FIT3221 Data science in practice: Project 2
Faculty of Information Technology
FIT3221 Data science in practice: Project 2 is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology. It isn't offered in 2027. It needs FIT3220 and unlocks 1 unit, leading on to 2 units in all.
- Credit points
- 6
- Offered in 2027
- Not offered
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Requisites
Before FIT3221
Prerequisites
Pass these before you enrol.
After FIT3221
1 unit list FIT3221 as a prerequisite or corequisite.
Overview
This unit is the capstone unit in the Data Science practice stream and, for most students, the final stage of the Monash IT Industry Practice Program. You will make a substantial individual contribution to a student-led company through real projects, products or services that use data to generate insight, support decisions, develop models or communicate evidence.
At this stage, you are expected to operate with professional independence, contribute to company direction and outcomes, and demonstrate readiness for industry or further study. You will take responsibility for your own professional and academic development, shape the evidence you need to demonstrate capability, and contribute to the sustainability of the company through data analysis, modelling, visualisation, data governance, stakeholder engagement, mentoring, process improvement or technical delivery.
As this unit is an exit point from the practice stream for most students, you will also help ensure that the company is well positioned to continue progressing the data science work you have been engaged with. This may include documenting analytical decisions, improving data workflows, preparing handover materials, supporting continuity of data and model knowledge, mentoring successors, strengthening team practices, and identifying next steps for the company’s products, services or projects.
You will prepare a showcase portfolio that presents evidence of your capability, achievements, professional growth and individual contribution. This portfolio will support reflection on your development across the practice program and provide a curated record of your work that can be used when applying for industry roles, placements, graduate opportunities or future study.
Your work will require you to apply data science, data visualisation and machine learning practices with judgement, including attention to data quality, uncertainty, bias, interpretation, privacy, ethics, social impact, project constraints and stakeholder needs. Assessment is centred on an individual evidence-based portfolio that demonstrates your contribution, capability, judgement, growth, readiness for professional practice, and contribution to the ongoing sustainability of company work.
Offerings in 2027
The 2027 handbook lists no offerings for FIT3221.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Apply responsible data science practices by addressing security, privacy, ethical, social good, accessibility, sustainability, bias, fairness, transparency and stakeholder impact considerations in company project work;
- 2
Direct your professional and academic development through self-assessment, targeted development planning, feedback response and evidence-based reflection, preparing a showcase portfolio that communicates your capability, achievements and readiness for industry or further study;
- 3
Communicate data, evidence, uncertainty, model behaviour, risks, insights and outcomes clearly to technical and non-technical stakeholders using appropriate professional and visual forms;
- 4
Plan, coordinate and sustain company data science work by managing tasks, risks, dependencies, quality expectations and handover processes, collaborating across teams to position datasets, models, visualisations, products or projects for continued progress beyond your contribution;
- 5
Investigate complex data problems, evaluate alternative approaches and justify defensible solutions using evidence, experimentation, analytical reasoning and professional judgement;
- 6
Apply contemporary digital, data, programming and design practices to develop, adapt, test, document and evaluate data workflows, analytical products or model-enabled system elements that meet stakeholder needs and project constraints;
- 7
Select, apply and critically evaluate data science, data visualisation and machine learning methods to acquire, prepare, analyse, model, visualise and communicate data in authentic professional contexts.
Common questions
More details
- Credit points
- 6
- Level
- 3
- Study level
- Undergraduate
- Faculty
- Faculty of Information Technology
- Type
- Coursework
- EFTSL
- 0.125
- Student contribution
- SCA Band 2
- Study abroad
- Available
- Handbook years
- 2027