CSC-40042 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

Programme/Approved Electives for 2022/23

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

Standard entry requirements for the programme and level.

Barred Combinations

None

Description for 2022/23

The module equips students 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 students will understand advanced analytics and statistical modelling techniques and evaluate their applicability for different types of problems.
The module develops the following Keele Graduate attributes:

3. Information literacy: the ability to locate, evaluate and synthesise large amounts of frequently conflicting information, ideas and data.
4. The ability to creatively solve problems using a range of different approaches and techniques, and to determine which techniques are appropriate for the issue at hand.
6. The ability to communicate clearly and effectively in written and verbal forms for different purposes and to a variety of audiences.

The module aims to equip students with the knowledge of a variety of tools and statistical techniques that enable them to make sense of the emergence and exponential growth of big data.
The students will be able to critically evaluate and apply advanced analytics and statistical modelling 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; will be achieved by assessments: 1 & 2
assess a range of statistical approaches and apply the correct statistical approaches to extract information from a set of data typically available in a modern business or research organisation; will be achieved by assessments: 1 & 2
evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems; will be achieved by assessments: 1 & 2
develop advanced applications of statistical data analytics techniques using an advanced specialist programming language (e.g. R, Matlab); will be achieved by assessments: 1 & 2
assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations. will be achieved by assessments: 1 & 2

Study hours

22 hours lectures;
22 hours tutorial;
2 hours exam;
104 independent learning.

School Rules

None

Description of Module Assessment