Afterwards the student is able to:
- Interpret a real-life problem, and extract from it a sensible mathematical research question,
- Devise an action plan for the modelling project with the above research question,
- Execute the modelling assignment, with emphasis on the mathematical analysis, in particular relating to the other courses in this module,
- Present written and verbally the results of the modelling project,
- Cooperate in a team and give constructive feedback,
- Present the results of a project both in written and verbal form,
- Analyse the correctness of algorithms implemented in Python,
- Describe, explain and modify larger programs in Python.
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The prime objective (80% of the course) is to predict water levels using lots of measurements that have been collected in the past. This is directly related to signals and transforms but if you want to characterise the quality of your prediction you also need to model and quantify the uncertainty, which is connected to probability theory. In the project the students work in a group. Students should report their results in a written report, as well as an oral presentation. A second component is a programming course (20% of the course) focusing on the use of Python. The focus is on the following topics:
- Recursion
- Formal reasoning on the correctness of algorithms
- Implementing elementary algorithms from a textual description
- Using Python for data and time series analysis.
Assessment
Modelling assignment (100%)
Programming assignment (pass/fail)
Collaboration (pass/fail)
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