Cyclistic Bike Share Company - Google Data Analytics Capstone Case Study

Table of Contents

• Introduction
• Dataset Overview
• Objective
• Data Analysis Process
• Technologies Used
• Files
• Usage
• Conclusion
• Author
• License

Introduction

This README provides an overview of the Cyclistic Bike Share Company Google Data Analytics Capstone Case Study. Cyclistic is a fictional bike-sharing company that operates in the city of Chicago. The company has both traditional bikes and electric bikes available for rent, and customers can pick up and drop off bikes at various stations across the city. The case study focuses on analyzing historical bike trip data to gain insights into customer behavior and make data-driven recommendations to improve the company's marketing strategies.

Dataset Overview

The dataset used in this case study includes historical trip data from Cyclistic. The data spans 12 months, from May 2022 to April 2023, and contains information about bike rides, including trip duration, start and end times, start and end station locations, and user types. The dataset is provided in the form of individual CSV files, one for each month.

Objective

The objective of this case study is to analyze the Cyclistic bike trip data and provide data-driven recommendations to improve the company's marketing strategies. By understanding customer behavior, usage patterns, and preferences, we aim to identify opportunities for increasing ridership and improving customer satisfaction.

Data Analysis Process

The data analysis process for this case study involves the following steps:

1. Data Gathering: Collect the 12 individual CSV files containing the bike trip data for the last 12 months.
2. Data Cleaning: Clean the dataset by handling missing values, data inconsistencies, and formatting issues.
3. Data Exploration: Explore the dataset to gain insights into user behavior, usage patterns, and trends.
4. Data Analysis: Perform in-depth analysis of the data to identify key findings and trends.
5. Data Visualization: Visualize the analysis results using charts, graphs, and other visual representations.
6. Recommendations: Based on the analysis findings, provide data-driven recommendations to improve Cyclistic's marketing strategies.

Technologies Used

The following technologies were used in this case study:

• R: R programming language was used for data analysis, cleaning, and visualization.
• RMarkdown: RMarkdown was used as the tool to create dynamic and reproducible reports.
• tidyverse: The tidyverse package, including dplyr and ggplot2, was used for data manipulation and visualization.

Files

The case study includes the following files:

1. README.md: The file you are currently reading, providing an overview of the case study.
2. docs: A folder that contains the RMarkdown used for analysis.
3. index.Rmd: RMarkdown file containing the R code and analysis for the case study.
4. index.html: The HTML report generated from the RMarkdown file, summarizing the analysis findings and recommendations.

Usage

To run the analysis and explore the case study, follow these steps:

1. Ensure R and RStudio are installed on your system.
2. Clone the repository or download the files to your local machine.
3. Open RStudio and set the working directory to the location of the case study files.
4. Open the index.Rmd file in RStudio to view and run the analysis code.
5. Knit the RMarkdown file to generate the HTML report (index.html) summarizing the analysis findings and recommendations.

Conclusion

Through this case study, we aim to provide valuable insights and recommendations to Cyclistic Bike Share Company based on the analysis of user data. By leveraging data-driven strategies, Cyclistic Bike Share Company can enhance their product offerings and marketing efforts to better cater to their target audience, ultimately leading to improved customer satisfaction and business growth.

Author

This case study was conducted and documented by Arjit Bhardwaj.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Visit the Cyclistic Bike Share Case Study for detailed analysis and insights.