Math to Data

“How does a pure mathematics researcher with mostly academic experiences pivot towards data science?” To address this question, this website was founded in 2022 to serve as a collection of resources.

This blog contains resources and notes for academic researchers who wish to pivot to data science. From abstract theory to concrete applications, one can create a portfolio to demonstrate such work. I capture and document this process with an honest account of what the project and portfolio growth look like, with hopes to create a learning trajectory (or map) for a self-teaching path. For clarity on my positionality and the beginning of this journey, please check out this curated article on Medium.

Mathematics is an awesome art — beginning with definitions and axioms (fundamental assumptions like a line can be drawn from a point to any other point), one can prove irrefutable theorems with rules of deductive logic. Such absolute certainty is beautiful, and found nowhere else in academia. The creativity involved in weaving together these arguments together is arguably a form of poetry with the strictest ruleset.

If we are trained to write proofs in math on the theory underpinning data science, then we have already done the work to understand how to read the proofs that justify algorithms. This gives pure mathematicians an advantage when acquiring new computer science knowledge.

Academic pure mathematics researchers have the knowledge, in theory.

The key to pivoting from pure math to data science, thus, must be to demonstrate that our broad theory based knowledge can be useful in data science projects.