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 2021 is available in the Archives.

UTS: Analytics and Data Science: Transdisciplinary Innovation
Credit points: 8 cp

Subject level:

Postgraduate

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.

Note

This subject requires attendance at the evening and Saturday sessions, which form important parts of assessed work.

Description

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 open source technologies 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)
  • Critiquing trends and theoretical frameworks
    Critique contemporary trends and theoretical frameworks in data science for relevance to one's own practice (2.1)
  • 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)
  • 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)
  • 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 workshops on campus and client data challenges. 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. Online media also provide the platform to share information and encourage interaction between students, staff, stakeholders and industry experts. This subject uses the CIC Around networked learning tool, for students to develop critical thinking and written communication skills; and peer assessment techniques and tools, for students to reflect on the most effective ways to communicate a message by visualising data.

Intensive Workshops: Dynamic and interactive workshop 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. Clients 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.

Transdisciplinary Visualisation Challenges: Building upon their developing critical approaches to working in teams, students will collaborate to harness their diverse perspectives to communicate solutions to problems in data science and innovation. Working in teams on 'real life' data challenges, students will together build and share transdisciplinary approaches to visualisations and narratives that can add value to a wide range of ‘real client’ organisations and communities.

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

Assessment task 1: Story telling with visualisations

Objective(s):

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

Type: Report
Groupwork: Group, group assessed
Weight: 30%
Length:

Minimum 700 words

Criteria:

Part A: Written report

  1. Complete submission with a Tableau workbook and professionally formatted documents (spelling, grammar, punctuation, layout) with content presented in sections with proper headlines, such as introduction, motivation, approach, results and take-home message.
  2. Robustness in justifying the selection of visualisations and their relevance to the story being told.
  3. Insightfulness in asking proper questions, creating effective visuals and telling compelling stories.

Part B: Oral presentation

  1. Clarity in communicating narrative in oral presentation and visualisation.
  2. Appropriateness of visualisation principles applied in the design of the interactive output.
  3. Depth of insights about the data resulting from the interactive output.

Assessment task 2: In-class quizzes and assessments

Objective(s):

SLO:2 and SLO:3

Type: Quiz/test
Groupwork: Individual
Weight: 20%
Criteria:

Answer quiz questions correctly and complete tutorial tasks on time.

Assessment task 3: Visual analytics project

Objective(s):

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

Type: Report
Groupwork: Individual
Weight: 50%
Length:

2000 words plus Executive Summary (500 words) and visualizations, excluding appendix which can be of any length.

Criteria:

Part A: Progress report

  1. Demonstration in making progress towards the project goals.

Part B: Final report

  1. Complete submission with Python code, Tableau workbook if any, and report. The report must be a professionally formatted document (spelling, grammar, punctuation, layout) with content being presented in sections with proper headlines, such as introduction, motivation, literature review, analytic questions, method, results, discussion, conclusion, references and appendix.
  2. Demonstration of mastering of python visualization techniques and story telling skills and clarity of description.
  3. Robustness in drawing on relevant external sources of evidence (e.g. client/stakeholder white papers; academic literature) for formulating analytical questions and discussions.
  4. Effectiveness in describing the analytical process and communicating data insights using proper visualizations and other evidence.
  5. Comprehensiveness of data analytics, depth of discussion, strength of insights and appropriateness of recommendations.

Minimum requirements

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