https://github.com/manishkaa/google_data_analytics_capstone_case_study
This case study is a part of Google Data Analytics Capstone Project
https://github.com/manishkaa/google_data_analytics_capstone_case_study
bigquery data-analysis sql tableau
Last synced: 9 months ago
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This case study is a part of Google Data Analytics Capstone Project
- Host: GitHub
- URL: https://github.com/manishkaa/google_data_analytics_capstone_case_study
- Owner: manishkaa
- Created: 2025-05-08T17:07:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-08T17:16:36.000Z (about 1 year ago)
- Last Synced: 2025-05-17T06:11:16.999Z (about 1 year ago)
- Topics: bigquery, data-analysis, sql, tableau
- Homepage:
- Size: 1.87 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚴 Cyclistic Bike-Share Case Study
## 🌟 Introduction
This repository contains the case study project completed as part of the **Google Data Analytics Professional Certificate**. The project is based on the Cyclistic bike-share data, following a structured approach through various data analysis phases.
### 🎯 Objective
The goal of this case study is to analyze how Cyclistic's annual members and casual riders use the bike-share service differently. The analysis will help develop marketing strategies to convert casual riders into annual members.
---
## 🚲 About Cyclistic
Cyclistic is a bike-share program in Chicago that features a fleet of bicycles, including classic bikes, docked bikes, and electric bikes. Cyclistic's marketing strategy emphasizes increasing annual memberships because members are more profitable than casual riders. The company aims to convert casual riders into annual members through data-driven marketing strategies.
---
## 💼 Business Task
Design marketing strategies aimed at converting casual riders into annual members by analyzing usage patterns.
---
## 📊 Case Study Phases
This case study follows the **Google data analysis process**:
1. **🔍 Ask:** Identify the business problem and key questions.
2. **📂 Prepare:** Collect and examine the data from Cyclistic's public dataset.
3. **🛠️ Process:** Clean, transform, and validate the data for accurate analysis.
4. **📈 Analyze:** Conduct exploratory analysis to find trends and patterns.
5. **📑 Share:** Create data visualizations and compile findings.
6. **🚀 Act:** Formulate data-driven marketing recommendations.
---
## 📝 Key Analysis Questions
- How do annual members and casual riders use Cyclistic bikes differently?
- Why would casual riders buy Cyclistic annual memberships?
- How can Cyclistic use digital media to influence casual riders to become members?
---
## 🔎 Insights and Findings
### 🚶 Usage Patterns
- Members primarily use bikes on weekdays during commuting hours (8 am and 5 pm), indicating work-related trips.
- Casual riders use bikes more frequently during weekends and for longer durations, suggesting leisure trips.
### 🚴 Bike Preferences
- **Classic Bikes:** Most popular among both members and casual riders.
- **Electric Bikes:** Less frequently used but favored more by members for shorter, faster trips.
- **Docked Bikes:** Exclusively used by casual riders.
### 📅 Temporal Patterns
- **Peak Months:** March shows the highest ridership among both groups.
- **Day of Week:** Casual riders are more active on weekends, while members maintain consistent usage throughout the weekdays.
- **Time of Day:** Casual riders use bikes throughout the day, while members have two prominent peaks (morning and evening).
### ⏳ Ride Duration
- Casual riders tend to take longer rides, averaging twice the duration compared to members.
- Members show consistent ride durations throughout the year.
### 📍 Location Patterns
- Casual riders frequently start and end their trips at recreational spots (parks, beaches, museums).
- Members mostly start and end their trips near residential and commercial areas (universities, offices, train stations).
---
## 💡 Recommendations
1. **Targeted Marketing:** Promote annual memberships at popular casual rider locations during peak seasons.
2. **Seasonal Offers:** Introduce summer membership packages to convert casual users.
3. **Commuter Benefits:** Highlight cost savings for daily commutes to encourage membership.
4. **Enhanced Digital Presence:** Use social media campaigns targeting casual riders during weekends.
---
## 🛠️ Technologies Used
- **SQL (BigQuery):** Data processing and analysis.
- **Tableau:** Data visualization and dashboard creation.
- **StackEdit:** Report writing and documentation.
- **GitHub:** Version control and code sharing.
---
## 🗂️ Project Structure
```
cyclistic-case-study/
├── data/ # Raw and cleaned data files
├── scripts/ # SQL queries for data analysis
├── visualizations/ # Tableau dashboards and charts
├── reports/ # Case study documentation
└── README.md # Project overview and details
```
---
## 💻 Usage
1. Clone the repository:
```
git clone https://github.com/manishkaa/Google-Data-Analytics-Capstone-Case-Study.git
```
2. Open the documentation to understand the project structure.
3. Access the `scripts/` folder to review SQL queries.
4. View the visualizations in the `visualizations/` folder.
---
## 📈 Results
The analysis provided insights into how casual and member riders differ in their usage patterns. Recommendations were made to develop targeted marketing strategies for converting casual riders into members, focusing on seasonal promotions and commuting benefits.
---
## 📜 License
This project is licensed under the MIT License.
---
## 🌐 Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for improvements or suggestions.
---
## 📬 Contact
For any questions or feedback, reach out via GitHub issues or directly through my profile.