University of Technology Sydney

94669 Project: Data-driven Design Challenges

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Subject handbook information prior to 2021 is available in the Archives.

UTS: Transdisciplinary Innovation
Credit points: 8 cp
Result type: Grade and marks

Requisite(s): 94668 Project: Complex Challenges to Creative Possibilities AND 94676c Technology Lab 2: Connect and Network AND 94672c Creative Methods and Entrepreneurial Initiatives
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
There are course requisites for this subject. See access conditions.

Description

Undertaking a project in this subject exposes students to new ways of viewing and interacting with data. Working in teams, students use simple data analytics techniques and a range of data visualisation technologies – from pen and paper to spreadsheets and software – to interrogate various types of data from different stakeholder perspectives and identify patterns in the dataset. Whether solving problems or exploring new opportunities, students examine contemporary cases and live projects illustrating how novel big data sources can act as catalysts to drive innovation and transform industries and professions. Students work individually and in teams to investigate traditional and emerging big data sets, and test models or frameworks prior to rapidly developing a data driven prototype or proof of concept. They experiment with and consider different methods for communicating insights and proposals to different stakeholders for different purposes.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Elicit and utilise knowledge and experiences from team members and stakeholders with diverse backgrounds to interpret and communicate data
2. Demonstrate techniques to identify strengths, weaknesses and assumptions of a selection of data including social, economic, cultural or strategic significance of an interpretation
3. Articulate an interpretation of complex ideas drawn from data to mobilise and connect people and the organisations, networks, and systems in which they work and live
4. Develop a data-driven prototype designed to facilitate change in a given context
5. Exercise judgement in analysis and interpretation of data to ensure it is statistically accurate, contextualised and strategically applied in technological proposals

Course intended learning outcomes (CILOs)

This subject contributes specifically to the development of the following course intended learning outcomes:

  • Communicate, explore, network and negotiate in ways that elicit and are inclusive of ideas from diverse disciplines (3.1)
  • Use a range of appropriate data, media, techniques, technologies and methods creatively and critically in multi-disciplinary teams to discover, investigate, design, produce and communicate ideas or artefacts (3.3)
  • Articulate often-complex ideas simply, succinctly and persuasively to a diverse team or audience (3.4)
  • Imagine and design initiatives within existing organisational structures (intapreneurship) or build a new context for change (entrepreneurship) (4.3)
  • Design and develop technological ideas, strategies and practices for betterment that engage with and respond respectfully, generatively and analytically to different ways of knowing across community and cultural contexts (5.2)

Contribution to the development of graduate attributes

This subject provides opportunities for you as a student to draw insights from complex data sets to create value and opportunity. You are exposed to various service design methods, practices and tools which assist you in creating opportunities from big, complex data sets. As part of this, you work alongside industry and community stakeholders to explore the complexity of real world industry problems. Finally, the subject challenges you to experiment with expressing and communicating different ways in which data can be imagined.

So your experiences as a student in this subject support you to develop the following graduate attributes (GA):

  • GA 3 - Inter- and transdisciplinary practices
  • GA 4 - Resilient practices within complex systems
  • GA 5 - Imaginative and ethical citizenship

Teaching and learning strategies

Learning in this subject takes place in a collaborative, immersive and experiential environment, using inquiry-oriented learning strategies. Students work with academics and industry/community mentors across a wide range of disciplines, undertaking a project in teams. Taking a creative and critical approach, students generate and explore possibilities surrounding a complex opportunity space. Each week, students engage with preparatory work (online via Canvas) which includes looking at case studies, readings and quizzes to then be further developed in class.

Content (topics)

  • Analytical insight and uncertainty, assumptions and cognitive biases
  • Professional judgement
  • Challenges and ethical issues in data analysis
  • Meaningful representation of data - story and persuasive argument
  • Inter and trans-disciplinary dialogue
  • Big data

Assessment

Assessment task 1: Insights and opportunities

Objective(s):

This task addresses the following subject learning objectives:

1 and 2

This assessment task contributes to the development of course intended learning outcome(s):

3.1 and 3.3

Type: Project
Groupwork: Individual
Weight: 30%
Length:

1,000 words

Assessment task 2: Patterns and perspectives

Objective(s):

This task addresses the following subject learning objectives:

1, 3 and 5

This assessment task contributes to the development of course intended learning outcome(s):

3.1, 3.4 and 5.2

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

10-minute pitch

Assessment task 3: Data-driven innovation design challenge

Objective(s):

This task addresses the following subject learning objectives:

3, 4 and 5

This assessment task contributes to the development of course intended learning outcome(s):

3.4, 4.3 and 5.2

Type: Project
Groupwork: Group, individually assessed
Weight: 40%

Minimum requirements

A minimum of 80% of attendance of classes (as outlined in the timetable) is required. This normally means you can miss at most two (2) weekly classes. If you do not meet this requirement, you may be refused permission by the Responsible Academic Officer to be considered for assessment in this subject (UTS rule 3.8.2), which may result in you failing the subject. If the reason for your absence is due to illness or misadventure, please follow the normal procedures to apply for special consideration.

Late penalties apply to all assessment tasks as outlined in the FTDi FYI Student Guide booklet and assessment briefs.