36102 iLab 112cp; on campus/ online (2 x 3hrs, 3 x 7hrs); independent online work and negotiated supervision and client meetings
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.
Any student wishing to enrol in first- and second-year subjects concurrently, needs to apply for a waiver.
In this transdisciplinary innovation lab, students work individually and in teams to investigate traditional and emerging big data sets, and test theories or frameworks prior to rapidly developing a data driven prototype or proof of concept. In the iLab, they generate creative possibilities by combining new data sources with existing data. Each student and team is provided with a 'sandbox' to support them in designing experiments (for real or simulated stakeholders), evaluating the potential of different software technologies and developing key aspects of thinking like a data science professional. They consider the implications of their findings for different stakeholders and write a range of data narratives to explore the communication of data results for different purposes. Immersed in a lab environment oriented towards innovation and the execution of data driven experiments using real life, 'messy' data sets, students develop and study different workflows handling the extraction of value from diverse data types.
Spring session, City campus
Detailed subject description.