University of Technology Sydney

36102 iLab 1

12cp; 2 x block sessions, fortnightly check-in with tutors, optional partner drop-ins, plus self-directed work; availability: Master of Data Science and Innovation students only
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.

Typical availability

Spring session, City campus

Detailed subject description.

Access conditions

Note: The requisite information presented in this subject description covers only academic requisites. Full details of all enforced rules, covering both academic and admission requisites, are available at access conditions and My Student Admin.