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Hi, I'm Michael Dang. I'm a data analyst based in Melbourne, specialising in Python, SQL and data visualization. I love turning complex datasets into clear, actionable insights and building dynamic dashboards that inform strategy and operations. My background spans academic, open-source and industry projects, from predictive modelling to decision-support systems. I enjoy collaborating with stakeholders to shape impact, automate data pipelines and keep learning in fast-paced environments like AI and automation.…Hi, I'm Michael Dang. I'm a data analyst based in Melbourne, specialising in Python, SQL and data visualization. I love turning complex datasets into clear, actionable insights and building dynamic dashboards that inform strategy and operations. My background spans academic, open-source and industry projects, from predictive modelling to decision-support systems. I enjoy collaborating with stakeholders to shape impact, automate data pipelines and keep learning in fast-paced environments like AI and automation.

Michael Dang

Data Analyst, Data Scientist, Designer, +4

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Hi, I'm Michael Dang. I'm a data analyst based in Melbourne, specialising in Python, SQL and data visualization. I love turning complex datasets into clear, actionable insights and building dynamic dashboards that inform strategy and operations. My background spans academic, open-source and industry projects, from predictive modelling to decision-support systems. I enjoy collaborating with stakeholders to shape impact, automate data pipelines and keep learning in fast-paced environments like AI and automation.…Hi, I'm Michael Dang. I'm a data analyst based in Melbourne, specialising in Python, SQL and data visualization. I love turning complex datasets into clear, actionable insights and building dynamic dashboards that inform strategy and operations. My background spans academic, open-source and industry projects, from predictive modelling to decision-support systems. I enjoy collaborating with stakeholders to shape impact, automate data pipelines and keep learning in fast-paced environments like AI and automation.

Available to hire

Hi, I’m Michael Dang. I’m a data analyst based in Melbourne, specialising in Python, SQL and data visualization. I love turning complex datasets into clear, actionable insights and building dynamic dashboards that inform strategy and operations.

My background spans academic, open-source and industry projects, from predictive modelling to decision-support systems. I enjoy collaborating with stakeholders to shape impact, automate data pipelines and keep learning in fast-paced environments like AI and automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Sport Data Analyst Internship at Paralympics Australia

June 1, 2025 - June 1, 2025

Internship focusing on data-driven performance insights for table tennis. Designed Power BI dashboards visualizing player performance metrics, match dynamics, scoring anomalies, and integrated data on eating habits, healthcare and training strategies. Improved winning chance by 20% and resting period efficiency by 25-35%. Analyzed mobility factors with vitamin D, identifying 60% contribution to mobility improvements and noting 45% of players experiencing wrist/elbow strain and 10% higher psychological stress, necessitating 15-20% more movement monitoring during training. Mapped swing patterns to inform defensive drills and predictive modeling (76% default to backhand-right when cornered; ~30% switch between backhand/forehand). Reduced video storage by 40% via a Python-based frame-filtering algorithm. Automated ETL scripts improved data refresh latency by over 20%. Benchmarked ML/DL models (CNN, LSTM) on AWS EC2/NCI clusters to classify swing types and predict scoring outcomes, achievin

Project Lead – Engine Team | Project Echo at DataBytes Geelong

December 1, 2024 - December 1, 2024

Led data engineering and analytics for acoustic signature research. Designed an interactive Power BI dashboard to visualize species-specific acoustic signatures and clustering of calls between 500–2,000 Hz and 3,000–4,000 Hz. Cleaned and denoised over 1.2 TB of wildlife and environmental audio data, reducing storage by 20% and improving downstream model training efficiency by reducing bit depth, trimming silence, and converting to mono. Built scalable SQL data models to structure and query multi-species datasets across 10,000+ labeled clips. Optimized audio pipelines using lightweight ML models (MobileNet, YamNet), achieving ~30% lower power consumption on edge devices while maintaining >85% classification accuracy.