FIT3220 · Data science in practice: Project 1

Overview

This unit is the third stage of the Data Science practice program. You will continue working within a student-led company, contributing to real projects, products or services that use data to support insight, modelling, communication or decision-making. Building on earlier practice units, you will take greater responsibility for data-focused work while continuing to operate within team, project and company expectations. At this stage, you will also help sustain the company by supporting recruitment and onboarding of students entering the first practice unit. You may help define role needs, review applicant evidence, support interviews or selection activities, and induct new students into company data practices, workflows, tools and professional expectations. You will take greater agency over your professional and academic development by identifying the data science capabilities you want to strengthen. In consultation with mentors, peers and company leaders, you will set development goals, seek feedback, and build portfolio evidence of your growing capability. Your work may involve acquiring, preparing, analysing, modelling, visualising or communicating data, while considering data quality, uncertainty, bias, interpretation, governance and responsible use. Assessment is centred on an individual evidence-based portfolio demonstrating your contribution to company work, support for new students, response to feedback, and advancing data science capability. This unit prepares you for the capstone practice unit, where you will be expected to demonstrate more independent and integrated data science practice in complex professional contexts.

Offerings

The Handbook publishes no offerings for this unit.

Assessment

The Handbook publishes no assessment items for this unit yet.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Direct your professional and academic development by identifying capability goals, seeking and responding to feedback, supporting recruitment and onboarding activities, and evidencing growth through reflective practice;
2. Design and apply data science workflows that support acquisition, preparation, analysis, modelling, interpretation or evaluation of data in a company project context;
3. Develop and evaluate data visualisations, dashboards or data stories that communicate patterns, uncertainty, limitations and insights to relevant audiences;
4. Contribute to the development, evaluation or interpretation of machine learning models or model-enabled workflows, using appropriate evidence and performance measures;
5. Demonstrate advancing Data Science course capability through portfolio evidence of individual contribution, analytical reasoning, technical decision-making, feedback response and professional judgement.

Workload

No workload detail published.

Student experience, not official rules. Nothing here changes what the Handbook says.