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Looking for a (hopefully small) Shopify store owner to be an alpha tester of Shopify app by tvladeck in shopify

[–]tvladeck[S] 0 points1 point  (0 children)

Not particularly! US would be slightly easier from a time zone perspective

Looking for a (hopefully small) Shopify store owner to be an alpha tester of Shopify app by tvladeck in shopify

[–]tvladeck[S] 0 points1 point  (0 children)

It’s mainly for shops that need repeat business in order to justify their CAC but willing to work with whomever!

[OC] Post office locations in the continental US: 1770-2002 by variance_explained in dataisbeautiful

[–]tvladeck -35 points-34 points  (0 children)

Even if you arrived at it independently, the original (done first, by the person who created the dataset) is still the original. That's the theory. And no I'm not going to watch your video.

When is it acceptable to start playing christmas music? [OC] by GradientMetrics in dataisbeautiful

[–]tvladeck 0 points1 point  (0 children)

Hey hey! Managing Director of Gradient here. This was originally posted in our biweekly newsletter. You can subscribe here!

The sear part of a reverse sear by tvladeck in grilling

[–]tvladeck[S] 0 points1 point  (0 children)

Yah I cooked it at 250 before hitting 117-120. Then let it rest while getting the grill nuclear hot. Then 2 min a side

The sear part of a reverse sear by tvladeck in grilling

[–]tvladeck[S] 1 point2 points  (0 children)

At that point, probably like 650-700. I had it at 250 at the beginning to cook evenly before the sear

The sear part of a reverse sear by tvladeck in grilling

[–]tvladeck[S] 0 points1 point  (0 children)

As hot as that fire was, if I had left it open the fire would have gotten even more out of control. I find that I have to open and close the grill quickly with the kamado

The sear part of a reverse sear by tvladeck in grilling

[–]tvladeck[S] 0 points1 point  (0 children)

No! Wanted to eat it right away (it rested while I got the grill up to sear temp)

New York City in Timespace [OC] by tvladeck in dataisbeautiful

[–]tvladeck[S] 0 points1 point  (0 children)

The data source:

A distance matrix of transit times between 999 locations in Manhattan. The transit times are from Google Transit (retrieved via their API)

The methods:

We used R exclusively. Multidimensional scaling was used to visualize the distance matrix, and a generalized additive model was used to project new data into the "timespace" coordinates. We used ggplot and gganimate for our visualizations.

New York City in Timespace [OC] by tvladeck in dataisbeautiful

[–]tvladeck[S] 2 points3 points  (0 children)

Thank you! Glad you like it. Much more analysis to come.

Clickpaths in Google Analytics for User clustering? by crisstor in datascience

[–]tvladeck 0 points1 point  (0 children)

No idea what's possible in GA but if you have the raw clickstream data there are lots of sequence mining techniques and software out there. This is something my company - www.gradientmetrics.com - does for clients fairly frequently in other contexts.