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Unit information: Data-Driven Computer Science in 2022/23

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

Unit name Data-Driven Computer Science
Unit code COMS20011
Credit points 10
Level of study I/5
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Aitchison
Open unit status Not open

COMS10014 Mathematics for Computer Science A or equivalent



School/department Department of Computer Science
Faculty Faculty of Engineering


This unit seeks to acquaint students with the fundamental aspects of processing digital data, presented in the context of concrete examples from applications in machine learning, data mining, and (1D/2D) signal processing.

Particular emphasis is placed on the importance of representation and modelling.

Intended learning outcomes

On successful completion of this unit, students will:

  1. Demonstrate understanding of how audio, video, graphical objects, etc are represented digitally.
  2. Appreciate the role of representation, feature extraction, modelling, estimation, clustering, and classification in digital data processing.
  3. Appreciate the differences and commonalities between data processing tasks.
  4. Demonstrate understanding of the role of training/learning in modelling, classifying and clustering.
  5. Be confident working with high-dimensional spaces and associated transformations.
  6. Be able to analyse data processing problems and decide what techniques to apply.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Details

Exam (Summer, 100%)


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