NICHOLAS HA

Data Science graduate from UNSW with a strong foundation in statistical analysis, machine learning, and data visualisation. Skilled in leveraging tools such as Python, SQL, and R to uncover insights and drive data-driven decision-making. Passionate about solving complex problems through critical thinking and collaboration, with experience in both academic projects and real-world applications. Eager to contribute analytical expertise to innovative teams and make a meaningful impact.

Data Engineering and Analyst project with Oracle

• Analysed large-scale datasets to optimise the fastest lap paths for F1 drivers.
• Conducted extensive Exploratory Data Analysis (EDA) and applied feature selection techniques to identify critical variables influencing lap performance to pass to modelling team.
• Performed data cleaning and interpolation to address missing or inconsistent data, ensuring the dataset's accuracy and reliability.
• Derived actionable insights from complex data patterns, supporting strategic decision-making to improve lap times.

Data Science and modelling project with KDDI

• Developed machine learning models to classify device types using large-scale datasets with over 1 million rows and 60 features.
• Conducted extensive data preprocessing, including data cleaning, feature selection, and normalization, to prepare the dataset for classification.
• Explored various algorithms, including Support Vector Machines (SVM), Gradient Boosting (XGBoost) to improve prediction accuracy and scalability.
• Gained proficiency in working with high-dimensional data and applying machine learning techniques for cybersecurity applications.