About
The person behind the notebook
I'm flpvvvv, a data scientist drawn to the part of the discipline where careful mathematics meets clear explanation. I'm interested in turning statistical ideas into something readable without flattening their rigor.
This site is intentionally blog-first. Rather than presenting a portfolio of polished claims, it foregrounds the work itself: notes, derivations, definitions, examples, and the gradual sharpening of intuition.
What I Work On
I explore the intersection of statistics, machine learning, and data analysis. That means building models, understanding uncertainty, and translating complicated quantitative ideas into forms that can actually be used.
The throughline is interpretation: not only what the data says, but how confidently it says it, what assumptions are hiding underneath, and how to communicate that honestly.
This Notebook
The archive began as study notes around MITx: 18.6501x Fundamentals of Statistics and grew into a broader writing practice. The posts span probability theory, estimation, hypothesis testing, Bayesian reasoning, and adjacent questions in machine learning.
Writing is part of the learning process. Publishing the notes forces clarity, and clarity usually reveals what I do and do not yet understand.
How I Think About Explanation
Good technical writing should preserve the sharpness of the original mathematics while removing unnecessary friction. I care about typography, pacing, notation, and visual structure because they change whether a proof feels opaque or legible.
That is why this version of the site treats equations as first-class content rather than decorative interruptions in a blog template.