Python Coding Challenges
Published:
Created a growing collection of short, narrative-style coding projects designed to strengthen Python reasoning, design thinking, and problem-solving fluency through realistic mini-scenarios.
Projects
Below are selected case studies (academic + applied) with a data-science focus.
Machine Learning Fundamentals – Applied Python Projects
Published:
Implemented core machine learning algorithms — from regression and classification to clustering and deep learning — through applied mini-projects in Python. Focused on building intuition for model training, evaluation, and interpretability using Scikit-learn and Jupyter Notebooks.
Exploratory Data Analysis (EDA) Projects in Python
Published:
Developed an applied EDA framework combining real-world case studies — emergency call records and financial time series — to demonstrate data wrangling, feature extraction, and visualisation workflows using pandas, seaborn, and plotly.
Altitudinal Migration and Seasonal Redistribution in Rainforest Bird Communities
Published:
Developed a hierarchical Bayesian workflow to quantify partial altitudinal migration and system-wide community reshuffling across elevation and season in the Australian Wet Tropics.
Python OOP Systems: From Procedural to Object-Oriented Design
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Developed a suite of applied Python mini-systems demonstrating the progression from procedural programming to object-oriented design and class interaction across real-world examples.
Analytical Framework for Forest Gap Gradient Effects on Tropical Species Abundance
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Developed the analytical framework using generalised linear models to assess abundance changes of tropical species across forest gap gradients.
Integrating Microclimate, Physiology, Biogeochemistry, and Population Models to Link Climate Change to Demographic Outcomes
Published:
Developed and implemented a holistic Bayesian framework integrating microclimate, mechanistic physiology, biogeochemical processes, and population dynamics to identify causal pathways from climate change to survival and recruitment.
Modelling Biogeochemical Pathway Cascades using Bayesian Hierarchical Models
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Revealed ecosystem cascades and biogeochemical pathways in tropical systems using Bayesian hierarchical modelling to quantify direct and indirect effects in complex ecological networks.
Web-Based Tool for Deck Optimisation in Magic: The Gathering
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Developed an interactive JavaScript tool using hypergeometric probability to optimise resource distribution in competitive deck building.
Detecting Climate Impacts on Rainforest Birds Using Bayesian Spatiotemporal Modelling
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Applied hierarchical Bayesian models with satellite-derived predictors to identify climate-driven population changes in rainforest birds across space and time.
Forecasting Population Viability with Bayesian Hierarchical Models
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Developed Bayesian hierarchical models incorporating detection probability to forecast population viability and support elevated conservation status for imperilled species.
Dynamic Community Re-shuffling Under Elevational Climate Shifts
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Developed a high-throughput spatial forecasting workflow of community turnover under climate change, optimising computational performance for multi-species forecasting across elevational gradients.
Assessing Bird Population Trends to Inform Conservation Priorities
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Used time-series GLMs and interactive visualisation (Shiny app) to nominate 14 bird species for elevated protection under national and international priority lists.
Predicting Species Abundance from Environmental Suitability
Published:
Applied ensemble machine learning to convert presence-only niche models into abundance predictions, combining big-data wrangling, advanced analytics, and multi-algorithm ensembles.