The module has two main ambitious goals:
i .) It aims to guide students through several model making processes, where we take real world problems from different fields, and build an approximative mathematical and/or computational model by which we predict and optimise. (An essential part of working with models, is to know their domain of validity, which will be determined critically, and sometimes extended iteratively.)
ii.) In this module we will work with real world (and sometimes generated) data, look it from different angles, process it, extract information by visualisation, and computation. In several cases, we will go through how to draw conclusions, optimise or do predictions based on data.
For the model making processes we will usually use 4 main steps: Definition of the problem and asking relevant questions, Abstraction of the questions into computable format, Computation on data resulting various plots, charts, quantities, and finally Interpret the results and see how well we addressed the original questions and how could we go further.
The main environment for the Module will be Wolfram Language (in particular Mathematica for which a licence will be provided) because of its steep learning curve, rich visualisation options and easy to access curated datasets.
However, open source softwares (Python, Sage) and environments (CoCalc) will be also introduced and students can use these to complete their projects as well.
Students of this module will strengthen their analytical skills, critical thinking, and will get a glimpse into machine learning and data science.