NOTES

This dataset comes from Divvy Bikes for learning purpose only, to know more about license, go to license section below. The company is "a fictional company" and does not relate to any commercial companies.

- About Company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.

- Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

- Your Task

> How do annual members and casual riders use Cyclistic bikes differently?

- Dataset information

1. License

See Data License Agreement for more information

2. Column Descriptors

ride_id: trip id

rideable_type: type of bike (classic, docked and electrical)

started_at: Trip start day and time

ended_at: Trip end day and time

start_station_name, start_station_id: Trip start station with its id

end_station_name, end_station_id: Trip end station

start_lat, start_lng: Latitude and Longitude of trip start station

end_lat, end_lng: Latitude and Longitude of trip end station

member_casual: Rider type (casual and member, more information see About Company)