Case Study

Author

Scott Sesher

Published

June 7, 2025

🚧 This case study is currently under construction.
Please check back soon for a complete walk-through, analysis, and visualizations.

🚲 How Does a Bike-Share Navigate Speedy Success?

This project began as the capstone case study for the Google Data Analytics Certificate, which presents a fictional company named Cyclistic. The business goal:
Determine how casual riders and annual members use Cyclistic bikes differently β€” and identify strategies to convert more casual users into annual members.

To bring this scenario into the real world, I extended the analysis using actual Divvy bike-share data from the City of Chicago, covering rides from 2013 through early 2025. I also incorporated hourly weather data and geospatial analysis to enhance the depth and utility of the findings.

πŸ“š Project Context

This project satisfies the requirements of the Google Data Analytics capstone while also serving as a portfolio piece demonstrating:

  • Independent sourcing and integration of multi-year real-world data
  • Use of tools beyond the course scope (e.g., SQLite, Leaflet, Tableau, shell scripting)
  • Realistic data cleaning, transformation, and exploratory analysis

The fictional business challenge of Cyclistic is explored through the lens of actual Divvy usage patterns.

🎯 Business Task

How do casual riders and annual members use Divvy bikes differently, and how can that inform marketing strategies?

πŸ“¦ Data Sources

The analysis is based on the following datasets:

  • Divvy Bike Trip Data (2013–2025)1 : This dataset includes anonymized ride-level data from Chicago’s Divvy bike sharing system. The bulk of the data (June 2013 through Sep 2019) (Divvy_Trips_20250501.csv) was obtained from the City of Chicago Data Portal. The rest (Oct 2019 through Jan 23 2020 and Jun 2023 – April 30 225) was downloaded from Divvy’s S3 archive . Please note, rides from the time period of the COVID-19 Pandemic are not included.
  • Chicago Weather Data (Hourly): Hourly weather data for Midway International Airport (station ID: 72534) was downloaded from the Metostat bulk archive. The dataset includes temperature, wind speed, precipitation, and other key weather indicators for correlation with bike usage patterns.
  • Tourist Attractions Dataset (Custom): A manually curated dataset of key tourist attractions in Chicago (e.g., Navy Pier, Millennium Park, Willis Tower) was created to identify rides likely associated with tourism. Latitude and longitude coordinates were obtained using Google Maps for educational use and spatial filtering.

πŸ”— Attribution

  • City of Chicago. (2025). Divvy Trips Dataset. https://data.cityofchicago.org/Transportation/Divvy-Trips/fg6s-gzvg/about_data
  • Lyft. (2025). Divvy Trip Data Archive. https://divvy-tripdata.s3.amazonaws.com/index.html
  • Meteostat (https://meteostat.net). (2025). Hourly Weather Data – Midway Airport (72534). https://bulk.meteostat.net/v2/hourly/72534.csv.gz Provided under the terms of the Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)
  • Tourist location coordinates obtained via Google Maps (maps.google.com) for educational and analytical purposes.

πŸ” Key Metrics and Analysis

πŸ“Š Summary of Findings

πŸ’‘ Implications and Recommendations

⏩ Next Steps

Footnotes

  1. Divvy Bikes ride data from June 13th 2013 to April 30th 2025 with the exception of the COVID-19 Pandemic, as that date is not representative of normal bike usage.β†©οΈŽ