DivvyBikes

This hands-on project aims to explore the data set for the Chicago Divvy Bikes. This is a capstone project for the Google Data Analytics Certificate. I am working as a fictional company focused on answering key business questions, following the steps of the data analysis process: ask, prepare, process, analyse, share, and act.

Data Analytics Methodology

Transforming data into insights: the six steps of data analytics include: ask, prepare, process, analyze, share, and act.

1. Ask: effective questions and collaborate with leaders and managers who are interested in the outcome
2. Prepare: It all start wth a solid preparation. During this step, the analysts identify what data they need to achieve the succesful result they identified in the previous step.
3. Process: In this step, the data analyst make sure how data would be collected, stored, managed, and protected.
4. Analyse: This is the moment where the data analyst dive into the data, combine data from multiple sources, do the calculations and data validations.
5. Share: Share the results with stakeholders involved in the project.
6. Act: The last stage of the process where the team of analysts work with stakeholders within their company and decide how best to implement sugegstions and actions based on the fidings.

Tasks in this project

1. Understand the problem statement
2. Import libraries and dataset
3. Process the data
4. Perform exploratory data analysis

Data Source

For this project we will be using the Data source available at kaggle.

Code Source

To develop the analysis, I used the Kaggle notebook available on GitHub. Click here to access the file and see the results.

Conclusions about the EDA

In this hands-on project, my objective was to go through the steps of the data analysis process: ask, prepare, process, analyse, share, and act.

The data I analyzed consisted with unique trips starting January, 2022 to December, 2022, by two types of customers: casual riders (who purchase single-ride or full-day passes), and members (who purchase annual memberships). The objective of the EDA was to understand and determine the usage patterns by customers, trying to answer the question: how do annual members and casual riders use Divvy bikes differently during the year?

After preparing and processing the data, I was able to work with 4.1 mio trips, and build analysis such as preferred rideable type, proportion of rides by type of user, the length and distance by each type of customer, usage patterns by day, month and throughout the year.

Finally, I had the chance to plot and see the map of Chicago by counting the preferred starting stations by latitude and longitude, and I did a graphical representation providing an intuitive and spatial understanding of the bike network, highlighting key stations based on activity and the connectivity among different locations. The visual nature of this graph makes it easier to identify patterns, such as popular stations or commonly used routes, within the bike-sharing network.

Enjoy the content!