Hidden Trends in AirBnB Reviews?

Scrape data off any website, uncover hidden trends in Airbnb reviews, deep learning for beer labels, and more.  ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌  [DataCamp](  ISSUE #40 — MARCH 27, 2018  DataCamp Weekly Hi there - this is issue #40 of our newsletter! This week, learn how to scrape data off any website, uncover hidden trends in Airbnb reviews, use deep learning to identify beer labels and more. Enjoy! [Share An Article](  Community Top Posts  [1]( [Scrape-It-Yourself: Spotify Charts](  WEBSCRAPING  | 12 Upvotes   | 0 Comments   | Posted by jakedaniels  Learn to scrape data off any website with rvest, purr and SelectorGadget.  [2]( [Uncovering Hidden Trends in AirBnB Reviews](  NLP  | 11 Upvotes   | 0 Comments   | Posted by sakshigupta88  Foundation steps for a sentiment analysis project through a case study on AirBnB reviews.  [3]( [K-Means Clustering in R Tutorial](  MACHINE LEARNING  | 11 Upvotes   | 0 Comments   | Posted by cjsejal  Learn all about clustering and, more specifically, k-means in this R Tutorial, where you'll focus on a case study with Uber data.  [4]( [Text Mining & Natural Language Processing in Data Science (with Julia Silge)](  PODCAST  | 11 Upvotes   | 0 Comments   | Posted by hugobowne  How can data science be leveraged to inform business decisions around all the text data that businesses have and what can text mining all the open text data out there tell us about society?  [5]( [First Step to Become a Data Scientist](  CAREER  | 9 Upvotes   | 2 Comments   | Posted by rizwankhn2003  Data scientists must keep refreshing their knowledge to stay up to date. This post is a helpful introduction to the field of data science.  [6]( [BiBirra: Beer Label Recognition](  DEEP LEARNING  | 9 Upvotes   | 1 Comment   | Posted by theov  Using the wine recognition app (ViVino) as inspiration, this data scientist created a version for beer that can recognizes multiple bottles at once. A working version of this project is available for you to try it out yourself.  [7]( ["Kindof" Big Data in R](  R PROGRAMMING  | 7 Upvotes   | 0 Comments   | Posted by steve-miller  This article is an illustration of using a functional programming style plus powerful package capabilities to combine in-memory and file processing to handle “large” datasets in R. Using data.table, tidyverse & fst package.  [8]( [Exploring the Underlying Theory of the Chi-Square Test Through Simulation](  STATISTICS  | 6 Upvotes   | 0 Comments   | Posted by keith-goldfeld  Through various simulations, this article sheds some light behind the theory that underlies the chi-square test.  [9]( [R's S3 Generic-Function Object-Oriented System](  R PROGRAMMING  | 6 Upvotes   | 0 Comments   | Posted by jasdumas  Learn more about R's S3 generic-function object-oriented system with an example using cookie recipes.  [10]( [Rocker: R and Docker](  CLOUD  | 5 Upvotes   | 0 Comments   | Posted by davidsmi  If you regularly have to deal with specific versions of R, or different package combinations, it can be a pain. A good solution to this is using Docker containers, which were expressly designed to make it quick easy to configure and launch an independent and secure collection of software and services.   [DataCamp Community]( BY THE COMMUNITY, FOR THE COMMUNITY [Discover](  Popularity by package   Shiny app streaming package download rates visualized as a bubble chart. Learn how to build your own dashboard with our course [Building Dashboards with shinydashboard](.   That's all for now. Have a great week!   [DataCamp] [DataCamp](  DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118  [Facebook] [Facebook]( [Twitter] [Twitter]( [LinkedIn] [LinkedIn]( [YouTube] [YouTube](  [Unsubscribe]( Â