Data Science

Purchasing a car is a significant time and financial commitment. There is so much at stake that the required song and dance with the sales manager don’t alleviate any fears about over paying. Thus, it is difficult to determine the equilibirum point at which the dealer will accept your offer versus how much you are willing to pay. I decided to write this for a few reasons: I am in the market for a new car Rather than doing actual car shopping I thought it would be more fun to procrastinate Online calculators are quite clunky when you want to compare and contrast monthly payments Hopefully, this will help others make more informed car buying decisions (TBD on Shiny app) Note: this guide will focus only on leasing and not the auto financing aspect of it...

I think my tweet speaks for itself: Words can not express how excited I am to use this :D — Christopher Yee (@Eeysirhc) March 10, 2020 The goal of this article is to document how to send #rstats code and plots directly to Slack. Load packages library(slackr) library(slackteams) library(slackreprex) Slack credentials Member ID You can easily grab that from this guide here. Slack Key ID To retrieve your Slack key ID, login here and then follow the prompts....

I like to browse company career pages once in awhile to see what positions they have open. In my opinion, this provides a glimpse into what they are investing in for the next few years. Hopper is one company which stands out but the reason I am writing this is a puzzle they included in the job description: At Hopper, every dataset tells a story. Do you have what it takes to decipher the clues?...

My friend John-Henry Scherck recently tweeted his process on how to refresh stale content: Put together a quick video on how to refresh stale content using nothing more than Google Search Console and a word doc. Check out the full video here: https://t.co/Vva4Zm4mNn pic.twitter.com/74Fm2oIz4c — John-Henry Scherck (@JHTScherck) January 21, 2020 I imagine this can be broken down into five distinct parts: Stale content selection Understanding keyword intent Actually refreshing the content Internal link optimization Publish This short guide will focus on the first aspect where we’ll use #rstats to remove the manual work associated with stale candidate selection....

A couple weeks ago, a friend asked me about my base annual salary during my time as Square’s SEO Lead. Rather than spitting out a number, I thought it would be more interesting to see if we could answer her question using #rstats. tl;dr This is what I posted on Twitter: Ok #bayesian twitter: helping a friend with salary negotiations and this incorporates what she wants, job boards, confirmed salaries, etc……how do I validate if this model is a load of crock or not?...

In my previous article about simulating page speed data, I broke one of the cardinal rules in programming: don’t repeat yourself. There was a reason for this: I wanted to show what is going on under the hood and the theoretical concepts associated with them before using other functions in R. For this follow-up, I’ll highlight a few #rstats shortcuts that will make your life easier when generating and exploring simulated data....

We may be inundated with data but sometimes collecting it can be a challenge in and of itself. A few reasons off the top of my head: Sparsity Difficult to measure Impractical to devote company resources to it Lack of technical expertise to actually build or acquire it Lazy (yours truly - except for that one time) Through simulation we can generate our own dataset with the added benefit of fully understanding what features we choose to put in our models (or leave out)....