Module Specification

Visual Analytics and Data Storytelling

London school of INNOVATION

Module Specification

Visual Analytics and Data Storytelling: Transforming Complexity into Compelling Narratives


The impact of data hinges not just on its analysis but on how it's communicated. This module equips you with practical skills to transform complex data into impactful visuals.

You will learn how to prepare and clean data, explore visualisation techniques ranging from standard charts to advanced formats like heatmaps, network graphs, and geospatial maps, and apply design principles such as colour theory, typography, and visual hierarchy. By applying ethnographic techniques, you will understand your audience to ensure your visuals and stories resonate.

Mastering these skills enables you to craft compelling data narratives that engage diverse audiences and drive decision-making. You will gain hands-on experience with industry-standard tools and emerging technologies, preparing you for real-world challenges.

Enhancing your ability to communicate complex insights clearly will distinguish you in any field where data-driven decisions are essential.


Code Number of Credits ECTS Credits Framework HECoS code
DV71 30 15 FHEQ - L7

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Critically evaluate methodologies for transforming complex data into effective visual narratives tailored to diverse audiences.
LO2 Knowledge and Understanding Demonstrate a systematic understanding of advanced data visualisation principles and techniques, including design principles and their application.
LO3 Intellectual Skills Systematically analyse complex datasets to derive insightful visual narratives that support informed decision-making.
LO4 Technical/Practical Skills Design and produce compelling data visualisations using industry-standard tools, applying advanced design principles and methodologies.
LO5 Technical/Practical Skills Efficiently prepare, clean, and manipulate complex datasets for visualisation using advanced data querying techniques.
LO6 Professional/Transferable Skills Collaborate effectively in teams to produce and present data visualisations, enhancing team performance in complex contexts.

Assessment Patterns

Weighting Format Outcomes assessed
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.		 K LO1
K LO2
I LO3
T LO4
T LO5
P LO6
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.		 K LO1
K LO2
I LO3
P LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 36.00
AI formative assessment 18.00
Workshop/Lab Sessions 27.00
Independent reading, exploration and practice 153.50
Summative assessment 64.00
300.00

Prerequisites and Co-requisites

  • Prerequisite: Modern Database Systems

Content Structure

Week Chapter Name Chapter Description
Week 1 Data Preparation and Cleaning Master data preparation and cleaning because accurate and reliable data are crucial for effective visualisations. By ensuring data integrity, you build trust in your visual narratives, which is essential for driving informed decisions. Understanding the importance of clean data helps you avoid misleading insights and enhances the credibility of your work.
Week 2 Fundamental Visualisation Techniques Explore basic visualisation methods to effectively communicate simple data insights. Understanding why certain charts best represent specific data types helps you convey your message clearly. Mastering these fundamentals is vital because it forms the basis for creating more complex visualisations and ensures your audience easily grasps the information presented.
Week 3 Advanced Visualisation Techniques Dive into advanced visualisation methods to represent complex and large-scale data effectively. Grasping why these sophisticated techniques are necessary allows you to tackle challenges like big data and real-time streams. This understanding is key to extracting deeper insights and making sense of intricate datasets that simple charts cannot handle.
Week 4 Visual Design Principles Apply design principles because they enhance the impact and clarity of your visualisations. Knowing why elements like colour theory, typography, and layout matter ensures your visuals are not just aesthetically pleasing but also communicate your message effectively. This focus on design maximises audience engagement and comprehension.
Week 5 Understanding the Audience Use ethnographic techniques to understand your audience because tailoring your visuals to their needs increases engagement and comprehension. Recognising why audience insight matters helps you communicate complex data without oversimplifying, ensuring your message resonates even with non-technical stakeholders and leads to better decision-making.
Week 6 Visual Analytics Tools Gain hands-on experience with industry-standard tools to stay current in the field. Understanding why tools like Tableau, Power BI, and AI-driven analytics are important empowers you to create dynamic visualisations efficiently. This knowledge enhances your ability to present data in innovative ways that capture attention and facilitate analysis.
Week 7 Querying and Integrating Data Learn to query and integrate data because combining multiple data sources enriches your analysis. Knowing why data integration is crucial helps you present comprehensive visualisations that meet specific business objectives. This ability enhances the relevance of your data stories and supports more informed decisions.
Week 8 Data Storytelling Craft compelling narratives by understanding why storytelling enhances data communication. Turning data into stories makes complex insights accessible and memorable, engaging your audience more effectively. Recognising the power of storytelling aids in driving understanding and action based on your data findings.
Week 9 Critical Evaluation Skills Develop critical evaluation skills because assessing visualisations critically leads to continuous improvement. Understanding why constructive feedback is valuable helps you enhance your own work and contribute positively to others. This focus on evaluation elevates the overall quality of visual communication in the field.

Module References

There are no module reference contents to display.

Methods of teaching/learning


Introductory lecture (1.50 hours)

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.


Concept learning (knowledge graph) (36.00 hours)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.


AI formative assessment (18.00 hours)

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.


Workshop/Lab Sessions (27.00 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (153.50 hours)

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Programmes this module appears on

Programme Term Type
1 Data Science and Analytics (MSc) 2 Core
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Visual Analytics and Data Storytelling: Transforming Complexity into Compelling Narratives (DV71)