Coursework and projects from my Data Science Fundamentals class. The capstone piece is Profiling Academic Trajectories, a full ML exploration of student performance data with clustering, classification, and interpretability.
- Profiling-Academic-Trajectories — Largest project; clustered and classified higher-ed student performance using UCI’s Higher Education dataset, plus SHAP-driven feature insights and an internal codebook of categorical encodings.
- Life-Expendancy-Analysis — WHO life expectancy deep dive with cleaning, country standardization, exploratory visuals, clustering, and PCA for dimensionality reduction.
- Data-Analysis-and-KNN — k-NN classification on the Palmer penguins dataset with data cleaning, feature engineering, and evaluation metrics.
- Data-Analysis-and-PCA — PCA-driven exploration of the UCI Wisconsin Breast Cancer dataset, including preprocessing, scaling, and model performance checks.
- Gradient-Descent — Gradient descent exercises with custom visualizations of cost functions and step-by-step optimization behavior.
- Data-Ethics — Written analysis of three data ethics articles covering governance, illusory truth effects, and responsible big-data research.
- Lecture-Notes — Lecture notebooks and sample datasets used throughout the course.
- Notebooks live alongside small data drops in each folder; see per-project READMEs for dataset pointers.
- Python dependencies are listed in requirements.txt; individual notebooks may also pin versions inline.