Content, progress and pedagogy of the module

Module 4 aims at providing the student with an opportunity to apply a set of data science methods – a combination of techniques covered in M1-3 as well as other relevant analytical approaches – to an existing empirical problem in an area, which is relevant to the student’s field of study.

After completion of the module, students are able to define an appropriate problem formulation within their line of study, identify a sophisticated data collection and analysis strategy, carry out the analysis and present their results using state-of-the-art data science approaches, as well as critically self-evaluate their findings. They can select the most suitable among the wide range of methods presented in the modules M1-3, and autonomously apply it to their specific problem.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• defining relevant real-world empirical problems within organisations.
• explaining the choice of the methodological implementation.
• showing insights in potential limitations of the undertaken analysis.

Skills

The objective is that the student after the module possesses the necessary skills in:

• identifying and delineating a problem that can be analysed using data science approaches.
• collecting / extracting / mining necessary appropriate data, including describing and exploring it.
• visualising / communicating the results, written, visually and orally at an appropriate academic level.

Competences

The objective is that the student after the module possesses the necessary competences in:

• initialising, control and complete problem-oriented data science project work.
• coordinating own resources for the solution of domain-specific related problems.
• taking responsibility for own professional learning and development.

Type of instruction

For information see §17.

Exam

Exams