Class Work
IS 574 – Business Intelligence and Analytics Systems
(Spring 2026)
Description of Class:
In this course, I studied BI and data architecture design, from data warehousing models to dashboard and UX design, then applied that knowledge to a full group BI project for a fictional e-commerce company. The coursework connected data architecture, ETL design, and dashboard storytelling into one end-to-end analytics system.
Key Projects:
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TriMetrics BI Solution (Group Project): I helped design a full BI solution pulling data from three source systems through an ETL pipeline into a Snowflake data warehouse, including an RFM-based customer segmentation model and four Power BI dashboards. I authored the data architecture and dashboard design documents and built the visual specs in Figma.
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BI Architecture Business Case: I evaluated a "buy" cloud BI architecture against a fully custom "build" architecture for an e-commerce use case, weighing deployment speed and cost against control and integration complexity.
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BI Adoption in Construction Research Paper: I researched how BI is used in construction project management, including real-time budget and safety dashboards, connecting the analysis back to my prior AEC marketing experience.
DSC 430 – Programming for Data Science
(Spring 2026)
Description of Class:
In this course, I built on my Python foundation with an emphasis on clean, professional code and applied data science tooling, covering algorithms, object-oriented programming, Monte Carlo simulation, and data analysis with NumPy, Pandas, and Matplotlib. Each assignment was submitted with a recorded video walkthrough, graded on correctness, efficiency, and code readability.
Key Projects:
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Spotify Data Analysis (SpotiPy): I used Pandas and NumPy to analyze a large Spotify dataset, exploring relationships between artist and track attributes.
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Monte Carlo Text Simulation: I built a random-number and text-based application using the full text of War and Peace, applying Monte Carlo methods including random search and Pi approximation.
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Planetary Data & Median Filtering: I completed a final planetary-data analysis and implemented a median-filtering algorithm for image and signal processing.
DSC 412 – Applied Linear Algebra
(Winter 2026)
Description of Class:
In this course, I worked through the linear algebra foundations underlying most machine learning and data science methods, from matrix decompositions to eigenvalues and singular value decomposition. Homework combined hand-derived proofs with MATLAB verification, plus recorded video walkthroughs explaining the reasoning behind each solution.
Key Projects:
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Matrix Factorization Problem Sets: I solved systems of equations using Gaussian elimination, LU decomposition, and QR decomposition via Gram-Schmidt orthogonalization, verifying each result in MATLAB.
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Eigendecomposition & SVD: I computed eigenvalues, eigenvectors, and full singular value decompositions by hand, then verified them programmatically, building the mathematical foundation behind PCA and least-squares regression.
DSC 441 – Fundamentals of Data Science
(Winter 2026)
Description of Class:
In this course, I worked through the full data science and data mining pipeline in R, from data cleaning and preprocessing to unsupervised and supervised modeling and evaluation. Weekly assignments across real-world datasets built hands-on experience with clustering, classification, dimensionality reduction, and model evaluation beyond simple accuracy.
Key Projects:
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Global Economic Profiling Pipeline (Final Project): I built an end-to-end pipeline on a 195-country dataset, clustering countries into economic development profiles with k-means and then classifying high-potential markets with a tuned k-NN model, achieving 95.65% cross-validated accuracy and an AUC of 0.993.
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Predictive Modeling Practice: I built and evaluated linear/logistic regression, decision tree, kNN, and SVM models across datasets including US Census data, breast cancer diagnostics, and housing prices.
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Clustering & Dimensionality Reduction: I applied k-means and hierarchical clustering with multiple linkage methods, along with PCA, to surface hidden groupings in real datasets.
CSC 401-720 Introduction to Programming
(Fall 2026)
Description of Class:
In this course, I learned the fundamentals of Python programming, including variables, data types, conditionals, loops, functions, file handling, exceptions, and the basics of object-oriented programming. Weekly assignments and hands-on exercises helped build a strong foundation for more advanced data science and coding work.
Key Projects:
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ATM Simulation Program: I built a simplified ATM system in Python that reads account data from a CSV file and allows users to log in, view balances, deposit, withdraw, and print receipts. The project focused on modular design using user-defined functions, file I/O, exception handling, and proper namespace management.
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TransitCard OOP System: I built a TransitCard class in Python that models a public transit payment card, including balance checks, fare deductions, value reloads, and error handling for invalid transactions. The project focused on object-oriented programming concepts such as class and instance variables, custom methods, and exception handling.
IT 403 Statistics and Data Analysis (Fall 2026)
Description of Class:
IT 403 introduced core concepts in statistics and data analysis, covering descriptive statistics, probability, confidence intervals, hypothesis testing, and linear regression. The course emphasized real-world analysis using SPSS and focused on developing practical skills for interpreting data and applying statistical methods in empirical research.
Key Projects:
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Exploratory Data Analysis (EDA) Project: I conducted an exploratory analysis in SPSS using survey data to examine distributions, group differences, and usage patterns. The project involved generating histograms, descriptive statistics, boxplots, and normality tests to summarize the data, identify outliers, and compare trends across demographic groups.
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Regression & Relationship Analysis Project: Using SPSS, I analyzed relationships between variables through scatterplots, correlation, and linear regression modeling. The project focused on interpreting slope, intercept, R², residual patterns, and using regression equations to make predictions and assess the strength of associations.
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Confidence Intervals & Hypothesis Testing Project: I applied statistical inference methods to real-world scenarios by constructing confidence intervals and running hypothesis tests to evaluate claims about population means. This included computing z-scores, interpreting p-values, and assessing whether sample evidence supported or contradicted stated assumptions.