🎓 Education:

• Master's in Data Science – University of Melbourne
• Bachelor's in Statistics – University of Melbourne

💼 Aspiring Data Analyst with a strong foundation in statistical modeling, machine learning, and data visualization. Passionate about solving real-world problems through data-driven insights.

• Programming Languages: Python (Pandas, NumPy, Scikit-learn, XGBoost), R, SQL
• Data Visualization: Tableau, Matplotlib, Seaborn
• Machine Learning: Regression, Classification, Clustering, Time Series Forecasting
• Big Data Tools: Kubernetes, Kafka, Elasticsearch, Docker
• Data Wrangling & EDA: Jupyter Notebook, ETL pipelines, data cleaning, feature engineering

• Built an interactive Tableau dashboard analyzing traffic accidents in Victoria, visualizing trends, hotspots, and demographics.
• Tech Stack: Tableau

• Developed machine learning models to predict NOx (Nitrate/Nitrite) concentrations from low-frequency data, supporting real-time monitoring of river water quality.
• Conducted EDA to analyze water quality parameters (e.g., temperature, pH, turbidity) and identified key patterns.
• Trained models using Gaussian Process Regression (GPR), Random Forest (RF), and XGBoost, achieving high predictive accuracy.
• Tech Stack: Python, Scikit-learn, XGBoost, Pandas, NumPy

• Built a Kubernetes-based multi-cluster system for ingesting and analyzing large-scale urban data streams in real-time.
• Implemented pipelines for diverse data sources (e.g., sensor data, social media feeds) and performed system optimization to ensure low latency.
• Tech Stack: Kubernetes, Elasticsearch, Kafka, Docker, Python

• Developed an interactive R Shiny dashboard to visualize Melbourne's attractions, transport routes, and entertainment hotspots.
• Tech Stack: R Shiny, ggplot2, Tableau

• Conducted comprehensive EDA to uncover actionable insights, including trend analysis and data visualization.
• Tech Stack: Python (Pandas, Matplotlib, Seaborn), Jupyter Notebook

• Analyzed the factors affecting rental prices in Melbourne, such as proximity to landmarks (universities, hospitals, stations), crime rates, and land use patterns.
• Conducted spatial analysis, correlation studies, and regression modeling to uncover the relationships between rental prices and various socio-economic factors.
• Implemented machine learning models including OLS regression, spatial lag models, and K-means clustering to identify key drivers of rental price variations.
• Tech Stack: Python, GeoPandas, Pandas, Matplotlib, Scikit-learn

• Advanced machine learning techniques for time series data.
• Efficient data pipeline development with distributed systems (e.g., Kafka, Kubernetes).
• Advanced statistical techniques for multivariate data analysis.

🎯 Career Goal:
To join a forward-thinking team as a Data Analyst, where I can apply my statistical expertise, programming skills, and passion for uncovering insights from data to drive impactful decisions.