Past Meetup

Just Enough Math - for Data Science

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Advanced math for business people: “just enough math” to take advantage of new classes of open source frameworks. This is an expansion of Paco Nathan's Data Day Texas keynote talk, a preview of his workshop at OSCON (, and the basis for his new book from O'Reilly.

Many people take college math up to calculus, but never learn how to approach sparse matrices, complex graphs, supply chain optimizations, etc. Just Enough Math ties these pieces together into a conceptual whole, with clear business use cases and simple Python code, as a new approach to computational thinking.

The premise is that so many people did not continue university-level math beyond the “killing fields” of calculus, but have lots of interest in using advanced math for Big Data. So much in business is beginning to depend on that capability -- separating the Amazon's from the JC Penney's. Even so, math programs in many universities cling tenaciously to Cold War-era priorities, intent on weeding out people who would not pass requirements as engineers to build missiles, etc.

With the commercial successes of Machine Learning, Cloud Computing, etc., there are very good business cases for having “just enough math” to leverage new kinds of open source tools. These days people in business need to understand more about complex graphs, sparse matrices, Bayesian priors, optimization solvers, etc., which are not hard to learn but placed far beyond calculus.

BTW, bring your laptop with Python installed... we'll have a special preview of a new platform built by O'Reilly.

This talk is based on a "book" by Paco Nathan and Allen Day that O'Reilly will publish this summer, along with embedded video, code exercises, etc.