Data Visualisation: Graphics for Impact (UG)


Data Visualization: Graphics for Impact Microcredential

Duration: 5 weeks
Credits: 5 Academic Credits (UG)
Delivery: All course content is delivered on Moodle, our virtual learning platform. You will converse with the tutors and fellow students in online forums.
Timetable: Fully online and flexible, with no scheduled classes to attend.
Funding: You are eligible for a fully funded place on this course if you are Scottish-domiciled and/or work for an organisation based in Scotland.
College: College of Social Sciences
School: School of Social & Political Sciences

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In the business world, data is critical for making informed decisions and justifying policies and budgets. One of the most effective ways to communicate data is through data visualisations, such as graphs, maps, bar charts, and interactive trendlines. These visualisations can quickly tell a complete story, bring reports to life, and spread important messages. However, to be effective analysis tools, they must be trustworthy, accessible, and able to facilitate better understanding. This course is designed to help you plan, create, and distribute powerful data visualisations that aid decision-making, enable effective reporting, and communicate clear messages.

Why this course

Upon completion of this course, you will be able to:

  • Plan, create, and distribute data visualisations that aid decision making, enable effective reporting, and convey clear messages;
  • Articulate standards for impactful, trustworthy, and accessible visualisations that empower users to learn from data and comply with legal requirements and aesthetic guidance;
  • Analyse, report, and communicate data in a powerful way without the need for any prior data analysis or statistical and mathematical training;
  • Bring attention to and highlight data that might otherwise go unnoticed;
  • Define and discuss different types of data visualisations and their uses in the context of providing evidence for practical questions;
  • Use data visualisations to answer questions and tell "data stories" in the context of short reports;
  • Use the open-source software ‘R’.

Course structure

This course is designed to provide learners with a progression of skills from basic to intermediate in the field of Data Visualisation. It will cover the following topics:

Week 1: Understanding Visualisations: Looking At & Analysing Data

Week 2: Making & Reporting Visualisations

Week 3: Mastering Visualisation: Trust, Best Practices & Reporting

Week 4: Diversifying Graphs: Maps, Networks, Text & Data Narratives

Week 5: Empowering Visualisations: Interactive Graphs & Publishing on the Web



  • Guided project (optional)

Completed in Week 3, learners will be given an initial opportunity to apply their new skills. Feedback will be provided.

  • Final assessment (100%) (optional)

Learners will complete a data visualisation task using data of their choice. The task will involve producing and discussing a visualisation. Set in Week 4. Due in May 2023.

Learners who choose to submit this assessment will be awarded 5 Academic Credits towards a relevant Undergraduate degree at the University of Glasgow.


Course alteration or discontinuation
The University of Glasgow endeavours to run all courses as advertised. In exceptional circumstances, however, the University may withdraw or alter a course. For more information, please see: Student contract.

Career prospects

This course is designed to equip learners with skills to progress into the following roles and industries:

  • Data visualisation
  • Those intent on commissioning high-quality visualisations
  • Professionals in SMEs
  • Third sector
  • Public sector
  • Administration
  • Communications

Completion of this course grants potential for:

  • Further academic study
  • Promotion
  • Increased earning potential
  • New career path

Entry requirements

It is suggested that learners on this course are educated to at least SCQF Level 6 and have an IELTS equivalent of 6.5.

Learners would benefit from prior experience of statistical software (particularly R), perhaps acquired through Dr Emily Nordman’s Upskilling course, Applied Data Skills for Processing and Presenting Data.

Learners will not be asked to prove their academic or professional history.