Overview

The module will give the learner a clear understanding of Data Types and Abstract Data Types including both theory and practical implementation skills. This will be achieved by understanding the following:

• Data storage (Lists, trees, graphs, hash tables, binary search trees).
• Algorithm design techniques.
• Algorithm creation and implementation.
• Algorithm efficiency using big-O and time complexity.
• Future of algorithm design, and usage including ethics.

Assessment Strategy

-threshold -Equivalent to 40%.Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

-good -Equivalent to the range 60%-69%.Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

-excellent -Equivalent to the range 70%+.Assemble critically evaluated, relevant areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

Learning Outcomes

• Compare data structures for a variety of scenarios.

• Create algorithms to fit a given brief.

• Design algorithms showing an awareness of ethics.

• Test algorithms using standardised measures.