University of Technology Sydney

36104 Data Visualisation and Narratives

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Analytics and Data Science: TD School
Credit points: 8 cp

Subject level:


Result type: Grade, no marks

Requisite elaboration/waiver:

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.

Content (topics)

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: Online Quiz


SLO:1, SLO:2, SLO:3 and SLO:4

Type: Quiz/test
Groupwork: Individual
Weight: 30%

1 hour per quiz


Assessment task 2: Individual Assignment


SLO:1 and SLO:4

Type: Report
Groupwork: Individual
Weight: 20%

1500 words excluding references and figures

Assessment task 3: Group Assignment and Presentation


SLO:2, SLO:3 and SLO:4

Type: Project
Groupwork: Group, group assessed
Weight: 50%

Part A: two A4 pages.

Part B: 2500-3000 words, excluding graphs and references.

Part C: 5-7 mins video presentation

Minimum requirements

Students must attempt each assessment task and achieve an overall pass mark in order to pass this subject.