As part of my applied data science training, I completed a series of in-depth assignments rooted in both practice and theory, drawing from the latest academic and industrial approaches in machine learning and statistical analysis. These projects were directly inspired by methodologies from MILA, Google Research, and SFU, and they pushed me to integrate technical execution with foundational concepts.

Throughout the curriculum, I studied and implemented core principles of supervised learning, statistical inference, data preprocessing, and interpretability. Early in the course, I worked with real-world climate and audio datasets, applying Numpy and Pandas to clean and structure complex data, and built pipelines to analyze monthly precipitation and audio segments from YouTube. This included pairwise correlation analyses, outlier detection using IQR methods, and visualization techniques to explore data distribution.

I also deepened my understanding of model training, explainability, and performance evaluation. I trained and assessed Random Forest classifiers, implemented feature selection using permutation importance, and interpreted models through Partial Dependence Plots and SHAP values — bridging statistical intuition with real-world decision-making. These tools helped reveal how specific features contributed to predictions and how they interact in non-linear models.

Beyond local modeling, I gained hands-on experience in deploying scalable ML systems. I containerized multi-component applications (Flask and Streamlit) using Docker, configured build processes with Google Cloud Build, and deployed services to production via Cloud Run. These tasks required me to manage CI/CD pipelines, understand service isolation and API orchestration, and handle real constraints such as model loading time and concurrency issues.

Each assignment was grounded in both algorithmic thinking and business-relevant evaluation: from testing normality and variance assumptions in Reddit comment distributions, to quantifying bias in chess ranking data via permutation testing. I also explored the societal and ethical impact of algorithmic outputs, aligning technical work with broader concerns of fairness and transparency.

This coursework sharpened my ability to approach problems from first principles, design reproducible pipelines, and critically assess the implications of my models. It strengthened both my theoretical foundation and system-level thinking — preparing me to contribute meaningfully to organizations seeking trustworthy, explainable, and human-aligned AI solutions.