• University of Exeter Business School
• Module

Analytics and Visualisation for Managers and Consultants

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: Demonstrate knowledge and understanding of fundamental, and domain specific, analytics methods and tools
• 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.
• 4. P10: Contribute effectively to managerial decision processes within a business context.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. P12: A collaborative mind-set: Our graduates are enterprising and motivated individuals who are able to actively collaborate and effectively communicate within a range of diverse settings.
• 6. P14: 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 Tableau, 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)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

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 (2^nd 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 (2^nd ed.). Cheshire, CT: Graphics Press.
• Ware, C. (2020). Information Visualization: Perception for Design (4^th 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)

Key words search

Visualisation, Python, Analytics