Real Projects, Real Skills
Students don't just learn financial analysis here. They work on actual datasets from Australian businesses, build dashboards that matter, and present findings that people actually use. It's messy sometimes, but that's the point.
Our autumn 2025 cohort wrapped up last month, and honestly, the work surprised us. These weren't polished corporate presentations – they were genuine attempts to solve complicated problems with incomplete information. Which is exactly what real financial work looks like.
What Students Actually Build
Every project starts with a dataset that isn't clean. Because when do you ever get clean data? Students spend weeks wrestling with spreadsheets, identifying patterns, and figuring out what questions they should even be asking.
The autumn group worked with three different Australian SMEs. One was a retail chain tracking inventory across eight locations. Another was a manufacturing business trying to understand seasonal cost variations. The third was a service company that couldn't figure out why their margins kept shrinking.
Inventory Analysis Project
Built predictive models to reduce overstock by tracking historical patterns across multiple locationsCost Variance Dashboard
Created interactive visualizations showing quarterly trends and seasonal impacts on production costsMargin Analysis Study
Identified service delivery inefficiencies through detailed breakdown of time and resource allocationForecasting Model
Developed cash flow projections using three years of historical data and market indicatorsHow Projects Actually Work
We don't hand students a neat assignment with clear instructions. They get messy real-world scenarios where the path forward isn't obvious. That's uncomfortable at first, but it's where learning happens.
Data Discovery Phase
Students receive raw datasets with minimal context. They spend the first two weeks just understanding what they're looking at, cleaning errors, and forming initial hypotheses about what might be worth investigating.
Analysis and Iteration
The middle six weeks are where things get interesting. Students build models, test assumptions, realize they were wrong, and start over. They present weekly progress updates and get feedback from peers and mentors who challenge their thinking.
Visualization Development
Once the analysis holds up to scrutiny, students learn to communicate findings visually. This means building dashboards that non-financial people can understand. It's harder than it sounds. Most first attempts are terrible.
Final Presentation
The last three weeks focus on turning analysis into recommendations. Students present to the actual businesses whose data they used. Sometimes their findings get implemented. Sometimes they don't. Both outcomes teach valuable lessons.
Reuben Lockhart
Project Supervisor
Learning Through Real Feedback
Reuben has been running these project cohorts since early 2024, and he doesn't sugarcoat feedback. Students work in small groups of four, which means everyone's contribution matters. If someone isn't pulling weight, the whole team feels it.
The approach is straightforward – weekly check-ins where students present their progress, explain their methodology, and defend their conclusions. Reuben asks uncomfortable questions. Other students point out flaws. It's not mean-spirited, but it's not gentle either.
What makes it work is the focus on practical application rather than theoretical perfection. Students learn tools like Excel power queries, Python for data cleaning, and Tableau for visualization. But more importantly, they learn to think critically about financial information and communicate findings clearly.
Recent Student Work
These are actual projects from our 2024 and early 2025 cohorts. They're not perfect, and students would probably do things differently now. But they represent genuine attempts to analyze real business problems with real constraints.
Multi-Year Revenue Analysis
A four-person team spent eight weeks analyzing three years of sales data for a regional distributor. They identified seasonal patterns that management hadn't noticed and built a forecasting model that's still being used.
Operating Cost Breakdown
This group worked with a small manufacturer trying to understand why profitability varied so much month to month. The students built a detailed cost allocation model that revealed inefficiencies in production scheduling.
Cash Flow Projection Tool
A service business wanted better visibility into future cash positions. Students created a rolling twelve-month forecast model with scenario planning. The first version had bugs, but by week eleven they had something genuinely useful.
Applications Open September 2025
The next project cohort starts in late September and runs through December. We take twenty students per intake, working in groups of four. If you're interested in working on real financial analysis projects with actual business data, applications open three months before the start date.
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