41193 IS Data Visualisation Studio
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 41192 IS Value Creation Studio OR 31250 Introduction to Data Analytics
Description
Taking the Information Systems (IS) perspective (rather than the more common Data Science or IT perspectives), this subject focuses on the latest developments and professional practices in data visualisation and visual analytics for non-technical decision-makers. Recognising the importance of data visualisation literacy in various disciplines including IS, the subject differs from other more common data visualisation subjects by emphasising (i) learning the whys behind doing data visualisation in addition to the how to dos, (ii) identifying business/organisational cases and their requirements, the stakeholders, and their problem space and decision-making needs, (iii) visual ethics and responsible use of visual data, and (iv) utilising commercial data visualisation software instead of elaborate coding.
In short, the subject treats visualisation as a “verb” rather than a “noun” and aims for visual problem exploration “through data” rather than creation of static visual outputs. To this end, the subject is taught through a combination of lectures and studios with a focus on design thinking exercises and ethical visual analytics. Students develop the necessary skills and the mindset for problem finding and solving through visual data exploration that goes beyond creation of static visual outputs. These skills include identification of different stakeholders and analysis of their decision-making needs and environments, problem formulation, finding and ethical harvesting of relevant data, data quality, data integration, multidimensional modelling, and development of a data exploration prototype using a commercial data visualisation software. Focusing on value creation and decision-making needs of different stakeholders, students also learn how to use data-driven visual storytelling and other contemporary techniques to explore the problem space, inform, influence and inspire different types of stakeholders (including business and IT).
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Determine the value proposition of data visualisation and visual data exploration for different decision-makers in an enterprise context. (B.1) |
|---|---|
| 2. | Integrate relevant data sources in an ethical manner following contemporary data quality and data integration principles and practice in an enterprise context. (D.1) |
| 3. | Design a prototype of a data exploration environment as an information system. (C.1) |
| 4. | Work collaboratively to design a data exploration prototype for a real-world problem. (E.1) |
| 5. | Reflect on the experience of becoming a competent, ethical and holistic data visualisation professional in an enterprise context. (F.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Socially Responsible: FEIT graduates identify, engage, interpret and analyse stakeholder needs and cultural perspectives, establish priorities and goals, and identify constraints, uncertainties and risks (social, ethical, cultural, legislative, environmental, economics etc.) to define the system requirements. (B.1)
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
- Reflective: FEIT graduates critically self-review their performance to improve themselves, their teams, and the broader community and society. (F.1)
Teaching and learning strategies
This subject is structured around a major visual data exploration project designed to support decision-making of non-technical stakeholders. The subject consists of weekly 1h lectures and 2h studios. Each weekly session is designed to help students to build the necessary foundational knowledge and skills as well as confidence and inspiration to successfully complete the given project. Weekly studios are highly interactive and emphasise active learning, collaboration and an appreciation of the complexities of contemporary data visualization and visual analytics professional practices of non-technical decision makers in real-life settings.
The subject includes a variety of learning and teaching activities such as short presentations, group and individual problem-solving activities focusing on different aspects of interactive data exploration, project work, learning circles, project stakeholder analysis and project presentations as well as group feedback and peer review activities.
Students are immersed in the world of professional practice in visual data exploration for decision-making through boundary-spanning activities, transdisciplinary innovation, engagement with process stakeholders, appreciative inquiry, data humanism, data-driven storytelling and value co-creation as well as critical thinking about the ethics of data collection, integration and visualisation (including visual ethics). By creating ethical, professional and problem-focused (rather than data-driven) solutions students are given an opportunity to offer something of value to our wider university community.
Content (topics)
- The IS perspective of data visualisation and visual exploration: How does it differ from the Data Science and IT/CS perspectives?
- The role of data visualisation & visual analytics in different decision environments
- Static data visualisation versus visual data exploration
- Stakeholder need analysis and design thinking for data visualisation
- Analysis of a real-life industry-wide data exploration solution
- Contemporary data quality and data integration methodologies
- Multidimensional data modelling for non-technical decision makers
- Visual ethics and responsible use of visual data
- Data-driven visual story-telling and its application in different organisational settings
- Current developments and future trends in visual data exploration (the IS perspective)
Assessment
Assessment task 1: Problem definition and stakeholder analysis
| Intent: | Students become familiar with the purpose of problem definition and stakeholder analysis. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1 and E.1 |
| Type: | Report |
| Groupwork: | Group, group assessed |
| Weight: | 10% |
| Length: | Group report: maximum 1500 words |
Assessment task 2: Data preparation and initial concept
| Intent: | Students practice ethical data harvesting, data quality and initial visual concept development |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and E.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 25% |
| Length: | Group report: maximum 2000 words |
Assessment task 3: Visual data exploration prototype - presentation
| Intent: | Students demonstrate their visual data exploration prototype. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1, D.1, E.1 and F.1 |
| Type: | Presentation |
| Groupwork: | Group, group and individually assessed |
| Weight: | 15% |
| Length: | 15 mins per group |
Assessment task 4: Visual data exploration prototype - report
| Intent: | Students develop capability in designing a visual data exploration prototype. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1, D.1, E.1 and F.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 30% |
| Length: | Group report: maximum 2500 words |
Assessment task 5: Prototyping project reflection
| Intent: | To reflect on the project in the form of key lessons learned. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): B.1, C.1, D.1 and F.1 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | 1000 words |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
Required text books:
- The subject does not have a set textbook. Weekly readings and other resources are provided on the LMS.
Required Software
- Tableau - https://www.tableau.com/
Recommended texts
- Sleeper, R. (2018) Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master, O'Reilly Media, Inc.
- Kriebel, A., & Murray, E. (2018), #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time, Wiley.
- Additional resources will be recommended as per subject and project needs.