Analytics and Visualisation for Managers and Consultants
Module title | Analytics and Visualisation for Managers and Consultants |
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Module code | BEMM461 |
Academic year | 2025/6 |
Credits | 15 |
Module staff | Dr Shirley Atkinson (Lecturer) |
Duration: Term | 1 | 2 | 3 |
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Duration: Weeks | 10 |
Number students taking module (anticipated) | 200 |
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Module description
In this module you will develop the skills necessary to communicate analytical results to senior managers. You will learn the consulting skills necessary to understand business problems and develop solutions based upon analytics. The module will develop your skills in communicating information about data visually and verbally. You will learn how to use visualisation tools in Python.
Module aims - intentions of the module
“A good sketch is better than a long speech” - often attributed to Napoleon Bonaparte
This module focuses on the skills necessary to communicate data and results to others.
Students will learn the best practices for creating effective visualisations.
Students will be able to assess the quality of visualisation approaches.
Understand the most appropriate method of visualising a variety of data types.
Present information that facilitates decision making.
Students will deliver a presentation that critically evaluates an existing visualisation and will undertake a final project to demonstrate their visualisation skills.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. P1: Utilise relevant tools and platforms to identify and effectively analyse relevant digital data
- 2. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. P9: Communicate effectively through oral presentations and written reports, presenting methodologies and findings in a way that is appropriate to the intended audience.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 4. P12: Critically analyse and evaluate complex problems across diverse contexts, and synthesize innovative and creative solutions
- 5. P14: Demonstrate technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.
Syllabus plan
Storytelling with Data - We will discuss the power of storytelling and how this can be utilised to create effective visualisations
The Psychology of Effective Data Visualisation - Visualisations are a form of visual communication. In order to communicate effectively, we need to understand the psychology of our audience and how they will consume any information that we present to them. We will also consider principles of Human-Centred Design as presented by Don Norman
Graph Construction, Selection and Design - Drawing from the ideas of authors such as Edward Tufte, Stephen Few and Alberto Cairo, we will consider different options for visually encoding data and how to determine what types of graph to use and when. We will discuss each element of a graph’s construction to enable students to produce sophisticated and elegant visualisations that communicate information with clarity and impact
Dashboards Using modern development tools such as Python and/or data visualisation platforms, we will explore the principles and best practice around ensuring a suitable visual representation of key business measures and metrics.
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
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20 | 130 | 0 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
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Scheduled learning and teaching activities | 20 | Class meeting time |
Guided independent study | 30 | Preparation reading prior to taught sessions. |
Guided independent study | 40 | Reflection and further reading following taught content sessions |
Guided independent study | 60 | Assessment preparation: research and creation of assessment |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Daily in-class exercises | During class hours | 1-5 | Oral |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
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100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
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Annotated bibliography | 40 | c.1000 words | 1-3, 5 | Written digital feedback |
Case study visualisation | 60 | 3,000-word equivalent | 1-5 | Written digital feedback |
Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
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Annotated bibliography | Annotated bibliography (c.1000 words, 40%) | 1-3, 6 | Referral/deferral period |
Case study visualisation | Case study visualisation (3,000-word equivalent, 60%) | 1-5 | Referral/deferral period |
Re-assessment notes
Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.
Indicative learning resources - Basic reading
Indicated texts (not required but will be referenced in the course).
- Cairo, A. (2011). The Functional Art: An Introduction to Information Graphics and Visualization. San Francisco, CA: New Riders.
- Few, S. (2012). Show Me the Number: Designing Tables and Graphs to Enlighten (2nd ed). Burlingame, CA: Analytics Press.
- Munzner, T. (2014). Visualization Analysis and Design. Boca Raton, FL: CRC Press.
- Norman, D. (2013). The Design of Everyday Things: Revised and Expanded Edition (Revised and Expanded ed.). London: MIT Press.
- Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.
- Ware, C. (2020). Information Visualization: Perception for Design (4th ed.). Cambridge, MA: Morgan Kauffman
- Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. Available at https://clauswilke.com/dataviz/
Indicative learning resources - Other resources
Software:
- Students will mainly use Python and Visual Studio Code in class.
- Students will be able to explore the use of popular python libraries for data visualisation
- Students will have the option of using Inkscape for image manipulation and D3.js for interactive data-driven documents.
- We will routinely need a text editor (such as Notepad++, gedit, Kate, Emacs or Atom)
Credit value | 15 |
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Module ECTS | 7.5 |
Module pre-requisites | None. |
Module co-requisites | None. |
NQF level (module) | 7 |
Available as distance learning? | No |
Origin date | 09/01/2020 |
Last revision date | 16/04/2025 |