SQL Saturday Attendee Distances

I have a long, long post on figuring out how far SQL Saturday attendees tend to drive:

Before I begin, allow me to perform the data science Airing of Grievances.  This is an important part of data analysis which most people gloss over, instead jumping right into the “clean up the dirty data” phase.  But no, let’s revel in its filth for just a few moments.

Despite my protestations and complaints, I think there are some reasonable conclusions.  If you need to look like you’re working for a couple of hours (or at least want to play around a bit with SQL and R), this is the post for you.

Related Posts

Tidy Anomaly Detection With Anomalize

Abdul Majed Raja walks us through an example using the anomalize package: One of the important things to do with Time Series data before starting with Time Series forecasting or Modelling is Time Series Decomposition where the Time series data is decomposed into Seasonal, Trend and remainder components. anomalize has got a function time_decompose() to perform the same. […]

Read More

Uploading Data Sets To Azure ML From R

Leila Etaati continues her series on the Azure ML R package by showing how to upload a data set: There is a function in AzureML package name “workspace” that creates a reference to an AzureML Studio workspace by getting the authentication token and workspace id as below: 1 ws <– workspace( id , auth  ) to […]

Read More

Categories

July 2016
MTWTFSS
« Jun Aug »
 123
45678910
11121314151617
18192021222324
25262728293031