AirBnB grows by sharing data scientist knowledge

November 10, 2016
By

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

This animation of AirBnB host locations from 2011-2014, presented by Ricardo Bion (data scientist manager at AirBnb) at the EARL Boston conference earlier this week, shows the dramatic growth in properties to rent through the service along with the most common routes of travellers. (You can find the R code that created this animation here.)

Popular_routes

How did AirBnb achieve such rapid growth? According to Ricardo, it’s by being a “data-informed company”. One of the first eight hires at AirBnb was a data scientist. And as we’ve noted here before, R is widely used at AirBnb along with other data science tools including Python and Jupyter. In fact, most data scientists there are proficient in more than one tool.

Another driver of this growth is a commitment to knowledge sharing, focusing on the tenets of reproducible research, quality, consumability, discovery and learning. Low-quality repositories lead to teams that only read and trust research they have created themselves. To address this, AirBnb created an internal knowledge repostory where data scientists can share “knowledge posts” (research reports created as Jupyter Notebooks, R Markdown notebooks, or Markdown files with all associated code and data for reproducibility). Peer review is accomplished using Github’s issue and pull request workflows, and posts are tagged for easy discoverabilty. In this way, AirBnb seeks to create a central repository of high-quality research trusted by everyone at BnB. 

Airbnb-report

AirBnb recently released this Knowledge Repository as an open-source project on Github. For more background on the Knowledge Repository, follow the link below.

Medium: Scaling Knowledge at Airbnb

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)