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What story does your data tell?
What story does your data tell?
Throughout the data analysis process, I saw that the data storytelling and data visualization worked together which gave a conclusion that members occupied the most position in the total market of the Cyclistic company. However, member cyclists tend to use the services during the mid weekday, especially Tuesday, Wednesday, and Thursday. Casuals tend to travel on the weekends like Friday, Saturday, and Sunday which could explain the reason why they have more trip length.
Throughout the data analysis process, I saw that the data storytelling and data visualization worked together which gave a conclusion that members occupied the most position in the total market of the Cyclistic company. However, member cyclists tend to use the services during the mid weekday, especially Tuesday, Wednesday, and Thursday. Casuals tend to travel on the weekends like Friday, Saturday, and Sunday which could explain the reason why they have more trip length.
Introduction
Introduction
This is my project of Google Data Analytics Capstone - Case Study 1.
This is my project of Google Data Analytics Capstone - Case Study 1.
The detailed information about the case study can be found in the Google Data Analytics course on Coursera.
The detailed information about the case study can be found in the Google Data Analytics course on Coursera.
For this project, I will produce a report with the following deliverables:
For this project, I will produce a report with the following deliverables:
1. A clear statement of the business task
1. A clear statement of the business task
2. A description of all data sources used
2. A description of all data sources used
3. Documentation of any cleaning or manipulation of data
3. Documentation of any cleaning or manipulation of data
4. A summary of your analysis
4. A summary of your analysis
5. Supporting visualizations and key findings
5. Supporting visualizations and key findings
6. Your top three recommendations based on your analysis
6. Your top three recommendations based on your analysis
I will follow the steps of the data analysis process including Ask, prepare, process, analyze, share, and act as well as follow the Case Study Roadmap as a guide to complete this project.
I will follow the steps of the data analysis process including Ask, prepare, process, analyze, share, and act as well as follow the Case Study Roadmap as a guide to complete this project.
Scenario:
Scenario:
You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. 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, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.
You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. 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, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.
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. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.
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. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.
I. Case Study Roadmap - Ask
Guiding questions
· What is the problem you are trying to solve?
The company believes that an annual membership group of customers will bring more financial benefits rather than casual riders. Thus, one of the main questions raised is to identify how the differences in behaviors of using bikes between 2 groups of customers – annual members vs. casual riders.
· How can your insights drive business decisions?
The insights into biking usage patterns will help the marketing team to understand the differences in behaviors between 2 groups of customers and then design an effective marketing strategy for their project.
· Business task statement:
Based on provided historical bike trip data: analyze and identify casual riders and annual members' usage patterns. This helps the marketing team to build up a new strategy to convert casual riders into annual members.
II. Case Study Roadmap - Prepare
Guiding questions
● Where is your data located?
The data is located at https://divvy-tripdata.s3.amazonaws.com/index.html
● How is the data organized?
The data is separated by month (csv file)
● Are there issues with bias or credibility in this data? Does your data ROCCC?
There are no issues with bias or credibility in the dataset. It's also ROCCC because it's reliable, original, comprehensive, current, and cited.
● How are you addressing licensing, privacy, security, and accessibility?
The company has its own license over the dataset. There is not any personal information about the riders provided in the data.
● How did you verify the data’s integrity?
All the files have a consistent format (columns and type of data).
● How does it help you answer your question?
The data provide detailed keys information to analyze
● Are there any problems with the data?
The limitation is that I will be using only 1 - month data as a sample instead of 12 months.
Reason for limitation: the files are too large to download due to low disk space.
Deliverable: A description of all data sources used
The riding data provided by the Cyclistic company which is used to analyze is data for June 2022.
III. Case Study Roadmap - Process
Guiding questions
· What tools are you choosing and why?
- I'm using Excel for this project, for two main reasons: The large dataset I chose is not large so Excel can handle it effectively and I’m very familiar with Spreadsheet (Excel or Google Sheets).
· Have you ensured your data’s integrity?
- Yes, the data format is consistent throughout the columns.
· What steps have you taken to ensure that your data is clean?
- Any duplicated values are checked and removed: no duplicates are found
- The columns are also formatted according to the requirement.
· How can you verify that your data is clean and ready to analyze?
- It is shown in the posted work.
· Have you documented your cleaning process so you can review and share those results?
- Yes, it's all documented in the comments.
Deliverable
[x] Documentation of any cleaning or manipulation of data: Yes
IV. Case Study Roadmap - Analyze
Guiding questions
● How should you organize your data to perform analysis on it?
- Data has been organized in a single Excel file
● Has your data been properly formatted?
- Yes
● What surprises did you discover in the data?
- Member users have less ride length than casual riders
● What trends or relationships did you find in the data?
- More members than casuals in the dataset
- Members have less riding time
- Members tend to prefer classic bikes to docked bikes.
● How will these insights help answer your business questions?
- The sample data of 1 month is not enough to build a profile for members. Therefore, I recommend analyzing more data to have correct insights.
Deliverable A summary of your analysis: Yes
Members had the most rides at 52% of total rides by users. The total rides by weekday also corresponded to the number of rides, which means members still had more rides than casuals.
However, when comparing average ride length and total ride length, they both agreed that casual cyclists tend to have longer trips than members.
It is a strong preference for classic bikes for both groups of users, followed by electric bikes. There are some casual riders who choose docked bikes, but no members ride docked bikes.
V. Case Study Roadmap - Share
Guiding questions
· Were you able to answer the question of how annual members and casual riders use Cyclistic bikes differently?
- Yes. Although the data showed differences between casuals and members, it needs additional analysis, and recommends more data to get a deeper understanding.
· What story does your data tell?
- Throughout the data analysis process, I saw that the data storytelling and data visualization work together which gave a conclusion that members occupied the most position in the total market of the Cyclistic company. However, member cyclists tend to use the services during the mid-weekday, especially Tuesday, Wednesday, and Thursday. Casuals tend to travel on the weekends like Friday, Saturday, and Sunday which could explain the reason why they have more trip length.
· How do your findings relate to your original question?
- The findings could help to get insights into differences in the behavior of casuals and members, which relates to the "Find the keys differences between casuals and annual riders" question.
· Who is your audience? What is the best way to communicate with them?
- The main target audience is my Cyclistic marketing analytics team and Lily Moreno.
- The best way to communicate is through a slide presentation of the findings.
· Can data visualization help you share your findings?
- Yes, the findings are through data visualization.
· Is your presentation accessible to your audience?
- Yes, the charts were made using vibrant colors, and corresponding labels.
VI. Case Study Roadmap - Act
Guiding questions
· What is your final conclusion based on your analysis?
- The data concluded that the members and casuals have created different patter in habits of bike usage.
· How could your team and business apply your insights?
- Need further analysis to give the final decision to the marketing team.
· What next steps would you or your stakeholders take based on your findings?
- Further analysis could be done
· Is there additional data you could use to expand on your findings?
- Working on more data to get more information for both groups.
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