CSC-20053 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2024/25

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25

The module equips learners with the knowledge of a variety of tools and statistical techniques to enable them to make sense of the exponential growth of big data. The learners will understand advanced analytics and statistical modelling techniques and evaluate their applicability for different types of problems.

The module aims to equip learners with the knowledge of a variety of tools and statistical techniques that enable them to deal with the analysis of large datasets.
The learners will be able to choose and apply data analytics and statistical techniques appropriate to different types of problems.

Intended Learning Outcomes

evaluate available data and determine how best to analyse the information available to provide required outcomes
: 1,2
apply statistical data analytics techniques using an advanced specialist programming language (e.g. R, Python, Matlab): 1,2
assess the options of storing, managing and manipulating large volumes of data in the context of business organisations: 1
select and apply an appropriate statistical approach, including contemporary machine learning methods, to extract information from a dataset, in the general context of data analysis: 1

Study hours

18 hours workshops/tutorials (supported online and in block release)
20 hours online lectures
106 hour independent learning
6 hour class-test (during block release)

School Rules

None

Description of Module Assessment