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Data Analysis for Cyclistic 

 

A Google Coursera Data Analytics Capstone Project

Adam Hauptfeld, PhD

  1. The Business Task 

  2. Data Source

  3. Cleaning and Manipulation

  4. Analysis

  5. Key Findings and Recommendations

  6. Lessons Learned

01

The Business Task

The bike-share company Cyclistic wants to use the data they’ve collected from their users to maximize the number of annual memberships they sell. I've been tasked with analyzing their data to gain insights on how casual riders are similar to and different from their annual members. The marketing team will then use these insights to build a marketing strategy to convert  the casual riders into members.

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This page is a presentation of my data analysis from start to finish. It begins with a description of the data source and the data cleaning process. Then it details the analyses I performed using SQL, and it concludes with my key findings and top three recommendations, supported by visualizations created in Tableau.

02

Data Source

Although Cyclistic is a fictional company, the data provided for this project comes from Chicago’s Divvy bike-share service, operated by Lyft. The license for the data allows for the royalty-free right to “access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose.” It can be viewed here.

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​The original data is saved in CSV documents and can be downloaded here. I used the four quarterly 2019 datasets, and downloaded the data on May 18, 2024.

Data Description
The data is stored in a table with 12 columns and a total of 1,048,576 rows. Each individual row represents a single trip by a single user on a Cyclistic bike. For each trip, the starting datetime and ending datetime, starting station ("from station") and ending station ("to station"), trip duration in seconds, and user type are all recorded. For most trips, the user's gender and year of birth are also recorded. Each row also has a trip ID, which serves as the table's primary key. 
The user type column is especially important for my analysis. There are two user types: annual members and casual riders (called "Subscribers" and "Customers" in the dataset). Annual members pay a yearly subscription for unlimited trips on Cyclistic's bikes, while causal riders rent bikes on a trip by trip basis. My business task is to find how the other variables differ by user type.
Privacy Statement

The users in the dataset are anonymous and the source data does not include information sufficient to identify individuals. The only personal information included is gender and their year of birth. The license prohibits the attempt to correlate the data "with names, addresses, or other information of customers or Members of Bikeshare."

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