This unit provides a capstone experience where students integrate and apply knowledge and skills acquired in earlier units to a substantial real-world information technology project. Students work in teams of typically four to six members, supervised by an academic and/or industry mentor. Each team is responsible for managing their project through group meetings and mentor/client meetings, submitting group deliverables against milestones, preparing the necessary design documentation, and choosing and following appropriate software processes. Individuals are expected to perform professionally in a team-based environment and reflect on their contributions to the team. A number of lectures are given to teach communication skills, principles of project management and the fundamental ethical and social principles involved in large-scale data science projects. The academic objectives of this unit are to lead students through a sizeable team-based effort to go through design, synthesis, development and deployment, the full data science practice life cycle, to better prepare them as professional data scientists.

Students are able to (1) assess the scope, dimensions, scale of effort and indicative costs of a complex data science project; (2) analyse data by carrying out exploratory data analysis; (3) build predictive or other suitable computational models for data-driven business decision making; (4) evaluate model performance against a set of standard metrics recognised by the data science community; (5) interpret model results meaningfully for effective communication with all stakeholders via written, oral and visual means; and (6) perform professionally as an individual and as part of a multidisciplinary and multicultural team.