Jerry Overton15
What software engineering practices do we need in #DataScience?
Theyaa Matti
The current agile software development practices contain, standard naming, commenting, associated tests.
Kyle Zellman
Clear variable naming, looping/control flow, readability/reproducibility
Chris Fangmann
@khzellman Any ideas on standards / best practices?
Henry Helgen
@TheyaaMatti coding standards, naming conventions, test planning are essential software quality steps with any approach.
Kyle Zellman
@ChrisFangmann when it comes to naming--clear, descriptive names that allow others to understand what the code is doing. Readability/reproducibility--robust comments, using spacing and indenting to your advantage.
Fabien Gelineau
ability to mix gracefully several programming paradigms: procedural, object orientation and functional programming tied all together
Jerry Overton
You'd have to know some really flexible architecture patterns for that, right? Any suggestions?
Soren Helsted
Does anybody have examples on good patterns and anti-patterns, and are patterns important in #DataScience programming?
Chris Baker
Test-driven development is important in any context, no less so for data science. Understanding of how an algorithm works doesn't help if you've coded it incorrectly.
Jerry Overton
@ScaredOfGeese But TDD is fairly new in #DataScience right? How many us have experience with unit testing in Python or R?
Henry Helgen
Commenting for re-usability. Header comment with purpose, change log, inputs, outputs, exceptions, how to call. Each block comments that describe the "Why did you write it that way?"
Chris Baker
These assumptions should be explicitly stated and verified using tests that document and demonstrate the proper functionality of constituent components.
Sorin Costea
let's not forget that TDD is no silver bullet either
Jerry Overton
What about the practice of writing comments first. Too simple?
Soren Helsted
Like when we did pseudo code in comments before adding the real code?