Applications will open in mid-February 2023
Numbers drive decisions, analyses of quantitative data justify policies and budgets. Data visualisations are the most effective way of communicating numbers whether for sales reports or COVID patient numbers. Graphs tell a whole story in seconds, bring reports alive, and allow a message to travel fast via social media. Maps, bar charts, or interactive trendlines often have more impact than the text around them. But they need to good visualisations.
Good data visualisations are trustworthy, accessible, and able to tell a story.
Good data visualisations are powerful analysis tools and foster better understanding.
- Contact: firstname.lastname@example.org
5 weeks, time commitment: 8-10 hours per week
Fully flexible, online learning with content available 24/7
Course cost: £399
Teaching start: Monday 24th April 2023
Why this course
At the end of the course participants will be able to:
- The aim of the course is to teach participants the skills to plan, create, and distribute data visualisations that aid decision making, enable effective reporting and tell clear messages. Moreover, the course introduces and applies standards for impactful, trustworthy and accessible visualisations.
- Uniquely after this course participants will be able to analyse, report and communicate data in a powerful way without the need of any prior data analysis, statistical or strong mathematical training. The course enables participants to bring alive data that otherwise doesn’t get attention or is hidd
- define and discuss different types of data visualisations and their uses, in the context of providing evidence for practical questions.
- describe and discuss standards which make data visualisations impactful, trustworthy and accessible
- use data visualisations to answer questions and tell “data stories” in the context of short reports.
- plan, create, and distribute visualisations that allow easy communication, aid decision making, and empower users to learn from data, using the opensource software R and free Amazon Web Services.
Participants know and be able to apply what makes:
- Impactful graphs that allow easy communication, aid decision making, and empower users to learn from data.
- Trustworthy graphs that are transparent and clear about the data used and evidence provided.
- Accessible graphs that comply with legal requirements and aesthetical guidance.
The course is structured around topical themes in visualisation and the necessary skills to put these into practices. The skills progress from basic to intermediate through the course with a focus on enabling learners to easily build on skills for specific projects. Each session consists of a lecture and demonstration, followed by peer-learning, self-study materials, and programming support labs.
Over 5 weeks participants will acquire knowledge about data visualisation though recorded lectures and brief readings. They will develop programming skills to generate their own visualisation through recorded demonstrations, weekly tasks, and supported by live online help sessions. A set of individual and peer-to-peer activities provide the opportunity to evaluate good visualisations and reports using them.
The course takes learners from everyday graphs and Excel sheets, to interactive online visualisations and their own projects generating maps of UK regions. All skills are taught with no prior knowledge assumed and supported by live help sessions.
A guided project after three weeks gives participants the opportunity to apply their skills. A finally assessment enables participants to show case what they have learnt. And subsequent feedback is intended to guide participants use of the skills beyond the course.
Meet the teaching team
This course is designed and delivered by Dr Thees F Spreckelsen, Lecturer for Research Methods at the School of Social and Political Sciences and the Glasgow Q-Step Centre for improving teaching of quantitative social science.
Thees has taught data analysis skills for 10 years, including as lecturer for the Centre for Evidence-based Intervention at the University of Oxford.
He has taught numerous short courses for the Q-Step centre and the Advanced Quantitative Methods Summer School of the Oxford Department of Education aimed at both academics and professionals. Most recently he was commissioned to provide research methods training to the Department for Work and Pensions.
The course will be supported by three teaching assistants from the University of Glasgow Q-Step centre. As current Phds they will bring cutting-edge expertise to the course as well as hands-on data visualisation skills.
All of these activities will be facilitated via the University of Glasgow virtual learning environment Moodle.
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.
The course is envisioned to be useful both for those seeking to use the skills in their current job and those seeking jobs that require these skills. The skills will be equally useful for those wanting to produce high-quality visualizations as those intending to commission them. Specifically, the course will benefit professionals in small/medium-sized business, third- and public sector employees.
The skills learnt will strength applications to jobs requiring administrative-, analysis-, as well as communication experience in any sector.
The course requires a general computer literacy (e.g. familiarity with MS Office programmes and ability to install software), access to a computer with a stable internet connection as well as Zoom (for help sessions).
The content and activities of the course require participants to spent 10hrs on lectures, activities and completing tasks.
Participants would benefit from prior experience of statistical software especially R – The microcredential course “Data skills for processing and presenting data” (Dr Emily Nordman) is recommended as an excellent prior training with some technical overlap.