University of Technology Sydney

36105 iLab: Capstone Project

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: 12 cp
Result 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.


This innovation lab (iLab) - capstone subject focuses students towards designing and investigating a data-driven prototype in using contemporary techniques and multi-structure data sets. This subject is unique compared to those in other data science courses in that it allows students to work on real world problems using experimental approaches from conceptualisation, design to data search and analytics to proposing solutions. This simulates a ‘virtual’ work environment type scenario making it highly applicable in a real working environment whilst still allowing both disciplinary and transdisciplinary approaches to be pushed to its boundaries towards innovative ideas. Student teams work on diverse data-driven solutions for complex real-world challenges. 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 in the lab environment, suitable for adaption in a work context. Students communicate the outcomes of their innovative lab investigations to relevant audiences.

Subject learning objectives (SLOs)

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

1. Research and apply critically and creatively the knowledge and skills gained to complex challenges by engaging with a variety of data and different analytic techniques, building upon transdisciplinary skills to be an ‘agent of change’ in the data science profession.
2. Investigate, analyse and evaluate open data sources ethically using problem finding and problem solving techniques, including human centred approaches to enrich data sense making, inform trends and propose new ideas, products or services.
3. Translate business issues into a data science project, interpret and present the project results in context meaningfully for effective communication with all stakeholders.
4. Contribute to effective team dynamics by behaving and managing real-life project tasks professionally.

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. Students apply transdisciplinary thinking to come up with new ideas and recommend insights for innovation opportunities. Students engage with team members and derive data driven solutions.

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. An evolving, emergent approach to data science challenges is implemented in team projects students choose to tackle across the session. Guided by the teaching team, students explore and identify radical and experimental approaches to data science discovery that allow students to push the boundaries of their data science knowledge and skills, with an aim to generate new knowledge on how to tackle complexity through data explorations/analytics/visualisation, etc. 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 any stakeholders in the iLab program.

Self-Development: Individual work on project and report will provide opportunities for personal reflection and integration of the work on the emerging innovations. Interactive thinking continues to frame students' curation, consolidation and communication of evidence of their learning and development of graduate attributes and professional evolution.

Content (topics)

Application of key knowledge, skills and capabilities gained in the MDSI course to date

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: Assessment task 1: Progress and review


Part A: Group/team attendance (tracked via session class roll) to discuss and work towards achieving project goals (AT1A) – 10%

Part B: Individual literature review of 10 relevant publication in project area towards developing a 5min digital media presentation submission, on the same (AT1B)- 20%



Type: Portfolio
Groupwork: Group, individually assessed
Weight: 30%

Assessment task 2: Assessment task 2: Project outcomes


Final project report- individual (AT2) (40%)

Type: Report
Groupwork: Individual
Weight: 40%

Assessment task 3: Assessment task 3: Presentation and evaluation


Final Presentation (AT3A) and Peer evaluation (AT3B) (30%)

Type: Presentation
Groupwork: Group, individually assessed
Weight: 30%

Minimum requirements

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

Late penalties apply as outlined in the UTS student booklet. Please consult this booklet for other useful information including Special Consideration, Plagiarism, Extension, and Student Support Services.


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