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CM50266: Applied data science

[Page last updated: 04 August 2021]

Academic Year: 2021/2
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)
Period:
Academic Year
Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Assessment detail data for this unit is currently being updated as a change has been approved. Updated assessment information will be published here shortly.
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
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.

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.

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.

Programme availability:

CM50266 is Compulsory on the following programmes:

Department of Computer Science

Notes:

  • This unit catalogue is applicable for the 2021/22 academic year only. Students continuing their studies into 2022/23 and beyond should not assume that this unit will be available in future years in the format displayed here for 2021/22.
  • Programmes and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.