| Unit name | Visual Analytics |
|---|---|
| Unit code | EMATM0066 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. O'Grady |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
Introduction to AI and Text Analytics (EMATM0067) |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
A picture may be worth a thousand words, but only if it is well-designed to represent data faithfully and meaningfully. In today’s data-driven world, the ability to interpret complex datasets is essential. The Visual Analytics unit will enable students to transform raw data into meaningful insights through advanced visualisation techniques and analytical methods. It will teach you how to harness data to support decision-making, identify patterns, and communicate findings effectively.
How does this unit fit into your programme of study?
Visual Analytics complements the rest of the programme, providing essential analytical skills and presentation of results. Within the programme, it strengthens your capacity to work with data at an advanced level, enriching your overall skill set.
An overview of content
This unit covers two key aspects of visual analytics: the science of information visualisation (primarily concerned with the way that data is represented visually), and advanced machine learning (as a tool to change the data representation, e.g. through dimensionality reduction, or as a way of analysing visual data) in a framework of statistical pattern recognition.
Information visualisation topics covered by this unit include: data types and their representations, non-vectorial data, human requirements for visual analytics, scientific visualisation, visualisation quality metrics, Shneiderman’s mantra (overview first, zoom and filter, details on demand) practical visualisation tools.
Machine learning topics covered by this unit include: principles of Statistical Pattern Recognition (probabilistic models for data, curse of dimensionality, generalisation error, bias-variance dilemma); linear models (Probabilistic Principal Component Analysis; Discriminant Analysis); generalised dissimilarity mappings and neighbour embedding techniques; Gaussian Processes; latent variable models (Gaussian Mixture Models, Generative Topographic Mapping and Gaussian Process Latent Variable Model); Bayesian model regularisation and combination; feature selection.
How will students, personally, be different as a result of the unit
You’ll understand how to critically assess data sources, detect meaningful patterns and present findings in ways that make complex information accessible and persuasive. This unit will shape how you approach challenges, enabling you to apply these principles to many other models and tasks in data science..
Learning Outcomes
At the end of the unit, students will be able to:
1. Define and apply the principles of information visualisation in terms of human perception and cognition.
2. Build machine learning models for data and explain their operation in terms of a statistical pattern recognition framework.
3. Use Bayesian regularisation and variational methods to fit models to data.
4. Create user-focused visualisations of numerical, categorical, and time series data using Python visualization tools and Tableau.
In the Visual Analytics unit, you will engage in a mix of student-centred, interactive, and applied learning activities designed to build both practical skills and deep understanding.
You’ll learn through a combination of lectures and labs. Lectures will introduce key concepts, such as data visualisation principles and machine learning fundamentals, while labs allow you to apply these ideas in real-world scenarios using Python and Tableau.
All practical labs have some formative assessment–embedded questions based on the lab exercises with answers given later on the VLE.
Tasks which help you learn and prepare you for summative tasks (formative):
All practical labs have some formative assessment – embedded questions based on the lab exercises with answers given later on the VLE.
Lectorials provide examples and case studies that will be worked through in class: students are expected to use the solutions to these to improve their understanding.
Tasks which count towards your unit mark (summative):
Four in-class quizzes that are based on material covered in the practical lab classes related to the machine learning topics (20%): ILO2 and ILO3.
Coursework: Create a visualisation of key features and a data projection of a medium-sized real-world dataset, analyse and evaluate the representation through a user trial, and report on conclusions relating them to the theory of information visualization (80%): ILO1, ILO2, ILO3, ILO4.
When assessment does not go to plan
A modified version of the assessment will be offered in the re-assessment period, which is designed to meet the original learning outcomes.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EMATM0066).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
