Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

Learning objectives

Knowledge

The student should acquire knowledge and skills in graph data analytics, such as:

Descriptive graph analysis

• network properties, such as diameter and clustering
• node characteristics, such as centrality
• community structure analysis (e.g., through graph clustering)
• introduction to graph mining

Graph Data Management to support Analytics

• graph data models, construction, extraction
• graph databases and queries
• applications of Graph Analytics

Skills

• demonstrate knowledge of graph data analytics methods and techniques

• be able to select relevant concepts and techniques for a given problem in graph data analytics

• be able to use correct notation and terminology in graph data analytics

Competences

• The student should be able to apply graph data analytics methods and techniques both theoretically and practically, including their use in problem-solving.

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Additional information

Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854