Through the credit-bearing UChicago Pathways Program in Data Science, I completed an accelerated, university-level curriculum that condensed a full UChicago quarter of coursework into three weeks. This immersive structure strengthened my statistical reasoning, programming foundations, and applied machine-learning skills while exposing me to the rigor of college-level data science.

Across lectures, labs, and graded assignments, I worked extensively with Python, NumPy, pandas, and scikit-learn to clean, analyse, and model real-world datasets. The academic core focused on probability, inference, exploratory analysis, and end-to-end analytical workflows, from handling missing data to validating and interpreting final models.

A major component of the program involved learning, implementing, and comparing five foundational machine-learning algorithms: k-Nearest Neighbours (k-NN), Decision Trees, Random Forests, and Logistic Regression. I gained hands-on experience training and tuning these models, evaluating performance metrics, addressing overfitting, and understanding model-selection trade-offs.

The program culminated in a final research project, where I independently cleaned a complex dataset, engineered features, experimented with multiple algorithms, and presented a justified model choice supported by statistical reasoning and visualisations.

Overall, the program provided a comprehensive foundation in practical machine learning, predictive modelling, and high-standard academic communication—equivalent to completing a full UChicago data-science quarter in an intensive, three-week format.