sinderpreet31 / cyclistic_project Goto Github PK

Case study: How does a bike-share navigate speedy success?

Scenario:

As a data analyst on Cyclistic's marketing team, our focus is on enhancing annual memberships to drive the company's success. We aim to analyze the differing usage patterns between casual riders and annual members to craft a marketing strategy aimed at converting casual riders. Our recommendations, supported by data insights and professional visualizations, await Cyclistic executives' approval to proceed.

About the company

In 2016, Cyclistic launched a bike-share program in Chicago, growing to 5,824 bikes and 692 stations. Initially, their marketing aimed at broad segments with flexible pricing plans attracting both casual riders (single-ride or full-day passes) and annual members. However, recognizing that annual members are more profitable, Cyclistic is shifting focus to convert casual riders into annual members. To achieve this, they plan to analyze historical bike trip data to understand the differences and preferences between the two user groups, aiming to tailor marketing strategies that encourage casual riders to purchase annual memberships.

Project Overview:

This capstone project is a culmination of the skills and knowledge acquired through the Google Professional Data Analytics Certification. It focuses on Track 1, which is centered around Cyclistic, a fictional bike-share company modeled to reflect real-world data analytics scenarios in the transportation and service industry.

Dataset Acknowledgment:

We are grateful to Motivate Inc. for providing the dataset that serves as the foundation of this capstone project. Their contribution has enabled us to apply practical data analytics techniques to a real-world dataset, mirroring the challenges and opportunities present in the bike-sharing sector.Dataset can be download through this link https://www.kaggle.com/datasets/sinderpreet/datainsight-google-analytics-capstone-project/data

Objective:

The primary goal of this project is to analyze the Cyclistic dataset to uncover actionable insights that could help the company optimize its operations, improve customer satisfaction, and increase its market share. Through comprehensive data exploration, cleaning, analysis, and visualization, we aim to identify patterns and trends that inform strategic business decisions.

Methodology:

Data Collection: Utilizing the dataset provided by Motivate Inc., which includes detailed information on bike usage, customer behavior, and operational metrics. Data Cleaning and Preparation: Ensuring the dataset is accurate, complete, and ready for analysis by addressing any inconsistencies, missing values, or anomalies. Data Analysis: Applying statistical methods and data analytics techniques to extract meaningful insights from the dataset.

Visualization and Reporting:

Creating intuitive and compelling visualizations to present the findings clearly and effectively, facilitating data-driven decision-making. Findings and Recommendations:

Conclusion:

The Cyclistic Capstone Project not only demonstrates the practical application of data analytics skills in a real-world scenario but also provides valuable insights that can drive strategic improvements for Cyclistic. Through this project, showcasing the power of data analytics in transforming data into actionable knowledge, underscoring the importance of data-driven decision-making in today's competitive business landscape.

Acknowledgments:

Special thanks to Motivate Inc. for their support and for providing the dataset that made this project possible. Their contribution is immensely appreciated and has significantly enhanced the learning experience.

STRATEGIES USED

Case Study Roadmap - ASK

• What is the problem you are trying to solve?
• How can your insights drive business decisions?

Key Tasks

• Identify the business task
• Consider key stakeholders

Deliverable

• A clear statement of the business task

Case Study Roadmap - PREPARE

• Where is your data located?
• Are there any problems with the data?

Key tasks

• Download data and store it appropriately.
• Identify how it’s organized.

Deliverable

• A description of all data sources used

Case Study Roadmap - PROCESS

• What tools are you choosing and why?
• What steps have you taken to ensure that your data is clean?

Key tasks

• Choose your tools.
• Document the cleaning process.

Deliverable

• Documentation of any cleaning or manipulation of data

Case Study Roadmap - ANALYZE

• Has your data been properly formaed?
• How will these insights help answer your business questions?

Key tasks

• Perform calculations
• Formatting

Deliverable

• A summary of analysis

Case Study Roadmap - SHARE

• Were you able to answer all questions of stakeholders?
• Can Data visualization help you share findings?

Key tasks

• Present your findings
• Create effective data viz.

Deliverable

• Supporting viz and key findings

Case Study Roadmap - ACT

• What is your final conclusion
• Is there additional data you could use to expand on your findings?

Deliverable

• Top three recommendations based on your analysis.