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Lecturer(s)
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Course content
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Content of the exercises: Block 1 - Introduction, introduction to Python, Jupyter notebook and Visual Studio Code development environment, Basic commands, data types and operators, Advanced data types and functions Block 2 - Modern concepts of object-oriented programming in Python, Modules and libraries Block 3 - Modules for working with data, Mathematical operations in Numpy, Data visualization in Matplotlib, Working with cameras and image processing in OpenCV
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Learning activities and teaching methods
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- Term paper
- 66 hours per semester
- Participation in classes
- 15 hours per semester
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| 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 |
| assessment methods |
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| Analysis of seminar paper |
| Analysis of seminar paper |
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Recommended literature
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