36105 iLab 2
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Subject handbook information prior to 2023 is available in the Archives.
Credit points: 12 cp
PostgraduateResult type: Grade, no marks
Requisite(s): 36100 Data Science for Innovation AND 36103 Statistical Thinking for Data Science AND 36106 Machine Learning Algorithms and Applications
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
The innovation lab subject focuses students towards designing, realising and investigating a data-driven prototype in which they utilise contemporary techniques and multi-structure data sets. Student teams work on diverse data-driven solutions for complex real-world challenges presented by industry and academic partners. They develop technical outputs, project documentation and supporting artefacts in the lab environment suitable for adaption in a work context. They test new approaches from current research literature and industry standards, under the supervision of transdisciplinary staff in structured project phases and share the results of data explorations. Using a range of open data sets and suitable case studies, students hone their skills in framing questions useful for embarking upon discoveries, addressing gaps in knowledge or tackling problems. Students communicate the outcomes of their innovation lab investigations in various forms to relevant audiences. Through taking responsibility for an aspect of a workflow to extract value, or alternatively, work on a subset or a thematic area of inquiry, each student also contributes to the work stream of a major project.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
|1.||Respond to complex challenges by innovating with new sources of data and different analytic techniques|
|2.||Find and integrate open data sources to enrich in-house data sense making, inform trends and propose new products or services|
|3.||Create value for partners through problem finding and problem solving in organisations and applying human centered approaches to data science investigations|
|4.||Effectively communicate a project’s vision, execution, value and outcomes to a range of stakeholders through cogent multi-modal data narratives|
|5.||Respond to complex data challenges as ethical data professionals by managing data science projects and documenting practices to enable appropriate implementation of relevant policy and governance measures|
|6.||Build effective team dynamics and professionalism by managing real-life data science projects|
|7.||Build upon their transdisciplinary knowledge and skills to be an ‘agent of change’ in the data science profession|
|8.||Design and implement a self-directed and reflective learning process in a professional practice context|
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following course outcomes:
- Understanding relationships & processes within systems
Identify and represent the human and technical elements and processes within complex systems and organise them within frameworks of relationships (1.1)
- Exploring and testing models and describing behaviours of complex systems
Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders (1.2)
- 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)
- Developing strategies for innovation
Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data (3.1)
- 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)
- Becoming a reflective data practitioner
Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts (5.1)
- Embracing ethical responsibilities
Interrogate and justify ethical responsibilities related to data selection, access, analysis and governance to create a framework for practice (5.2)
Contribution to the development of graduate attributes
The subject provides opportunities for students to immerse themselves in a lab environment to work with real-world challenges. They apply transdisciplinary thinking to come up with new ideas and recommend insights for innovation opportunities in partner organisations. Students engage with team members and derive data driven solutions that can add value for partners.
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 3 - Create value in problem solving and inquiry
- GA 4 - Persuasive and robust communication
- GA 5 - Ethical citizenship and leadership
Teaching and learning strategies
Transdisciplinary approach: Through a dynamic immersion into the world of designing creative, value-driven solutions, this subject brings together the theoretical and practical strands of data science innovation in this subject. An evolving, emergent approach to data science challenges is implemented in team projects students choose to tackle over the session. Students work as innovators looking to add value to particular organisational or community challenges to produce working prototypes that could be the seed for real organisational innovation or a start-up enterprise at the end of session.
Team collaborations: students collaborate in design teams and utilise diverse perspectives to innovate and solve problems in data science and innovation. Disruptive, controversial and speculative thinking, as well as reflection are built upon to encourage critical approaches to working in teams.
Online work: Canvas and other online media applications continue to be used to share information and encourage interaction between students, staff and stakeholders in the iLab program.
Design journal: Individual work in a design journal for tracking progress will provide opportunities for personal reflection and integration of the team's work on the emerging innovations. Portfolio thinking continues to frame students' curation, consolidation and communication of evidence of their learning and development of graduate attributes and professional evolution.
- Application of key knowledge, skills and capabilities gained in the MDSI course to date
- Human-centred data innovation in the field
- Design-led innovation processes and mindsets
- Innovation research for specific challenges and projects
- Achieving robust and unique outcomes to current and future data challenges.
- Working with real-world data
Assessment task 1: Progress journal and reflection
Part A: Group Progress tracker for project goals (AT1A)
Part B: Progress log and reflection (AT1B)
1, 4 and 5
|Groupwork:||Group, individually assessed|
Assessment task 2: Project outcomes
|Intent:||Final project report and individual contributions (AT2)|
2, 3, 4 and 7
|Groupwork:||Group, group and individually assessed|
Assessment task 3: Presentation and evaluation
Part A: Interim Presentation (AT3A); Part B: Partner and Peer evaluation (AT3B)
2, 3, 4 and 5
|Groupwork:||Group, individually assessed|
Students must attempt all assessment tasks and achieve an overall pass mark in order to pass this subject.
Late penalties apply as outlined in theUTS student booklet. Please consult this booklet for other useful information including Special Consideration, Plagiarism, Extension, and Student Support Services.
For a round up of resources for managing & working with personal data – standards, best practice, anonymisation issues, please visit:
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