Recommended prerequisite for participation in the module

The module builds on knowledge acquired in the modules “Programming for Data Wrangling and Visualisation”, “Introduction to statistics”, and “Introduction to AI techniques”.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Can explain important introductory concepts, results, and theories related to data science, statistics, and/or AI.
• Can relate such results to real-world applications.

Skills

• Is able to apply a number of introductory methods and tools from a specific area within data science (e.g., data wrangling and visualisation) and AI (e.g., a prediction model).
• Can assess whether a given result is valid and/or if a specific method can be used under the prescribed conditions/assumptions.

Competences

• Can independently select appropriate methods, software, and tools to investigate specific questions in an analysis.
• Is able to explain the scope of the application of methods and software tools.

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	Applications of AI on Your Own Data
Type of exam	Oral exam based on a 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