This project is the capstone assignment for the Google Data Analytics Professional Certificate program. The program prepares participants for a career in data analytics with training focused on key analytical skills (data cleaning, analysis, and visualization) and tools (Excel, SQL, R Programming, Tableau).
This project will analyze publicly available datasets, provided by the course, for a bikeshare program based in Chicago.
Cyclistic is a successful bike-share program launched in 2016. It has since grown to a fleet of 5,824 bicycles that are geo-tracked 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.
Cyclistic offers a variety of pricing plans including: single-ride passes, full-day passes, and annual memberships. Customers who purchase a single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.
For this case study, our guiding question is:
How do annual members and casual riders use Cyclistic bikes differently?
The following analysis makes use of the following tools and techniques:
- R programming language and libraries;
ggplot2,tibble,tidyr,readr,purrr,dplyr,stringr,lubridateandforcats - Data transformations: joins, visualizations, summary statistics
- Data inspection: removal of duplicate/unnessary data, change format/datatype, verify unique values
The analysis yielded some key differences between member and casual riders of the Cyclistic bike share service. Most notably were the peak times and durations when casual riders use the service versus when member riders use the service. This led to the following recommendations outlined below.
We know that casual riders take rides of longer duration during the weekends. To encourage casual riders to become members, Cyclistic could partner with special events that occur on weekends. For example, they could partner with concert venues or sporting events.
Casual riders have their peak ride times during the months of April, May, and June. To encourage them to continue using the bike share service, special promotions for membership could be offer outside of peak times in effort to encourage them to continue using the service. This could also be applied during the work-week (M-F) when casual riders are less-likely to use the service.
Lastly, Cyclistic could partner with restaurants and recreational facilities near the Top 10 Start/End locations. As an example a discounted bike share membership could be offered when they purchase an item at a local business near these locations.
The case study can be viewed at the following places: