Coronavirus (Covid-19) Information

Module ICE-4006:
Data Science Experiments

Module Facts

Run by School of Computer Science and Electronic Engineering

20 Credits or 10 ECTS Credits

Semester 1

Organiser: Dr Cameron Gray

Overall aims and purpose

This module aims to:

  • Provide students with an environment to experiment with techniques taught
  • Examine non-trivial data sets.

The format of this module is a free-form project where students are able to experiement with the techniques, methods, and processes taught and apply them to a student-selected data set. Tutors will advise on suitability of these data sets to ensure the required complexity and dimensionality.

Course content

Indicative content includes:

  • Data analysis methods and algorithms.
  • OSEMN (Obtain, Scrun, Explore, Model, iNterpret) cycle.
  • Methods for reporting data science experiments.
  • Applying acquired knowledge and skills to a student-selected data set or sets.

Assessment Criteria


Equivalent to the range 70%+. Assemble critically evaluated, relevent 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.


Equivalent to 50%. 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.


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.

Learning outcomes

  1. Employ data science techniques with a data-set.

  2. Report results of experiments analysing data.

  3. Evaluate the effacacy of experiments conducted.

Assessment Methods

Type Name Description Weight
Project 100

Teaching and Learning Strategy


Tutorials on a per-student as needed basis.

Private study

Experimentation and write up of the project portfolio.


Transferable skills

  • Literacy - Proficiency in reading and writing through a variety of media
  • Numeracy - Proficiency in using numbers at appropriate levels of accuracy
  • Computer Literacy - Proficiency in using a varied range of computer software
  • Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
  • Exploring - Able to investigate, research and consider alternatives
  • Information retrieval - Able to access different and multiple sources of information
  • Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
  • Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting

Subject specific skills

  • Deploy tools effectively
  • Development of general transferable skills
  • Deploy systems to meet business goals
  • Defining problems, managing design process and evaluating outcomes
  • Knowledge and/or understanding of appropriate scientific and engineering principles
  • Knowledge and understanding of mathematical principles
  • Knowledge and understanding of computational modelling
  • Specify, deploy, verify and maintain computer-based systems
  • Principles of appropriate supporting engineering and scientific disciplines

Courses including this module

Compulsory in courses: