About Me

I am currently studying Data Science at Monash University, with a background in software engineering. I enjoy working with real-world data to uncover insights, build visualisations, and create tools that support better decision-making. I am passionate about solving problems through thoughtful analysis and continuous learning.

Work Experience

Data Specialist Intern - IBM

Aug 2022 - Nov 2022

Grocery Team Member - Woolworths

May 2024 - Present

Sushi Preparer and Sales Assistant - Sushi Sushi

Dec 2021 - Feb 2024

Cafe Barista - McDonald’s

Aug 2021 - Dec 2021

Leadership and Volunteering Experience

Sales and Attraction Manager - AIESEC Australia

Aug 2022 - Jan 2023

Program and Events Coordinator - AIESEC Australia

Feb 2022 - Jul 2022

Portfolio

This portfolio features projects completed as part of my Data Science studies. Each project explores real-world questions using data analysis and visualisation. From understanding Melbourne’s café landscape to examining public transport accessibility, these works demonstrate my ability to extract insights, build interactive tools, and communicate results clearly.

Exploring the Distribution of Dining Spots in Melbourne

This project, developed using R Shiny, investigates Melbourne’s café and restaurant landscape through interactive data visualisations. By analysing the relationship between location, price levels, and customer satisfaction, it highlights trends in dining density, affordability, and quality across different suburbs.

Key features include a Leaflet-based map of venue locations, interactive bar and scatter plots, and dynamic controls such as a suburb filter and year slider, allowing users to explore how Melbourne’s dining scene has evolved over time. The dashboard offers valuable insights for both consumers and hospitality business owners.

Tools & Skills Used:

R Shiny, Leaflet, ggplot2, Plotly, dplyr

Data wrangling and cleaning
Interactive dashboard design
Visual storytelling with user-centred design principles (FDS, Gestalt, colour theory)

Analysing Public Transport Accessibility in Melbourne

This project explores accessibility gaps in Melbourne’s public transport network using spatial data analysis and visualisation. Focusing on the post-COVID context, it identifies suburbs with limited or no access to bus, tram, or train services, providing insights for future planning and infrastructure improvements.

The project uses spatial queries to calculate stop density across mesh blocks and identify underserved regions. Choropleth maps created in QGIS highlight coverage disparities across Melbourne, offering a clear view of transport inequality and network strengths.