Melbourne Data Analyst Portfolio

Data case studies built around business decisions.

Selected analytics projects focused on Power BI reporting, SQL analysis, customer segmentation, operational dashboards, data quality, and stakeholder-ready recommendations.

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Capability focus

What these projects are designed to prove

The goal of this page is not to show every project equally. It highlights the work most relevant to data analyst, BI reporting, commercial analyst, and operational reporting roles in Melbourne.

BI & Reporting Power BI dashboards, KPI cards, slicers, DAX logic, stakeholder reporting.

SQL

SQL Analysis Relational analysis, joins, aggregation, customer/revenue queries, database thinking.

Customer Analytics Segmentation, retention, reactivation, customer value, commercial targeting.

OPS

Operational KPIs Route demand, service patterns, volume trends, performance monitoring.

Data Quality Cleaning, validation, missing values, consistency checks, prepared outputs.

Selected work

Featured case studies

Each project is presented from the angle hiring managers usually care about: the business problem, the analytical approach, and the decision the work supports.

CASE STUDY 01

Power BI · Python · Customer Analytics · Commercial Strategy

Retail Customer Segmentation & Behaviour Dashboard

Best aligned with BI Reporting, Commercial Analyst, Customer Performance, and eCommerce Analytics roles.

Business problem: A large retail customer base needed to be translated into practical customer groups so commercial teams could prioritise retention, upsell, and reactivation activity.

Prepared and structured a 162K+ customer dataset for dashboard reporting.
Built behavioural segmentation across order activity, reorder behaviour, income group, region, and customer value.
Created a Power BI dashboard highlighting 11 strategic customer segments and the largest reactivation opportunity.

Decision value: Supports CRM, marketing, and commercial teams in deciding which customers to protect, grow, reactivate, or monitor.

PythonpandasPower BIDAXData CleaningCustomer Segmentation

View Case Study → GitHub →

CASE STUDY 02

Python · Streamlit · Geospatial Analysis · Operational Reporting

NYC Citi Bike Operational Dashboard

Strong fit for operational analytics, transport/logistics reporting, geospatial analysis, and dashboard roles.

Business problem: Bike-share operators need to understand high-demand routes, station imbalance, commuter patterns, and service pressure across the network.

Built an end-to-end analysis using Citi Bike trip data, station-pair aggregation, geospatial mapping, and weather integration.
Identified high-density commuter zones, peak travel windows, and high-volume route patterns.
Deployed an interactive Streamlit dashboard so users can explore operational patterns online.

Decision value: Supports bike redistribution, station planning, service optimisation, and operational decision-making.

PythonpandasStreamlitKepler.glPlotlyNOAA Weather Data

View Case Study → Live App → GitHub →

CASE STUDY 03

PostgreSQL · SQL · Revenue Analysis · Tableau

Rockbuster SQL Customer & Revenue Analysis

Best proof of SQL, relational database analysis, joins, customer revenue analysis, and executive reporting.

Business problem: A movie rental business needed to understand customer value, market performance, and revenue concentration before moving toward an online streaming model.

Queried a 15-table PostgreSQL database covering customers, payments, rentals, inventory, films, and geography.
Used joins, aggregations, subqueries, and CTE-style analysis to identify revenue drivers and high-value markets.
Translated SQL outputs into business-facing visuals and recommendations.

Decision value: Supports market prioritisation, customer targeting, and revenue-focused decision-making.

PostgreSQLSQLpgAdminTableauExcelData Modelling

View Case Study → GitHub →

CASE STUDY 04

Tableau · Excel · Public Health · Resource Planning

Influenza Resource Planning Dashboard

Relevant for health, government, public-sector reporting, resource planning, and risk-focused analytics roles.

Business problem: Public health teams need to understand where flu mortality risk is highest, when seasonal peaks occur, and which age groups require the most resource planning attention.

Analysed flu-related mortality across US states, age groups, and time periods.
Built Tableau dashboards showing seasonal recurrence, state-level concentration, and age-based risk patterns.
Highlighted that adults aged 65+ accounted for the largest share of flu-related deaths.

Decision value: Supports staffing, preparedness planning, targeted intervention, and public-health resource allocation.

ExcelTableauForecastingPublic Health DataData Storytelling

View Case Study → GitHub → Tableau →

CASE STUDY 05

Python · Tableau · Market Analysis · Geospatial Insights

King County House Market Analysis

Useful for commercial analysis, market research, pricing analysis, location analysis, and stakeholder dashboards.

Business problem: Housing market stakeholders need to understand which property, location, and quality factors influence pricing and affordability.

Cleaned and structured housing, mortgage, and GIS datasets for analysis.
Engineered fields such as price per square foot, grade groups, city-level price indicators, and market segments.
Built Tableau dashboards showing price drivers, premium cities, affordability differences, and location-based patterns.

Decision value: Supports market segmentation, pricing analysis, investment decisions, and location-based reporting.

PythonpandasTableauGIS DataMarket SegmentationEDA

View Case Study → GitHub →

Analytical workflow

How I approach data work

The same structure is used across the projects: clarify the question, prepare reliable data, build useful metrics, and translate findings into practical recommendations.

Define the questionStart with the business decision, reporting need, or operational problem.

Prepare the dataClean, validate, merge, structure, and document the dataset before analysis.

Build metricsCreate KPI logic, calculated fields, segments, flags, and analytical outputs.

Analyse patternsIdentify trends, outliers, high-value groups, risks, and operational pressure points.

Visualise clearlyBuild dashboards and reports that non-technical stakeholders can understand quickly.

Recommend actionTranslate findings into next steps, priorities, and business recommendations.

Positioning

Commercially grounded analytics

My background combines hands-on data analytics training with years of operational leadership, commercial discipline, stakeholder communication, KPI thinking, and real-world performance analysis.

✓Power BI and dashboarding: reporting outputs built for decisions, not decoration.

✓SQL and Python: data preparation, analysis, validation, and structured outputs.

✓Business translation: clear recommendations for commercial, operational, and stakeholder contexts.

BI reporting roles

Power BI, DAX, SQL, data modelling, dashboards, KPI cards, slicers, stakeholder reporting.

Commercial analyst roles

KPI tracking, contract/commercial thinking, performance reporting, trend analysis, operational insights.

Customer analytics roles

Customer segmentation, loyalty, reactivation, retention, basket behaviour, targeting opportunities.

Public-sector / health reporting

Data quality, resource planning, trend reporting, documentation, accuracy, and decision-ready summaries.

Need the full portfolio or resume?

Download the PDF portfolio for a polished overview of the projects, or open the resume for the formal career summary, tools, and work history.

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