Postgraduate Course: Data visualisation: knowledge transfer (HEIN11040)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Data visualisation, in general terms, is a tangible representation of data that provides an accessible means for analysing, understanding, and communicating the trends, outliers, and patterns of data for and across sectors and audiences. This course explores the communication of data relevant to health, social and care services settings through visualisations for exploration, analysis and related communication media. Visualisation can be a powerful means of sharing and communicating service user data to a range of audiences, such as driving policy development or improving stakeholder or broader public understanding. The course will explore theory, application, design and evaluation techniques that can be used to visualise and accessibly communicate data in various ways, such as combining vignettes taken from individual stories with data derived from official sources such as government statistics. Students will be introduced to several global projects that communicate health, social, and care services data in this way and will have the opportunity to develop their small data visualisation project. |
| Course description |
With data volumes increasing exponentially, an increasing focus on interdisciplinary approaches to complex problems, and an ever more tech-savvy general public, those working in health, social and care services can no longer rely solely on data presentation tools such as spreadsheets and tables. Data visualisation gives organisations and individuals the means to share important information, sometimes in real-time, by telling data stories. Data visualisation curates data into a form that is accessible and which can more easily highlight current utilisation, trends and outliers. Well curated data visualisation will allow individuals and organisations to tell a story. Unlike more traditional data representation forms, data visualisation aims to remove the noise from data and highlights usable information. This course will enable students to describe a visualisation problem within target users and global environments, explore the data visualisation, discuss and design appropriate and accessible visualisation concepts, and implement and critically reflect on their own and other's designs.
Outline Content
The course will introduce the value of data visualisation in the health, social and care services context. Next, the course will focus on the data visualisation design process and data visualisation tools and techniques. Finally, effective communication and reporting using data visualisation will be covered.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2021/22, Available to all students (SV1)
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Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 % |
| Feedback |
Feedback is information provided to the students about their learning relative to learning outcomes. The two main types of feedback are formative and summative. Formative feedback is generated to engage learners to constantly reflect on how they can approach, orient and evaluate learning, which leads to successful learning outcomes. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and discussion boards. A formative task will also be offered before the student submits their assessed course work. All assignments will be marked, and feedback is provided within fifteen working days (where possible).
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of modern data visualisation techniques and how to process the visual presentation of information.
- Apply knowledge and understanding to implement data visualisation through various media (interactive, infographic, storytelling) and self-chosen tools (e.g. Tableau, D3.js, R, or Python) to solve specific real-world challenges and interpret data and recognise their features and limitations.
- Apply critical analysis, evaluation, and synthesis to select the most appropriate tool for the visualisation challenge at hand, considering context, target audience, user accessibility, potential tasks that the visualisation should facilitate, and the data set's characteristics.
- Demonstrate the ability to effectively communicate challenges with end-users, stakeholders, peers, junior and senior colleagues using a range of data visualisation tools and why data visualisation is required.
- Critically reflect and evaluate their own and other's visualisation designs, make informed judgements and provide constructive solutions.
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets:
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within, and contribute positively, ethically and respectfully to the health and social care community, while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their own learning. Students will be asked to use on their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
2) Skills:
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and -active learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Mairead Bermingham
Tel:
Email: mairead.bermingham@ed.ac.uk |
Course secretary | Miss Magdalena Mazurczak
Tel:
Email: Magdalena.Mazurczak@ed.ac.uk |
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