Content, progress and pedagogy of the module

Aim: 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.

Content:

Empirical semester project on a programme-relevant theme in collaboration with an external organisation (external partner collaboration is not required but highly recommended, and supported). The project departs from a real-life empirical problem and uses a suitable combination of methods covered throughout the semester (M1-3 and other relevant techniques) to address it. If possible, the analysis is based on real data provided by the collaborating institution, possibly combined with other sources.

In this module, students will – in part independently and partly under supervision – write an empirical semester project (in the optimal case) in collaboration with an external organisation. The length of the project report depends on the group size (maximum of 4 students), with a maximum of 25 normal pages (2400 characters incl. spaces, which equals to approx. 360 words) per student, including references, but excluding appendices.

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

Knowledge:

• Define relevant real-world empirical problems within organisations.
• Explain the limitations of quantitative analysis on different levels of sophistication.
• Demonstrate knowledge about the choice of ontological and epistemological positions.
• Explain the choice of the methodological implementation.
• Show insights in potential limitations of the undertaken analysis.

Skills

Skills:

• Identify and delineate a problem that can be analysed using data science approaches.
• Collect / extract / mine necessary appropriate data.
• Assess the reliability / validity / ethical and legal status / limitations of the data.
• Describe and explore the data.
• Identify and carry out appropriate data preparation and analysis.
• Visualise / communicate the results.
• Reflect on the robustness / limitations / ethical, legal, social consequences regarding the analysis and results.
• Present and discuss results written and orally at an appropriate academic level.

Competences

Competencies:

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

Type of instruction

Supervision

Students will have a main supervisor from their respective master programme, and complementary methods support by the Social Data Science teachers.

Exam

Exams