Below are selected case studies (academic + applied) with a data-science focus.
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End-to-end SQL analytics project using the Lahman Baseball Database. Designed a complete relational workflow with schema creation, reusable views, advanced CTEs, window functions, and business-focused analyses on talent pipelines, salary dynamics, and player careers.
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Implemented core machine learning algorithms — from regression and classification to clustering and deep learning — through applied projects in Python. Focused on building intuition for model training, evaluation, and interpretability using Scikit-learn and Jupyter Notebooks.
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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.
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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.
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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.
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Developed a hierarchical Bayesian workflow to quantify partial altitudinal migration and system-wide community reshuffling across elevation and season in the Australian Wet Tropics.
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Developed the analytical framework using generalised linear models to assess abundance changes of tropical species across forest gap gradients.
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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.
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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.
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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.
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Developed Bayesian hierarchical models incorporating detection probability to forecast population viability and support elevated conservation status for imperilled species.
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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.
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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.
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Applied ensemble machine learning to convert presence-only niche models into abundance predictions, combining big-data wrangling, advanced analytics, and multi-algorithm ensembles.