michael90266

Coursera Google Data Analytics Capstone Project

Project Overview

This project is my capstone case study for the Google Data Analytics Professional Certificate.
I analyzed historical bike-share data from Cyclistic, a fictional Chicago-based bike-share program, to answer the key business question:

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

The insights inform a marketing strategy aimed at converting casual riders into annual members.

Files in This Repository

08_22_2025_coursera_r_markdown.Rmd - R Markdown source file containing the full analysis
08_22_2025_coursera_r_markdown.html - Rendered HTML report from the R Markdown file
Case Study 1_How does a bike-share navigate speedy success.pdf - case study instructions PDF

These files are uploaded to the main branch.

Divvy_Trips_2019_Q1.zip - zip containing CSV file containing rideshare data from Q1 2019
Divvy_Trips_2020_Q1.zip - zip containing CSV file containing rideshare data from Q1 2020

These files are uploaded to the source branch.

Data Sources

Cyclistic trip data (fictionalized Divvy dataset provided for this case study):

Divvy Tripdata
Data made available by Motivate International Inc. under this license

Tools and Skills Used

R / RStudio: data cleaning, wrangling, analysis, and visualization
tidyverse packages: dplyr, ggplot2, lubridate
Data storytelling framework: Ask → Prepare → Process → Analyze → Share → Act

Key Findings

Subscribers tend to take shorter trips on average compared to Customer trips, while there is less variation in Subscriber trip duration, suggesting more uniformity (commuting behavior).
Subscribers ride heavily during commute hours, while Customers ride more during weekends and leisure times.
Subscribers primarily rent and return bikes from stations near major transit hubs, reflecting commuting patterns, while customers often rent near leisure destinations such as parks and restaurants, and return bikes near parks and central transit hubs.