After finishing the course successfully, the students will be able to:
- analyse data analysis problems from a computational perspective,
- decompose problems into the individual steps needed to solve it,
- describe the analysis workflow in the form of UML diagrams,
- find and use existing tools to implement the individual steps, and
- implement the overall workflow in Python.
This course is an introduction to computational thinking about data analysis problems, meant for students with little programming experience. Following a problem-based learning approach, they will learn how to get from a data analysis problem to an abstract workflow description and finally to a concrete software program that solves the problem. The course will cover standard processes for approaching data analysis problems (CRISP-DM model), abstract workflow description techniques (UML diagrams), elementary software design principles (reuse, modularisation), and basic programming skills (using the popular Python language). Finally, it will also address workflow management systems and the FAIR data principles.|
This is an obligatory course for GSLS students with an Applied Data Science profile.