Bike Share Data Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.

Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.

In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

The Datasets Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) columns:

Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g., Sedgwick St & North Ave) User Type (Subscriber or Customer) The Chicago and New York City files also have the following two columns:

Gender Birth Year

Data for the first 10 rides in the new_york_city.csv file

The original files are much larger and messier, and you don't need to download them, but they can be accessed here if you'd like to see them (Chicago, New York City, Washington). These files had more columns and they differed in format in many cases. Some data wrangling has been performed to condense these files to the above core six columns to make your analysis and the evaluation of your Python skills more straightforward. In the Data Wrangling course that comes later in the Data Analyst Nanodegree program, students learn how to wrangle the dirtiest, messiest datasets, so don't worry, you won't miss out on learning this important skill!

Statistics Computed You will learn about bike share use in Chicago, New York City, and Washington by computing a variety of descriptive statistics. In this project, you'll write code to provide the following information:

1 Popular times of travel (i.e., occurs most often in the start time)

most common month most common day of week most common hour of day

2 Popular stations and trip

most common start station most common end station most common trip from start to end (i.e., most frequent combination of start station and end station)

3 Trip duration

total travel time average travel time

4 User info

counts of each user type counts of each gender (only available for NYC and Chicago) earliest, most recent, most common year of birth (only available for NYC and Chicago) The Files To answer these questions using Python, you will need to write a Python script. To help guide your work in this project, a template with helper code and comments is provided in a bikeshare.py file, and you will do your scripting in there also. You will need the three city dataset files too:

chicago.csv new_york_city.csv washington.csv

All four of these files are zipped up in the Bikeshare file in the resource tab in the sidebar on the left side of this page. You may download and open up that zip file to do your project work on your local machine.

2021 Chicago data set from the bike-sharing company Motivate International Inc.. Provided under this license and originally made available here.

Discover the booming e-bike sharing market! Our analysis reveals a $15 billion market in 2025, projected to grow at an 18% CAGR through 2033. Explore key trends, challenges, and leading companies shaping this dynamic sector. Learn more about micromobility, sustainable transportation, and the future of urban commuting.

Context

This dataset comes from the historical data files of the bike-sharing company that record the ride information of every single ride in terms of start/end time, start/end station, and rider's type/gender/age.

Content

These original data files are stored in an open database, which is operated by Motivate International Inc.. The data files, from the year 2013 to the year 2019, show the status of the same modified date, despite two files of Divvyt_Trips_2018_Q1.zip, and Divvy_Trips_2019_Q2.zip. From the start of the file of 202005-divvy-tripdata.zip, the data file is monthly updated to the database. The data files are transformed and merged into a dataset, called" Divvy_trips_all_2013-2021".

Acknowledgements

Motivate International Inc. (“Motivate”) operates the City of Chicago’s (“City”) Divvy bicycle-sharing service. Motivate and the City are committed to supporting bicycling as an alternative transportation option. As part of that commitment, the City permits Motivate to make certain Divvy system data owned by the City (“Data”) available to the public.

Inspiration

Try to figure out how the members and casual riders use the bike differently and have insight into the riders' behavior.

Introduction

The following is an exploratory case study toward the capstone project requirement for the Google Data Analytics Professional Certificate. The analysis follows the six phases of the Data Analysis process: Ask, Prepare, Process, Analyze, and Act (APPAA) recommended by Google and is made in RStudio. The case study involves data from the fictional bike-share company Cyclistic in Chicago. Data on its customer's trip details over 12 months (April 2020 - March 2021). The data has been made available by Motivate International Inc. under this license.

About Cyclistic

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are tracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system at any time. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. To do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Scenario

I am a junior data analyst working in the marketing analyst team at Cyclistic. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve my recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Ask

Business task

I am trying to answer the following question: How do annual members and casual riders use Cyclistic bikes differently? And after that the main goal: How can we influence casual riders to purchase annual subscriptions based on their riding habits?

Stakeholder

• Lily Moreno: The director of marketing and my manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program.
• Cyclistic marketing analytics team: My team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy.
• Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Prepare

• The data used in this case study is public data (view license here) made available by Motivate International Inc. which operates the City of Chicago’s Divvy bicycle-sharing service.
• The data is located on their server and is organized as separate files by month and year.
• The data format is .csv files within .zip folders.
• I downloaded, unzipped, and stored the original copies of the data on a secured external hard drive. I work with copies on my pc in case the originals need to be referenced.
• For my analysis, I used all the data available from April 2020 to April 2022.
• Given the large sizes of the datasets, I used R via RStudio to prepare, process, and analyze.

License

• The full license for the data: https://ride.divvybikes.com/data-license-agreement
• Bikeshare grants me a non-exclusive, royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, and distribute in my case study.
• The license do...

Dataset

This dataset was created by Rajesh Raina

Contents

San Francisco Ford GoBike , managed by Motivate, provides the Bay Area’s bike share system. Bike share is a convenient, healthy, affordable, and fun form of transportation. It involves a fleet of specially designed bikes that are locked into a network of docking stations. Bikes can be unlocked from one station and returned to any other station in the system. People use bike share to commute to work or school, run errands, get to appointments, and more. The dataset contains trip data from 2013-2018, including start time, end time, start station, end station, and latitude/longitude for each station. See detailed metadata for historical and real-time data . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Cyclistic

These datasets are used for the case study as the capstone project in Google Data Analytics course on Coursera

The datasets have a different name because Cyclistic is a fictional company. For the purposes of this case study, the datasets are appropriate and will enable you to answer the business questions. The data has been made available by Motivate International Inc. which is also a bike share company.

This is public data that you can use to explore how dierent customer types are using Cyclistic bikes. But note that data-privacy issues prohibit you from using riders’ personally identifiable information. This means that you won’t be able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area or if they have purchased multiple single passes.

The primary motivation for using shared e-bikes or shared e-cargo bikes among users in the United Kingdom in 2023 was to reduce journey times. More than half of respondents indicated that this was a reason for choosing an electric bike. This was followed by wanting to avoid fatigue or getting sweaty and to cycle up hills.

12 months (2021-02 to 2022-01) of bike-sharing data from Motivate International Inc. uploaded for the purpose of showcasing a Capstone project of the Google Data Analyst Professional Course on Coursera

Dataset

This dataset was created by Soumya Shanker

Contents

Bike Share Data Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.

Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.

In this project, provided by udacity and you will use data provided by Motivate, a bike-share system provider for many major cities in the United States, to uncover bike-share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

The Datasets Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core of six (6) columns:

Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g., Sedgwick St & North Ave) User Type (Subscriber or Customer) The Chicago and New York City files also have the following two columns:

Gender Birth Year

Dataset

This dataset was created by Michael Christensen

Contents

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights:

A better understanding of how casual riders and annual riders differ Why would a casual rider become an annual one How digital media can affect the marketing tactics Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (03/2022 – 02/2023) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,785,180 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,785,180 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

cyclisticbikeshare

About the company • In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. • Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. • Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. • Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Content

• The data has been made available by ‘Motivate International Inc’ under Data License Agreement. For more details click here
• Data sets has been downloaded from: https://divvy-tripdata.s3.amazonaws.com/index.html
• Provided data type – internal, first party data, original, current, trustworthy
• Cyclistic’s historical trip data of last 12 months (01.11.2020 -10.30.2021) was downloaded for analysis (12 csv files)
• Saved CSV files and XLS files in different folders
• Data was stored locally, and copies of every dataset was stored in google drive, in case I need to access original data quickly from any device
• Data sets contain the same number of columns, same names and same data for easy merging
• Limitations: Financial information and identity information is not available, so I won’t be able to analyze casual or member customers’ financial data, to create financially vining offer for casual customers to convert them into members ###cyclisticbikeshare About the company • In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. • Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. • Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. • Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Analyzing how company’s annual members and casual riders use Cyclistic bikes differently to identify trends and convert casual riders into annual members.

The dataset you see was my project for a case study provided by Google via their Google Analytics course via Coursera. This was a case study option to help a proxy bike share company, Google provided the data through a partnership with the City of Chigaco public transportation,* DiVy*, and the data has been made available by Motivate International Inc. This was a case study option to help a bike share company convert casual riders to annual members. My task was to explore the following question:

How do annual members and casual riders use Cyclistic (fictional company) bikes differently?

A changelog, excel data for the 12 months of 2021, pivot charts, and my presentation (if I were to present in front of stakeholders) are provided to show the skills acquired for my certification as a data analyst.

For transparency and to give credit to the provider of the original raw data:

Motivate International Inc. provided the data for this case study under this license

12 months of trip data used for cleaning, analysis, and identifying trends (dataset is public use and used for purposes of this case study to answer the business question).

Bikeshare Project In this project we explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Chicago, we use data provided by Motivate, a bike-share system provider, to uncover bike share usage patterns. It is a two-phase project, the First about filtering and loading the specified data, and the Second about analyzing data considering four main categories: * Popular times of travel * Popular stations and trip * Trip duration * User information

During working on the project, there were some points I've searched and found different methods to deal with them and I have applied them in different parts of the script, there are some points treated with different methods for the same result like in get_filter function and in duration_trip function.

This Google Data Analytics Capstone Project, Case Study 1, centers around the examination of Cyclistic's bike share data for a fictitious bike-share company. The primary objective of this project is to explore the bike share user’s ride patterns and behaviors in order to enhance marketing strategies and boost annual subscriptions. Leveraging data analysis techniques and tools, the project endeavors to reveal significant insights that can inform business decisions and enhance Cyclistic's overall performance.

Context

Cyclistic launched a successful bike-share offering in 2006. And has grown to a fleet of 5,824 bicycles. These bikes are geo-tracked and locked in a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the network at any time. Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments including flexible pricing plans. Cyclistic offers single-ride passes, full-day passes, and annual memberships. “Customers who purchase single-ride or full-day passes are referred to as CASUAL riders. Customers who purchase annual memberships are MEMBERS.

Business Objective

The main objective of this study is to analyze Cyclistic historical bike trip data to identify trends and the primary distinction in bike usage and behavior between two types of users.

"Casual" riders who pay for individual rides or full-day passes. "Members" who subscribe annually to access the service.

And identify how to convert casual riders into annual members by identifying key differences in how Cyclistic riders operate the service in Chicago.

Using the historical data to answer the following questions: 1. How do annual members and casual riders use Cyclistic bikes differently? 2. Why would casual riders buy Cyclistic annual memberships? 3. How can Cyclistic use digital media to influence casual riders to become members?

Data Source

Data used for this case study is 12 months of rider's trip data between May 2022 through April 2023. Data is publicly available via https://divvy-tripdata.s3.amazonaws.com/index.html provided by Motivate International Inc. under this license https://www.divvybikes.com/data-license-agreement/. The data is organized and contains necessary entities that can be sorted and filtered to gain insights. It is sequential and ROCCC (Reliable, Original, Comprehensive, Current, and Cited). However, there are a few duplicates and records that have N/A values. Hence the data will be cleaned for this project to align with business objectives.

Acknowledgement

Citi Bike is a privately owned public bicycle sharing system serving the New York City boroughs of the Bronx, Brooklyn, Manhattan, and Queens, as well as Jersey City, New Jersey. Named after lead sponsor Citigroup, it is operated by Motivate (formerly Alta Bicycle Share), with former Metropolitan Transportation Authority CEO Jay Walder as chief executive until September 30, 2018 when the company was acquired by Lyft. The system's bikes and stations use BIXI-branded technology from PBSC Urban Solutions.

As of July 2019, there are 169,000 annual subscribers. Citi Bike riders took an average of 56,497 rides per day in 2019, and the system reached a total of 50 million rides in October 2017. source wikipedia

This dataset contains ride details for the year 2021 from January to April. It contains 15 columns each:

tripduration - Duration in Seconds starttime - Start Time and Date stoptime - Stop Time and Date start station id - ID of Start Station start station name - Name of Start Station start station latitude - Latitude of start station start station longitude - Longitude of start station end station id - ID of End Station end station name - Name of End Station end station latitude - Latitude of End station end station longitude - Longitude of End station Bike ID - Bike ID usertype - Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member gender - Zero=unknown; 1=male; 2=female birthyear - Year of Birth

Introduction

This is a case study called Capstone Project from the Google Data Analytics Certificate.

In this case study, I am working as a junior data analyst at a fictitious bike-share company in Chicago called Cyclistic.

Cyclistic is a bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike.

Scenario

The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, our team will design a new marketing strategy to convert casual riders into annual members.

****Primary Stakeholders:****

1: Cyclistic Executive Team

2: Lily Moreno, Director of Marketing and Manager

ASK

1. How do annual members and casual riders use Cyclistic bikes differently?
2. Why would casual riders buy Cyclistic annual memberships?
3. How can Cyclistic use digital media to influence casual riders to become members?

# Prepare

The last four quarters were selected for analysis which cover April 01, 2019 - March 31, 2020. These are the datasets used:

Divvy_Trips_2019_Q2
Divvy_Trips_2019_Q3
Divvy_Trips_2019_Q4
Divvy_Trips_2020_Q1

The data is stored in CSV files. Each file contains one month data for a total of 12 .csv files.

Data appears to be reliable with no bias. It also appears to be original, current and cited.

I used Cyclistic’s historical trip data found here: https://divvy-tripdata.s3.amazonaws.com/index.html

The data has been made available by Motivate International Inc. under this license: https://ride.divvybikes.com/data-license-agreement

Limitations

Financial information is not available.

Process

Used R to analyze and clean data

• After installing the R packages, data was collected, wrangled and combined into a single file.
• Columns were renamed.
• Looked for incongruencies in the dataframes and converted some columns to character type, so they can stack correctly.
• Combined all quarters into one big data frame.
• Removed unnecessary columns

Analyze

• Inspected new data table to ensure column names were correctly assigned.
• Formatted columns to ensure proper data types were assigned (numeric, character, etc).
• Consolidated the member_casual column.
• Added day, month and year columns to aggregate data.
• Added ride-length column to the entire dataframe for consistency.
• Deleted trip duration rides that showed as negative and bikes out of circulation for quality control.
• Replaced the word "member" with "Subscriber" and also replaced the word "casual" with "Customer".
• Aggregated data, compared average rides between members and casual users.

Share

After analysis, visuals were created as shown below with R.

Act

Conclusion:

• Data appears to show that casual riders and members use bike share differently.
• Casual riders' average ride length is more than twice of that of members.
• Members use bike share for commuting, casual riders use it for leisure and mostly on the weekends.
• Unfortunately, there's no financial data available to determine which of the two (casual or member) is spending more money.

Recommendations

• Offer casual riders a membership package with promotions and discounts.