36104 Data Visualisation and Narratives
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 8 cp
PostgraduateResult type: Grade, no marks
Any student wishing to enrol in first- and second-year subjects concurrently, needs to apply for a waiver.
This subject requires attendance at the evening and Saturday sessions, which form important parts of assessed work.
This subject engages students in the power of narrative and visualisation as 'person-centred’ methods for sense making around data. Students explore a range of techniques and tools for visualising different types of data, that could include medical, open government, accounting, business, scientific, satellite, and historical data as a means of generating new insights. Students experiment with and evaluate different methods for communicating insights to a range of stakeholders and domain experts in a chosen field. Through investigating different narrative approaches to 'tell the story', students hone their skills to succinctly and persuasively communicate descriptions of phenomena studied in the data set as well as confidently justify their findings.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
|SLO:1.||Justify the selection and analysis of data as the basis for ‘data narratives’ for different stakeholders.|
|SLO:2.||Apply a range of visualisation and narrative techniques to a variety of data types.|
|SLO:3.||Justify the selection of narrative tools and techniques to illuminate critical aspects of problems.|
|SLO:4.||Justify and communicate ‘data narratives’ to stakeholders from a range of industries and contexts drawing on relevant data patterns and analyses.|
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following course outcomes:
- Making the invisible visible
Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system (1.4)
- Exploring, interpreting and visualising data
Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments (2.2)
- Understanding uncertainty, ambiguity and complexity
Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data (2.3)
- Designing and managing data investigations
Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components (2.4)
- Examining and articulating data value
Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations (3.2)
- Working together
Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges (3.3)
- Developing communication skills
Collaborate to develop and refine multimodal communication skills needed to successfully work in data science teams (4.1)
- Engaging audiences
Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels (4.2)
- Informing decision making
Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences (4.3)
Contribution to the development of graduate attributes
The subject provides opportunities for you as a student to build your professional toolkit through analysing, manipulating, interpreting and visualising data to develop compelling narratives that help make sense of data rich environments. In doing this you experiment with a range of data visualisation, storytelling and communication approaches to engage key audiences with data analytics. Finally, the subject challenges you to identify potential avenues for action and persuasively justify your recommendations for informing decision-making, both to specialist and non-specialist audiences.
So your experiences as a student in this subject support you to develop the following graduate attributes (GA):
• GA 1 Sociotechnical systems thinking
• GA 2 Creative, analytical and rigorous sense making
• GA 4 Persuasive and robust communication
Teaching and learning strategies
Online Preparation and Assignments: from the pre-semester Preparation and Orientation weeks to the end of the subject students will be completing online activities that are designed to prepare them for intensive activities on campus. A range of online media applications will be used to provide specific resources and activities that students complete before coming to each on-campus session.
Interactive face-to-face sessions: Dynamic and interactive face-to-face sessions will include in-class exercises and presentations that provide opportunities to develop innovative practice suited to data science contexts and industry professionals. Presentation and discussion formats involve introductions to innovative practice and tools as well as critical debate and reflection of learning in context. Guest speakers and industry experts will be partners in the program.
Professional Development Portfolio: Portfolio thinking will continue to frame students' curation, consolidation and communication of evidence of their learning and development of graduate attributes and their professional evolution. The portfolio environment provides a key platform for building an individual professional narrative, as well as personal reflection upon and integration of visualisation and ‘story-telling’ work done in class and in teams. Students will be developing their personal ‘toolkit’ of visualisation and narrative strategies and tools within their portfolio. Regular feedback opportunities from peers and the teaching team are built into class sessions, assignments and online activities.
Building compelling visuals: understand design principles that underpin all good visualisations
Mastering tools and techniques: learn to use main stream tools and programming techniques for visualisation
Crafting Narratives: understand and use techniques to tell stories about data
Storytelling with Data: learn how to communicate visually using data
Assessment task 1: Assembling your Professional Visualisation and Narrative Toolkit
SLO:1, SLO:2, SLO:3 and SLO:4
500 words per blog post
PART A: Presentation of selected tool
PART B: Blog posts
Assessment task 2: Crafting Participatory Data Stories
SLO:1 and SLO:4
PART A: Presentation of selected story
PART B: Blog posts
Assessment task 3: Data (Visualisation) Challenge
SLO:2, SLO:3 and SLO:4
|Groupwork:||Group, individually assessed|
Minimum 2000 words, excluding appendix which can be of any length.
Part A: Data Challenge
Part B: Final report
Students must attempt each assessment task and achieve an overall pass mark in order to pass this subject.