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Course & Unit Catalogues

CM50266: Applied data science

[Page last updated: 23 October 2023]

Academic Year: 2023/24
Owning Department/School: Department of Computer Science
Credits: 12 [equivalent to 24 CATS credits]
Notional Study Hours: 240
Level: Masters UG & PG (FHEQ level 7)
Academic Year
Assessment Summary: CW 65%, EX 35%
Further information on unit assessment Assessment Detail:
  • Assessment detail for this unit will be available shortly. (CW 65%)
  • Assessment detail for this unit will be available shortly. (EX 35%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Learning Outcomes: After completion of the unit, students will be able to:
* describe and implement the processes involved in initial data handing, preparation and assessment,
* discriminatively apply relevant analytic techniques in the context of defined objectives, and critically interpret results,
* program low-level solutions to analytic problems on smaller data sets,
* deploy, and critically assess, a high-level software technology in a "Big Data" scenario,
* handle, manage and analyse data in the context of legal, ethical and professional considerations,
* deliver a critical and informative report of methods applied and analytic output obtained, in both written and oral form.

Aims: To provide extensive hands-on experience in practical data-driven analytic science, from basic data handling, curation, cleaning and pre-processing, through analysis, low- and high-level software usage, and on to evaluation and reporting of results.

Skills: Intellectual skills:
* Conceptual understanding of data modelling approaches (T,F,A)
* Critical interpretation of analytic output (T,F,A)
Practical skills:
* Programming of data handling techniques (F,A)
* Application of scalable analytic software (T,F,A)
Transferable skills:
* Numerical programming (F,A)
* Technical report writing (T,F,A)
* Oral presentation (T,F,A)

Content: In the first half, topics covered normally include data sources and acquisition, preparation and pre-processing, summarisation and exploratory analysis, application of statistical and machine learning models using a relevant programming language (e.g. Python), model assessment and interpretation of results, along with legal and ethical factors. In the second half, topics will typically focus on higher level Python packages, big data technologies, data sources, mainstream applications and report production.

Course availability:

CM50266 is Compulsory on the following courses:

Department of Computer Science


  • This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24.
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