94669 Project: Data-driven Design Challenges
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 2018 is available in the Archives.
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 |
Contribution to the development of graduate attributes
This subject contributes 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 (CILO 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 (CILO 3.3)
Articulate often-complex ideas simply, succinctly and persuasively to a diverse team or audience (CILO 3.4)
Imagine and design initiatives within existing organisational structures (intapreneurship) or build a new context for change (entrepreneurship) (CILO 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 (CILO 5.2)
Teaching and learning strategies
Learning in this subject will take place in a collaborative, immersive and experiential environment, using inquiry-oriented learning strategies. Students will 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 will generate and explore possibilities surrounding a complex opportunity space.
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: Data collection and analysis
| Type: | Project |
|---|---|
| Groupwork: | Individual |
| Weight: | 30% |
Assessment task 2: Identifying and communicating patterns and perspectives
| Type: | Presentation |
|---|---|
| Groupwork: | Group, individually assessed |
| Weight: | 30% |
Assessment task 3: Data-driven innovation design challenge
| Type: | Project |
|---|---|
| Groupwork: | Group, group and individually assessed |
| Weight: | 40% |
Minimum requirements
Students must attempt each assessment and achieve an overall pass in this subject.
Students are required to attend 80% of all scheduled classes in this subject. 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.
