This data analysis project is in response to the Google Data Analytics Capstone Project.

The Capstone project offers three paths, either take one of the two case studies offered and perform data analysis as per requirement, or commit your own project on data you gathered. 

Since I wanted this project to be a benchmark for my skills as a data analyst, I decided to select the first of the two prepared case studies.

Cyclistic Case Study: How Does a Bike-Share Navigate Speedy Success?

The case study is based on a fictional bike sharing company, and my role is that of a junior data analyst in the marketing department of that company. I report to the director of marketing, and she has assigned me a task, which will in essence be the definition of my data analysis  project.

The dataset is actually that of Divvy, a real-life Chicago based bike sharing program by the Chicago DoT, and run by a private company (Lyft) on their behalf.

This distinction is important because the data management policies of Divvy will affect the data quality of the project, so it will be crucial that we refer back to it when conducting our own data analysis.

The requirement is to analyse a year's worth of data, so in this particular case, the latest data available is from May 2022 to April 2023.

Each of the case study paths has an accompanying docket, called the Case Study Roadmap, that defines the case study requirements and the steps to take for a successful case study. 

The project will be following the data analysis workflow taught in the Google Data analytics course, which are: ask, prepare, process, analyse, share, and act. These six steps will be the guidestones for the project. 

In addition, the project will be dependent on the knowledge taught in the course on some of the following well known tools: Microsoft Excel, Google BigQuery SQL, R Language, and Tableau visualisation software.

1. ASK :

2. PREPARE

3. PROCESS

4. ANALYSE

5. SHARE

6. ACT

Please click the link above (or on the sidebar) to check the details on each stage.

Members have a very different ridership profile than Casual riders; we can use the commonalities and distinctions to create a marketing campaign that  will be more effective in converting casual riders into members.