Citi Bike Company data Analysis using Excel
Citi Bike is the largest bike-share program in the United States, with 20,000 bikes and over 1,300 pick-up stations across Manhattan, Brooklyn, Queens, the Bronx, and Jersey City. As stated on their website, the service was designed for quick trips with convenience in mind, offering a fun and affordable way to get around town. Users can sign up for annual membership, or buy a short-term pass through the Citi Bike app. Once they’ve joined, they simply locate a nearby bike, ride around as they please, and return it to a nearby station once they’re done 🚴
Like most organizations, Citi Bike is constantly looking for ways to improve its business model and provide an even better experience for its customers. Through the Citi Bike app, they are able to gather loads of useful data which, when analyzed, reveals great insights into things like user demographics and behavior—for example, when and where people pick up and drop off their bikes and how long the average journey lasts. Such data is extremely valuable as it helps the good people at Citi Bike to understand how the service is being used and to plan and make decisions accordingly. For example, at what rate is the customer base growing and how many more bikes should they install across the city to accommodate this growth? Where should they install the most bikes? Who should they tailor their marketing and advertising to? Essentially, data helps them to determine where and how their money and efforts can be invested for maximum impact.
The mission of this project is to analyze data collected by Citi Bike (for the purposes of this project, we will call them NY Citi Bike to make things consistent) and help key stakeholders to make smart, data-driven decisions based on the insights you uncover.
- What are the most popular pick-up locations across the city for NY Citi Bike rental?
- How does the average trip duration vary across different age groups?
- Which age group rents the most bikes?
- How does bike rental vary across the two user groups (one-time users vs long-term subscribers) on different days of the week?
- Does user age impact the average bike trip duration?
- Data Cleaning A) Identifying and removing duplicates B) Identify and remove missing data points
- Descriptive Statistics and Exploratory
- Data Visualizations A) Create a bar chart for the top 20 NY Citi Bike pick-up locations B) Create a column chart showing the average trip duration across different age groups C) Create a bar chart for the number of bike rentals per age group D) Produce a stacked stepped area chart for weekday and user type E) Produce scatter plots for age vs trip duration
- Creating and sharing the data story A) Identify the audience B) Construct a compelling narrative C) Create and organize the data visualizations D) Create a presentation to share the data story
- Raw Dataset
- Final Data Analysis Presentation
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