After this course the student can:
- translate basic (fysics/mathematics) problems into an algorithm.
- implement an algortihm in Python code.
- identify and determine/estimate (measurement) errors.
- compute statistical parameters of measured fysical quantities or of quantities derived from measured quantities.
- fit a linear model to a dataset and draw conclusions on the validity of the model.
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In the course Programming and Data Processing 1the student is introduced in programming with Python. Types and variables, loops and branching, functions, algorithms, efficiency and style are among the topics that will be treated. The focus is on applying Python for dataanalysis. In addition the course treats topics related to erroranalysis. Various (measurement)errors as well as how these errors propagate in calculations are discussed. Statistical analysis is used to summarize data and to determine error margins. Also a start is made with fitting data to a model.
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