This project showcases a full data analytics workflow using Excel and real CitiBike NYC trip data. I cleaned the raw dataset, performed descriptive analysis, and created visualizations to extract insights on rider behavior and station usage patterns.
- 🧹 Cleaned over 3,500 duplicate rows and handled missing values
- 📊 Used pivot tables to analyze trip duration, user types, and usage patterns
- 📈 Built visual charts to support a data-driven story for stakeholders
- 🎯 Demonstrated Excel proficiency and a sharp eye for insights
Each visualization answers a key business question about CitiBike usage:
-
📍 [Top 20 Pick-Up Locations]
→ What are the most popular pick-up locations across NYC? -
🧓 [Trip Duration by Age Group]
→ How does the average trip duration vary across different age groups? -
👥 [Bike Rentals by Age Group]
→ Which age group rents the most bikes? -
📆 [User Type by Day of Week]
→ How does bike rental differ between Subscribers and Customers across weekdays and weekends?
- 🚏 Top Start Stations: The three busiest pick-up locations account for a significant portion of total rides
- ⏱️ Trip Duration: Younger riders (18–25) tend to have longer average trip durations
- 👥 Age Group Usage: Riders aged 25–35 rent the most bikes overall
- 📆 User Behavior:
- Subscribers use CitiBike mostly during weekdays (commuting pattern)
- Customers use it more on weekends (leisure pattern)
- Microsoft Excel
- Pivot Tables & Pivot Charts
- Basic Descriptive Statistics (mean, median, range)
This project was built as part of my self-paced journey into data analytics. It reflects not just technical skills, but also my ability to communicate findings clearly and extract meaningful insights from raw data — all using just Excel.