Quantifying Risk,
Predicting Variance.
Documenting foundational explorations in probability theory, mathematical structures, and statistical computing. Moving away from marketing to build systems rooted in pure data analysis.
The Academic Ledger
Introduction to Statistics
Descriptive analysis, central tendencies, variance indices, and sampling distribution behaviors.
Elementary Probability
Random sample variables, axiomatic probability laws, combinatorics, and conditional Bayes systems.
Linear Algebra
Vector spaces, transformations, matrix determinants, and foundational systems of linear equations.
Calculus 1
Limits optimization, continuous functions, derivative limits, and fundamental rate theorem mechanics.
Foundations of Mathematics
Mathematical proof methodologies, set theories, symbolic formal expressions, and logical inductions.
Statistical Computing
Translating theoretical mathematical frameworks into programmatic algorithms via R and Python environments.
The Research Notebook
The Intuition of Bayes' Theorem
Deconstructing how conditional probability updates our beliefs when new data or evidence comes to light. A deep dive into false-positive statistical paradoxes.
Visualizing Eigenvalues Geometrically
Tracking how linear transformation matrices stretch space without altering vector direction—the fundamental foundation of structural dimensionality reductions.
Simulating the Law of Large Numbers
Writing a basic loop in R to record 10,000 independent coin flips, mapping exactly how sample means stabilize seamlessly into expected theoretical averages.
Resource Exchange
Access study assets, shared lecture frameworks, or open code notebooks built throughout my undergraduate studies.
Academic Peer Exchange
Have a tough problem set, a shared research idea, or looking to exchange notes? Drop an entry directly into my workspace queue.