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Lecturer(s)
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Course content
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Content of the exercises: 1. Introduction, introduction to Python, Jupyter notebook and Visual Studio Code development environment 2. Basic commands, data types and operators 3. Advanced data types and functions 4. Modern concepts of object-oriented programming in Python 5. Modules and libraries 6. Mathematical operations in Numpy 7. Data visualization in Matplotlib 8. Working with tabular data in Pandas 9. Symbolic mathematics in Sympy 10. Working with cameras and image processing in OpenCV 11. ROS2 - Architecture 12. ROS2 - Communication methods 13. ROS2 - URDF format for robot description 14. Final project presentation
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Learning activities and teaching methods
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- Participation in classes
- 42 hours per semester
- Term paper
- 39 hours per semester
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| prerequisite |
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| Knowledge |
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| explain the basic syntax of Python programming language |
| explain the basic syntax of Python programming language |
| Skills |
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| use the Python programming language to solve simple tasks |
| use the Python programming language to solve simple tasks |
| learning outcomes |
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| Knowledge |
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| explain the basics of working with industrial data in Python |
| explain the basics of working with industrial data in Python |
| navigate in the ROS (Robot Operating System) environment |
| navigate in the ROS (Robot Operating System) environment |
| explain how Jupyter notebooks work and describe the advantages of using them for Python development |
| explain how Jupyter notebooks work and describe the advantages of using them for Python development |
| explain the principles of object-oriented programming in Python |
| explain the principles of object-oriented programming in Python |
| Skills |
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| prepare a more complex program in the Python programming language |
| prepare a more complex program in the Python programming language |
| understand someone else's code in the Python programming language |
| understand someone else's code in the Python programming language |
| efficiently acquire, analyse and visualise data of different scales |
| efficiently acquire, analyse and visualise data of different scales |
| use Python to solve data science problems |
| use Python to solve data science problems |
| teaching methods |
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| Knowledge |
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| Exercises on PC |
| Exercises on PC |
| Skills |
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| Exercises on PC |
| Exercises on PC |
| assessment methods |
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| Knowledge |
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| Analysis of seminar paper |
| Analysis of seminar paper |
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Recommended literature
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LUTZ, M. Learning Python. Fifth edition. Beijing: O'Reilly,, 2013. ISBN 978-1449355739.
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