In this dataset, users will be able to track credit card rankings. The filters that are available are APR, annual fee, credit category, card type, and bank issuer.

Here is the link for the documentation on the columns for this dataset:
Columns Definition

Users will be able to track companies amongst many industries on who are and are not writing code.

Here is the link for the documentation on the columns for this dataset: Columns Definition

View key metrics for company Sina Weibo handles. Metrics include followers, posts, and description. Users can track a company’s overall traction on Sina Weibo, a Chinese microblogging site over time. By collecting this data, we are able to determine the popularity of a company.

Here is the link for the documentation on the columns for this dataset: Columns Definition

Track the prices of individual properties by its location. Users will be able to filter the property to specific requirements such as the number of bedrooms, bathrooms, and size of the property.

Number of rental properties that were listed by Camden Property from 4/1/17 - 4/1/18:

States with the most expensive 2 bed and 2 bath apartment rental prices this past year:

Since locations are standardized across companies, we are able to load multiple properties at once as well as in one geographic area.

The average rental price of a one bedroom apt in Austin, Texas

View the price of products that are being offered by retailers across different geographic locations. Users will be able to track the current and list price as well as its purchase period. The right prices need to be low enough to be competitive while still being high enough to protect a company’s profit margins. This dataset is important to investors because the overall number of products give investors an insight on a company’s manufacturing plan, while the amount of promotions gives them an insight into the margins and revenue of the company.

Here is an example where we