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| Detailinformationen |
| Quellcurriculum |
Masterstudium Economic and Business Analytics 2025W |
| Lernergebnisse |
Kompetenzen |
(*)- Python basics: Students understand the syntax and structure of the Python programming language, including variables, data types, and control structures.
- Python in data science: Students are able to use the most important data science libraries to build simple analytics applications
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| Fertigkeiten |
Kenntnisse |
(*)- Learning Outcome 4 (LO4): Solve simple programming tasks
- Learning Outcome 5 (LO5): Choose appropriate data structures and data types
- Learning Outcome 6 (LO6): Understand potential pitfalls and interpret error messages in Python
- Learning Outcome 7 (LO7): Interface with given Python libraries for data science
- Learning Outcome 8 (LO8): Design and implement simple analytics applications in Python
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(*)- Learning Outcome 1 (LO1): Understand the syntax and structure of the Python programming language, including variables, data types, and control structures.
- Learning Outcome 2 (LO2): Understand the basic concepts of interfacing with external libraries in Python
- Learning Outcome 3 (LO3): Know about the most important data science libraries in Python
Course topics:
- Part 1: Deepening the basics of Python in practice
- Working with Python programming environments
- Practical considerations regarding data types, data structures, control structures
- Functions and object-oriented programming
- Part 2: Practical Python for data science
- Libraries for visualization
- Libraries for data science and machine learning
- Libraries for working with graphs
- Libraries for working with geospatial data
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| Beurteilungskriterien |
(*)Regular homework exercises that must be submitted online via Moodle. The homework exercises are worth 30 points. Exam at the end of the semester with 70 points. At least 35 points on the exam are needed to pass the course. There is a possibility to repeat it in case of negative results or scheduling issues (retry exam). The exam consists of programming tasks. It lasts 90 minutes.
Final grades will be given as follows:
| Points | Grade |
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| 87.5 - 100.0 | 1 |
| 75.0 - 87.0 | 2 |
| 62.5 - 74.5 | 3 |
| 50 - 62.0 | 4 |
| 0.0 - 49.5 | 5 |
Both the homework exercises and the exam cover all the learning outcomes.
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| Lehrmethoden |
(*)The course uses a combination of different teaching methods in order to
- maximize the motivation and attention of the students.
- address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred information inputs, supported by slides and literature
- Presentation of homework exercises by students to ensure comprehension of the content, followed by joint discussions with the whole group
- Development of content in collaboration with the students on the computer and the black board
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)- Slides
- In-class programming exercises with solutions
- Reading material
- Python Documentation https://docs.python.org/3/library/
- McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter, current edition, O’Reilly
- Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, current edition, O’Reilly
- Jordan, Applied Geospatial Data Science with Python: Leverage geospatial data analysis and modeling to find unique solutions to environmental problems, current edition, O’Reilly
- Pointers to additional literature
(All content is provided via Moodle)
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| Lehrinhalte wechselnd? |
Nein |
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