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ASSESSMENT TASKS
Task 1 FORMATIVE TASK Graphs and Graph Data Science Introduced
FORMATIVE TASK
Instruction: Explain the different types of graphs and their properties to a non-specialist. Your briefing note should contain the following:
- An outline of what is understood by the terms ‘graph’, ‘vertex’, ‘node’ and ‘edge’.
- Identify and explain the problem and solution of the Bridges of Konigsberg
- Discuss the various types of graphs, and provide examples for several of
Task 2 SUMMATIVE TASK Graph Data Science in Your Organisation
SUMMATIVE TASK
Instruction: Evaluate how graph data science can be used to advance the interests of an organisation of your choosing. Your report must contain the following:
- An outline of the various types of graph data models, and of the graph ecosystem as it currently stands (LO 1, 2.2, 2.3, 3.1 )
- Identify and explain the advantages and potential uses of LPG database, RDF database and relational databases (LO 3.2, 3.3, 4.2)
- A judgment as to the benefits of the use of graph data within the organisation, and the potential impact of this material on future decision making (LO 3.3, 3.4, 3)
| Learning Outcomes: To achieve this unit, the learner must be able to: | Assessment Criteria:
Assessment of these learning outcomes will require a learner to demonstrate that they can: |
| 2. Understand the core types of graph data models. | 2.1 Explain what is meant by a “Knowledge graph”.
2.2 Explain what is meant by a “Labelled Property Graph” (LPG). 2.3 Explain what is meant by a “Resource Description Framework” (RDF) graph. |
| 3. Understand the graph ecosystem. | 3.1 Outline the graph ecosystem from graph databases, graph languages to graph visualisation tools.
3.2 Analyse the features, uses, benefits and drawbacks of LPG databases, RDF databases and relational databases. 3.3 Analyse the use-cases and applications for LPG and RDF graph databases. 3.4 Analyse Python graph libraries and their features. |
| 4. Understand the types of graph data science and graph algorithms. | 4.1 Explain what is meant by “graph data science”.
4.2 Analyse the types of “graph algorithms”: search and pathfinding, centrality, and community detection. 4.3 Analyse the types of problems and use- cases that can be tackled by graph data science. |