Problem
Financial data needs to move from raw inputs into useful reporting layers without losing quality, traceability, or business context.
Portfolio
Project evidence for data, risk, and quantitative thinking.
The portfolio is where research direction becomes proof: project questions, technical decisions, tools, outputs, lessons, and the professional judgement behind the work.
Featured case study
Nedbank N*ovation Data Engineering Challenge
A banking and fintech data-engineering project built around virtual-account data, pipeline structure, quality checks, and gold-layer outputs.
Solution
Built a medallion-style pipeline that separates ingestion, transformation, validation, and curated outputs.
Tools Used
Python, PySpark-style thinking, SQL concepts, data validation, GitHub, documentation, and layered data modelling.
Outcome
Produced a structured project that demonstrates data-pipeline reasoning and a reviewable approach to analytics delivery.
Lessons Learned
Quality checks, naming, documentation, and clear assumptions matter as much as the transformation logic.
Project pipeline
How future projects should be presented.
Problem
What question, user need, data issue, or decision does this project address?
Solution
What structure, model, workflow, or analysis was built to respond to the problem?
Tools Used
Which languages, libraries, methods, datasets, and workflow tools were involved?
Outcome
What was produced, improved, clarified, or made easier to review?
Lessons Learned
What changed in Milton's understanding of data, risk, modelling, or delivery?
Technical capability
Tools grouped by how they support the work.
Programming
Python, R, SQL, Mathematica, analytical scripting, and reproducible notebooks.
Data
ETL design, validation, medallion-style pipelines, reporting layers, and documented data flows.
Finance and risk
Risk modelling, simulation, statistical analysis, quantitative finance concepts, and market structure.
Workflow
Git, GitHub, VS Code, PowerShell, Docker concepts, documentation, and version control habits.
Next portfolio moves
Turn learning into sharper proof.
Future case studies should make the work easier for recruiters, collaborators, and students to understand without digging through raw repositories.
Document the featured project
Add diagrams, validation notes, sample outputs, and project limitations.
Add research mini-projects
Publish small statistics, risk, Python, or operations research examples.
Connect portfolio to teaching
Show how analytical thinking improves explanations, study structure, and market education.