DataSciPy: Program Languages Influence How We Code

The assumptions of data science root deeply in the foundations of computing. It is a long trip between our code, the processing units, and user-detectable output which each language accounts for and accommodates differently.

These engineering solutions are consequential to the fundamental assertions of data science, and understanding them will aid us in our work.

Today we will look at how Python 3 and Julia 0.7: [1] represent the numerical values of ∅ (nothingness), float, and integer; [2] build and bundle these into arrays; and [3] provide functional methods to operate on these values.

Presenter: Jason Grafft is a human factors researcher at the University of Minnesota Medical School's simulation laboratory, SimPORTAL, working primarily with streaming data using functional programming in a polyglot development environment. He develops data farming, handling, and modeling techniques with the long-term goal of producing validatable, automated, expert-informed assessment of skills performed by healthcare providers.

https://www.simportal.umn.edu