module specification

This module introduces key concepts and techniques for visualising data to extract meaningful insights. It covers the process of understanding data, cleaning and handling outliers, selecting appropriate visualisation types, and effectively presenting results. Techniques for visualising different data forms, including graph and text data, are explored. A strong practical component allows students to apply visualisation methods to real-world data, enabling clear communication of insights and supporting data-driven decision making.

The aims of this module are to:
· Enable students to gain understanding of fundamental concepts in data analysis and visualisation,
· Develop students' expertise in data analysis, including summarising data with descriptive statistics, handling missing values and outliers, identifying patterns and trends, and comparing groups or time periods.
· Enable students to acquire skills in analysing graph, text data and creating effective visualisations to communicate insights clearly. · Enhance students’ ability to tell compelling stories with data by creating coherent visual narratives, establishing logical connections between charts, and integrating multiple visualisations to communicate insights in a cohesive and impactful way.
· Develop students with practical skills in applying data analysis and visualisation techniques for real world data analytical problems.

N/A

· Fundamental concepts in data visualisation (Cleaning and handling missing values or outliers, summarising data with descriptive statistics, identifying patterns or trends, creating visualisations to communicate insights, comparing groups or time periods) [LO1, LO2]
· Numeric and categorical variable analysis and visualisation [LO2, LO3, LO4]
· Exploring data quality dimensions and relationships of variables with visualisation [LO2, LO4]
· Exploring relationships between multiple variables through comparative and multivariate visualisation techniques such heatmap, correlation tables [LO4, LO5]
· Graph and text data analysing and visualising techniques for web and big data [L04, LO5]
· Applying visualisation methods to identify clusters, groups, and patterns within data [LO2, LO5]
· Developing and interpreting time-based visualisations to explore trends and temporal patterns [LO2, LO5]
· Collaboratively plan, develop, and deliver a data visualisation project, enhancing teamwork and communication skills, and developing the ability to present findings and respond effectively to feedback and queries [LO3, LO6]
· Storytelling with data covering essential visualisation techniques, developing a coherent data narrative, and building the skills needed to communicate findings effectively for industry applications, including dashboard creation and report presentation [LO3, LO4, LO6]

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (Blended learning):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week):
Practical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful interactive data analysis package.

- Blended learning:
Using the University’s VLE and online tools to deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

On successful completion of this module the student should be able to:
[LO1] Demonstrate a comprehensive understanding of fundamental concepts in data visualisation and recognise its importance in exploratory data analysis for identifying patterns, trends, and relationships in data.
[LO2] Select and apply appropriate visualisation techniques to explore and interpret different types of data, enabling clear and effective communication of findings.
[LO3] Design and develop interactive dashboards that integrate various visual elements to present data clearly and support decision-making.
[LO4] Apply suitable visualisation techniques to effectively convey analytical results from complex datasets, tailoring the approach to the audience and context.
[LO5] Critically evaluate and implement recent advances in data visualisation, including emerging visual formats and techniques, to enhance analytical interpretation and presentation.
[LO6] Collaborate effectively in a team to plan, execute, and present a data visualisation project, demonstrating practical application of visualisation skills to address real-world analytical problems.

https://rl.talis.com/3/londonmet/lists/09EFA5AC-AF85-6663-1C3A-79805E22A924.html?lang=en-GB&login=1

1. EMC Education Services (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition, Wiley. [CORE]
2. Pallant, Julie F. (2020) SPSS Survival Manual: a step-by-step guide to data analysis using IBM SPSS, 7th Edition, Allan & Unwin.
3. Brian Everitt, Torsten Hothorn (2011) An introduction to applied multivariate analysis with R. Springer.
4. Sullivan, Dan (2019) Advanced SQL for Data Science: Time Series / with Dan Sullivan, Carpenteria.
5. Richard Brath, David Jonker (2015) Graph Analysis and Visualization. Wiley.
6. Stephen Few, (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.