Analytics and Visualisation for Managers and Consultants
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 R.
Full module specification
|Module title:||Analytics and Visualisation for Managers and Consultants|
This module is closed to MSc Business Analytics only
|Duration of module:||
Duration (weeks) - term 2: |
“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.
ILO: Module-specific skills
- 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
- 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
- 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.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled Learning and Teaching Activity||25||Class meeting time|
|Guided Independent Study||10||Preparatory video lessons and podcasts on topics regarding visualisation and using software|
|Guided Independent Study||25||Preparatory reading prior to class meeting time|
|Guided Independent Study||40||Reading and preparation for visualisation critique presentation assignment|
|Guided Independent Study||50||Reading and preparation for final visualisation project|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Daily in-class exercises||During class hours||1-6||Verbal|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Visualisation critique presentation||40||Recorded 10 min video presentation with a maximum of 10 accompanying slides||1,2,3,5,6||Written digital feedback|
|Final visualisation project||60||3000 word equivalent||1-6||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|
|Visualization critique presentation||Recorded 10 min video presentation with a maximum of 10 accompanying slides (40%)||1,2,3,5,6||Summer reassessment period|
|Final visualisation project||3,000 word equivalent (60%)||1-6||Summer reassessment period|
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%.
- 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
We will be covering the following aspects of visualisation with R:
- Introduction to visualisation in R
- Tidy, layers and aesthetics
- Visual objects (geoms)
- Exploratory data analysis
Indicative learning resources - Basic reading
- 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
- Wickham, H. and Grolemund, G. (2021). R for data science. Available at https://r4ds.had.co.nz/
- Wickham, H., Navarro, D., and Lin Pedersen, T. (2021). ggplot2. Available at https://ggplot2-book.org/
- Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. Available at https://clauswilke.com/dataviz/
Module has an active ELE page?
Indicative learning resources - Other resources
- Students will mainly use R and RStudio in class.
- 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)
Last revision date