Dev/OPs, Building Data Science Codes

Dev/Ops — A Return to Classic Coding Methodology

In an age where programming is mostly about managing content and channels, it's refreshing to see problem-solving codes being built the way they started out being built: Converting complex mathematical expressions into logical steps of simple arithmatic statements. No waterfall. No Agile. Just punching out real computational power, problem by problem as they presented themselves.

Science and Data

One of the reasons I like this part of the Full Stack is that it allows me to share the special roots of programming. Programming is how scientists taught machines to interrogate data, and to get the data to give up important answers to the burning questions facing science and engineering. Since data science is about getting answers, this connection to early programming practices can be informative.

So Much More Can Be Done!

But moving into "the now," it's just plain awesome what can be done once you get the data talking to you. You can animate large N-body calculations. You can render potential power generation of near-shore tidal flows. Even the weather reports are getting a bit better each year. It's a great time to be in data science.

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Producing well-managed code within adaptable discipline in Washington, DC

With the concept of Dev/Ops we come to the first of the skill sets that pertains to the delivery of data science tools and capabilities.

Dev/Ops was the original mode of software development. Physicists translated their closed-form solutions into numerical algorithms to solve complex problems more quickly than could be done with human computers. Many physical scientists still do much of their own programming, mostly for the same reason health-conscious people cook their own food: They want to know what's in it.

A data science team needs to be able to produce robust codes and apps on much shorter time scales than were tolerated in the large system-integration days. Development is facilitated through the use of libraries of single-function codes, much like the libraries built up by physical scientists before coding became a semi-skilled trade. The capture of the customer's wishes is facilitated through any of a large number of commonly available collaborative tools.

Don't be shy about implementing those collaborative tools and making your customer use them. You both will be glad that you did.


How it works — LO+FTTM, Problems, and Innovation

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Even in pure research contexts
it's all about problem solving.


Problem solving always begins with
careful problem characterization.


Innovation is the art of turning
a great solution into a great application.

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