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CM50266: Applied data science

[Page last updated: 27 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 12      [equivalent to 24 CATS credits]
Further information on notional study hours Notional Study Hours: 240
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Academic Year
Further information on unit assessment Assessment Summary: CW 100%
Further information on unit assessment Assessment Detail:
  • Written reports on two case studies, the second with oral presentation element (CW 40%)
  • Completed lab reports (CW 60%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: 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.
Further information on programme availabilityProgramme availability:

CM50266 is Compulsory on the following programmes:

Department of Computer Science

Notes: