Cyclistic Bike Share: Analyzing Rider Behavior - Capstone Project

Overview

Welcome to my capstone project repository for the Google Data Analytics Professional Certificate! This project showcases my ability to apply data analytics techniques, using a combination of SQL, spreadsheets, and R programming, to derive insights from real-world data. The project was originally created as a notebook on Kaggle and then adapted to GitHub, where I present the analysis, visualizations, and actionable insights.

Project Description

This project analyzes bike-sharing data from Cyclistic, a fictional bike-share company, to understand rider behavior and identify trends. The key focus is on the use of SQL for data analysis, spreadsheets for data cleaning and transformation, and R for visualizing the results.

The analysis involves:

• Data Cleaning & Processing: Using SQL and spreadsheets for data cleaning and preparation.
• Analysis: SQL queries for exploring trends, patterns, and insights within the data.
• Visualizations: Using R to generate meaningful visualizations to communicate the analysis results effectively.

What’s Included:

• R Markdown File: This file outlines my analytical approach, integrating SQL query results and processed spreadsheet data, and includes R-coded visualizations.
• Data Files: SQL query results and processed data from spreadsheets used for visualizations.
• Knitted HTML & PDF: Two formats of the full final report, as originally formatted on Kaggle, with code, explanations, and visualizations.

Project Deliverables

• R Markdown File: Provides a detailed walkthrough of my analysis, integrating SQL results and spreadsheet data with R-coded visualizations.
• Data Files: Contains the raw SQL query results and processed data files from spreadsheets.
• Final Reports (HTML & PDF): Reflects the full final analysis, as it was originally presented on Kaggle, with all code, visualizations, and explanations.

Key Findings & Recommendations

At the end of the project, I provided actionable insights for stakeholders:

• Targeted Recommendations: Based on stakeholder questions, I provided recommendations to help the company improve its operations.
• Next Steps: I outlined actionable steps to implement the recommendations and maximize insights for business growth.

Technologies Used

• SQL: For data cleaning, processing, and analysis.
• Spreadsheets: For additional data processing and manipulation.
• R Programming: For creating visualizations to highlight trends and insights.