891-3.
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ZELLE, John M. Python Programming: An Introduction to Computer Science. Wilsonville: Franklin, Beedle &Associates, 2017. ISBN 978-1-59028-275-5.
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