Recommended prerequisite for participation in the module

The module builds on knowledge acquired in the modules “Advanced Data Wrangling and Interactive Visualisation”, “Advanced statistics”, and “Advanced AI”.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Have deep understanding within one or a few selected elements of data science, statistics, and/or AI, and relate to real-world applications.
• Must be able to understand and reflect upon the knowledge of the subject area and be able to identify scientific problems.

Skills

• Is able to apply advanced methods and tools from a specific area within data science (e.g., relational or non-relational databases and/or interactive visualisation), statistics (e.g., statistical inference), and/or AI (e.g., a prediction model and/or a generative model).
• Can assess whether a given result is valid and/or if a specific method can be used under the prescribed conditions/assumptions.
• Can independently select appropriate methods, software, and tools to investigate specific questions in an analysis, and reflect upon the choice systematically and critically.
• Is able to explain the scope of the application of methods and software tools.

Competences

• Formulate a statistical model and account for its parameters, including estimation and interpretation of these.
• Can communicate on data science, statistics, and/or AI problems and problem-solving strategies to peers within and outside of this area

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Extent and expected workload

Expected module workload is 450 hours.

Exam

Exams

Name of exam	Master Project
Type of exam	Master's thesis/final project
ECTS	15
Permitted aids	Please see the module description in Moodle.
Assessment	7-point grading scale
Type of grading	External examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures