News

Beyond Single-Pass Analytics – What Changes With Scale

This article is by Sean Adams and originally appeared on the Alteryx Engine Works Blog here: https://community.alteryx.com/t5/Engine-Works-Blog/Beyond-Single-Pass-Analytics-What-Changes-With-Scale/ba-p/513519

 

When we begin with analytics, either as a new practice within your firm, or starting a team in a new area–much of your work may initially be what I call "single-pass analytics," where the data goes from source, to prep, straight to the end-point.

Enriching Tax Data with Location Information

This article is by Robert Lenius and originally appeared on the Alteryx Engine Works Blog here: https://community.alteryx.com/t5/Engine-Works-Blog/Enriching-Tax-Data-with-Location-Information/ba-p/509848

 

Working in finance, I’m somewhat bummed that I don’t often get to explore the possibilities within location-based data.

Expand Your Predictive Palette III.I: Sales Forecast with Prophet Tool

This article is by Timothy Lam and originally appeared on the Alteryx Data Science Blog here: https://community.alteryx.com/t5/Data-Science-Blog/Expand-Your-Predictive-Palette-III-I-Sales-Forecast-with-Prophet/ba-p/504651

 

At a high-level, forecasting techniques can be broken down into three main categories:

Historical Average with Sliding Windows

Examples: Seasonal Decomposition, Exponential Smoothing (ETS)
Pros: Simplicity: any tool can integrate like Excel & Tableau;
Cons: Laggardly reaction to changes & overly responsive with outliers, only works with simple structure

Linear Models

Examples: ARIMA, VAR(Good for multiple time series)
Pros: Works with consistent variation, such as established seasonality trends
Cons: Proper assumption on stationarity and homoscedasticity (statistics term for consistent variance/error). Careful predictor selection to prevent multicollinearity (statistics term when predictors are linearly correlated).

Non-Linear Models

Examples: Prophet, GARCH (generalized autoregressive conditional heteroskedasticity), Deep Learning
Pros: Discovering non-linear relationship & data drift in your data.

Analytics as an Equalizer: Creating a “We Culture”

This article is by Olivia Duane Adams and originally appeared on the Alteryx Analytics Blog here: https://community.alteryx.com/t5/Analytics-Blog/Analytics-as-an-Equalizer-Creating-a-We-Culture/ba-p/471071

 

Everything starts with a story. There was a time, not too long ago, when it was incredibly rare to see women pursue careers in science and technology and while we still see women underrepresented in these areas, women are taking a stronger foothold in mathematics and analytics.

Alteryx 2019.4 is now available!

This article is by Christopher Russell and originally appeared on the Alteryx Analytics Blog here: https://community.alteryx.com/t5/Analytics-Blog/2019-4-You-Vision/ba-p/491388

See 20/20 with our 2019.4 release as we take off into the new year and begin solving your biggest business questions with better vision.

Innovating in Data Science and Machine Learning: Modeling Better Together

This article is by Alan Jacobson and originally appeared on the Alteryx Analytics Blog here: https://community.alteryx.com/t5/Analytics-Blog/Innovating-in-Data-Science-and-Machine-Learning-Modeling-Better/ba-p/472421

 
What is Feature Engineering?
First, before we go too far into all the reasons why we are excited, let’s talk a bit about what Feature Engineering is all about.