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CSC-20069 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2022/23 Last Updated: 19 January 2023

CSC-20069 - Visualisation for Data Science
Coordinator: Sangeeta Sangeeta Tel: +44 1782 7 33079
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

CSC-10058 Introduction to Data Science I
CSC-10060 Introduction to Data Science II

Barred Combinations

None

Description for 2022/23

The information age is characterised by large amounts of data generated as part of an ever-widening range of day-to-day activities. When properly analysed this data can lead an organisation to better decision-making, insight, and competitive advantage.
The module aims to equip learners with an appropriate understanding of the use of Data Analytics within areas such as health, security, science and business. The module also aims to equip learners with a variety of Data Visualisation techniques to make sense of the emergence and growth of big data.

Aims
This module aims to explore how organisations can use data analytics and how this relates to the information needs of different stakeholders. It will also cover different data visualisation techniques and explore evaluation and application of the most appropriate visualisations for a given situation, explaining the benefits and limitations.

Intended Learning Outcomes

Compare contemporary uses and theoretical concepts from the academic literature in big data and their impact on areas such as business intelligence.: 1
Apply data analytics and produce appropriate visualisations and reports for a given authentic case study to enable analysis and decision making in either a business or academic research context.: 1

Study hours

20 hours (approx) supported practical activities.
10 hours of online lectures with associated guided reading.
Independent Learning 120 hours.

School Rules

None

Description of Module Assessment

1: Coursework weighted 100%
Coursework
Students are provided with a set of raw data and must then evaluate appropriate data analytics and visualisation techniques which they then use to produce a set of visualisations that enable conclusions to be drawn by a manager/researcher. Students will produce a short report for a particular audience, containing justifications for the approach that they have taken a set of visualisations and related code and development documentation. The report should be the equivalent of 3,000 words.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 19 January 2023