Module detail

SHE level	11
SCQF credit points	15
ECTS credit points	7.5
Module code	MMI230816
Module Leader	Yang Fang
School	Glasgow School for Business and Society
Subject	Finance and Accounting
Trimester	B (January start), C (May start)

Summary of content

This module develops both technical skills in visualisation tools and the communication capabilities needed to translate analytical insights into business impact. Students learn principles of effective visual design, narrative construction, and audience adaptation. Practical exercises include creating dashboards, interactive visualisations, and executive presentations.

It offers a comprehensive exploration of data visualisation as a critical component of business analytics, equipping students with the knowledge and skills to transform complex datasets into clear, impactful visual representations. Beginning with foundational principles, students will learn about the importance of data visualisation in supporting business decision-making, including how to choose appropriate techniques based on data type and analytical goals. The module guides students through key visualisation practices such as identifying patterns, trends, and anomalies, using tools and techniques that support both static and interactive analysis.

As the module progresses, students will engage with more advanced visualisation methods, including multidimensional visualisations, interactive dashboards, and storytelling with data. Emphasis is placed on designing visualisations that are not only aesthetically pleasing but also meaningful and accessible to diverse audiences. Practical exercises will develop their ability to create dashboards tailored to stakeholder needs and to use visual narratives to drive strategic decision-making across business functions.

The module also incorporates a strong focus on ethical and legal considerations, exploring how data visualisation practices must uphold principles of accuracy, fairness, and compliance. Students will examine real-world case studies, learning how to address challenges related to data privacy, representation, and inclusivity. Finally, the module looks ahead to emerging trends in the field, such as immersive and AI-powered visualisation, preparing them to criticall

Module details

This module integrates data visualisation theory with practical, application-focused learning to equip students with the conceptual understanding and technical skills needed to communicate complex data effectively in business environments. Emphasis is placed on the visual communication of insights, critical analysis of data quality, ethical awareness, and design thinking. Through a combination of lectures, extended computer lab sessions, and structured independent learning, students will develop the ability to design, implement, and evaluate effective visualisation strategies that support data-driven decision-making across various business contexts.

Lectures:
Lectures will introduce key principles of data visualisation, including the role of visual perception, chart selection, storytelling with data, and dashboard design. Students will explore various types of data (categorical, numerical, temporal, spatial) and the appropriate visual techniques to represent them clearly and persuasively. Business-relevant applications—such as customer behaviour analysis, operational performance tracking, and reporting to stakeholders—will be integrated throughout. Active learning techniques, such as design critique sessions, case discussions, and interpretation tasks, will be used to deepen understanding and foster critical thinking around design choices and ethical considerations.

Computer Lab Sessions:
Lab sessions will provide students with practical experience using visual analytics tools such as Tableau, Power BI, and relevant programming libraries in Python or R (e.g., ggplot2, matplotlib, plotly). Students will work with business datasets to create exploratory visualisations, build interactive dashboards, and apply advanced techniques including heatmaps, network graphs, and visualisation of unstructured data. Lab exercises will reinforce good practices in data management, accessibility, and visual clarity, while encouraging experimentation with form, function, and user experience acro

Module structure

Activity	Total hours
Lectures	22
Tutorials	0
Practicals	10
Seminars	0
Independent Learning	108
Assessment	10
Placement	0

Assessment methods

Component	Duration	Weighting	Threshold	Description
Course Work001		50	45	CW1: Class Test (2 hours)
Course Work002		50	45	CW2: Practical Assignment (Project)

Academic essentials

Data Visualisation for Business Analytics

Summary of content

Module details

Module structure

Assessment methods