There are a ton of places to find data related to business online. Here are some examples:

• Kaggle: Kaggle hosts machine learning competitions. A byproduct of these competitions is a host of fascinating datasets that are generally free and open to the public. The datasets are sometimes accompanied with suggestions for how to analyze them. Here are a few examples:

  • Chase Bank Branch Deposits, 2010–2016: Records for every branch of Chase Bank in the United States. This dataset is not quite tidy and will require a little bit of reshaping with gather() or pivot_longer(), since there are individual columns of deposits per year.
  • Credit Card Approvals: a credit card dataset for prediction methods.
  • Credit Card Fraud Detection: anonymous credit card transactions labeled as fraudulent or genuine.
  • 515K Hotel Reviews Data in Europe: 515,000 customer reviews and scoring of 1,493 luxury hotels across Europe.
  • Netflix Movies and TV Shows
  • IBM HR Analytics Employee Attrition & Performance: simulated data created by IBM data scientists to model employee attrtition.
  • And many, many more!

• Quandl: Nasdaq Data Link provides some datasets related to finance and economics for free (though many require a paid subscription). The datasets are listed in this data catalog.

• FRED: Economic Data from the St. Louis Fed.

• Yahoo Finance

  • There are many ways to access data from Yahoo Finance. One option is to scrape data. Another is to use a package like tidyquant to tq_get() quantative data in a tibble format. This package can also be used to get data from FRED, Quandl, and other sources.

• Twitter: You could collect data on tweets that reference a specific company via the twitter API or by using a package like twitteR to access the API. Then you could use this on its own or in conjunction with some other data (e.g., stock prices/returns) to do an analysis of consumer sentiment, for example.

• Inside Airbnb: Inside Airbnb is a mission-driven activist project with the objective to provide data that quantifies the impact of short-term rentals on housing and residential communities, as well as create a platform to support advocacy for policies to protect our cities from the impacts of short-term rentals. The post data for many different cities. Please note these community guidelines:

  • Only take the data you need
  • Do not scrape data from the site
  • Only download the data once. Do not write scripts that download the data every time they are executed.

• Yelp Open Dataset: A subset of Yelp businesses, reviews, and user data for use in personal, educational, and academic purposes.

  • Note: the data are stored in JSON files and will require special steps to import into data structures we are familiar with (e.g., by using the package jsonlite). Feel free to come to office hours for help.

• Taxi and Ridehailing Usage in New York City

• Zillow Housing Data

• Amazon Review Data

  • Note: the data are stored in JSON files and will require special steps to import into data structures we are familiar with (e.g., by using the package jsonlite). Feel free to come to office hours for help.
  • The same group provides a collection of dataset on many other platforms and topics here.

• Bank Marketing Data: data on direct marketing campaigns and whether they succeeded in convincing customers to make a deposit.

• Data from Intro to Statistical Learning: The ISLR package contains several interesting (if dated and or/simulated) datasets:

  • ISLR::Caravan contains 5,822 real records on whether customers purchased a caravan insurance policy, along with product ownsership information and sociodemographic data based on their zip codes.
  • ISLR::Default is a simulated dataset of 10,000 credit card customers that can be used to predict which customers will default on their credit card debt.
  • ISLR::OJ contains 1,070 purchases of orange juice for two brands, along with customer and product characteristics (like prices) that could be used to study pricing strategies.

• Data is Plural Newsletter: Jeremy Singer-Vine sends a weekly newsletter of the most interesting public datasets he’s found. He also has an archive of all the datasets he’s highlighted.

• Google Dataset Search: Google indexes thousands of public datasets; search for them here.