Content, progress and pedagogy of the module

This module aims at providing the student with an opportunity to apply a set of data science methods – a combination of techniques covered in the previous modules as well as other relevant analytical approaches – to a well defined problem.

The project departs from a real-life empirical problem and uses a suitable combination of methods and frameworks covered throughout the semester. If possible, the analysis is based on real data provided by a collaborating institution, possibly combined with other sources.

After completion of the module, students are able to define an appropriate problem formulation within social data science, 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 previous modules autonomously apply it to their specific problem.

Learning objectives

Knowledge

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

• theoretical and practical opportunities and challenges of applying different perspectives, methods and approaches to a central problem within social data science.
• how to define relevant real-world empirical problems within data driven development.
• showing insights in potential limitations of the undertaken analysis.

Skills

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

• dentifying and delineating a problem that can be analysed using data science approaches.
• Developing and carrying out a social data science project including data collection, preprocessing, modeling, and performance assessment.
• Visualising, presenting and discussing results written and orally at an appropriate academic level.

Competences

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

• independently initialising, controlling and completing problem-oriented social 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