Using the HTML GUI SDK as a Coding NOOB

This article is by Ian Wi and originally appeared on the Alteryx Engine Works Blog here: https://community.alteryx.com/t5/Engine-Works-Blog/We-All-Start-Somewhere/ba-p/371822

Making Things Pretty
The role of the HTML GUI SDK is, to me at least, simple: it allows users to create a more customizable macro interface.

Introducing SnakePlane: a Better Way to Work with the Python SDK

This article is by Sydney F and originally appeared on the Alteryx Engine Works Blog here: https://community.alteryx.com/t5/Engine-Works-Blog/Introducing-SnakePlane-a-Better-Way-to-Work-with-the-Python-SDK/ba-p/392872

 
What is SnakePlane?
SnakePlane is an abstraction layer that simplifies using the Python SDK.

If you’ve ever worked with the Python SDK, you know that the experience can be… confusing.

Alteryx Community Version 19.3

This article is by Alex Koszycki  and originally appeared on the Alteryx Nation Blog here: https://community.alteryx.com/t5/Alter-Nation-Blog/Alteryx-Community-Version-19-3/ba-p/404700

 
Customer Support Improvements
We're happy to provide a few new features and enhancements to the Customer Support Case portal, including a more streamlined case submission process and a sharp new look for your open cases.

We All Start Somewhere

This article is by Ian Wi and originally appeared on the Alteryx Engine Works Blog here: https://community.alteryx.com/t5/Engine-Works-Blog/We-All-Start-Somewhere/ba-p/371822

 

Many users try Designer and never look back. In fact, many users try Designer, never look back, and go around telling folks they know about their experiences with Designer.

Bias Versus Variance

This article is by Sydney Firmin and originally appeared on the Alteryx Data Science Blog here: https://community.alteryx.com/t5/Data-Science-Blog/Bias-Versus-Variance/ba-p/351862

 

There are two types of model errors when making an estimate; bias and variance.

All Models Are Wrong

This article is by Sydney Firmin and originally appeared on the Alteryx Data Science Blog here: https://community.alteryx.com/t5/Data-Science-Blog/All-Models-Are-Wrong/ba-p/348080

All Models Are Wrong
The three broad categories of assumptions made by statistical models are distributional assumptions (assumptions about the distribution of values in a variable or the distribution of observational errors), structural assumptions (assumptions about the functional relationship between variables), and cross-variation assumptions (joint probability distribution).

For example, a linear regression model assumes that the relationships between variables in a data set are linear (and only linear). In the eyes of a linear model, any distance between the observations that make up the data set and the modeled line is just noise (i.e., random or unexplained fluctuations in the data) and can ultimately be ignored.