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Writing On Data Science Practice

Two core essays currently published on the site. Each piece is preserved in full in this redesigned version.

The 10 Commandments of Data Science Project

THE 10 COMMANDMENTS OF DATA SCIENCE PROJECT

Let's get straight to the point: 1. Post Analysis - There is no point just communicating a number. You have to analyse the numbers your algorithm throws out. Tie it back to the domain. Work with the domain experts as to what the results mean.

What prices is your algorithm recommending? What does the curve fitment for these products look like? The algorithm recommends something, does it make sense? Does it raise any eyebrows on the results?

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Clean Code for a Data Scientist

CLEAN CODE FOR A DATA SCIENTIST (?)

"I'm a Data Scientist. I don't need to write clean code because most of my code is throwaway anyway." This is a common claim, but collaboration changes everything. When you work in a team, readability and reproducibility are non-negotiable.

As a scientist your experiments must be reproducible and verifiable. That means others on your team should be able to understand and reproduce your results.

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