Degrees incorporating this pedagocial element :
Description
Using a scientific programming language (e.g., Python) as a tool for modelling and numerical analysis.
Outline:
- Number representation systems and their precision
- Data in Python
- Basic data structures: scalars, strings, lists, dictionaries, sets, tuples
- Matrix representations of numbers: the numpy ndarray (vs matrix),pandas data tables
- Read and write data according to the data type (CSV, JSON, pickle,. . . )
- Array operations:
- Unitary operators MX0 –> MX1
- N-ary operators (MX0, . . . , MXn-1) –> MXn
- Solving equations
- Linear matrix equations with applications to interpolation and regression
- Differential equations with applications to interpolation and prediction
- Probability and statistics in Python
- Probability laws: distribution families, random variables, realisations
- Statistical tests
Recommended prerequisite
Mathematical background on probability and statistics, linear algebra and differential equations