XiyuanWu / Google_Data_Analytics

Google Data Analysis Capstone - Cyclistic Bike Share Analytics

Google Data Analysis Capstone:

Cyclistic Bike Share Analytics

Please read this file first before you open a folder.

Introduction

You are a junior data analyst working on 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.

Key information includes:

• Cyclistic: A bike-share company based in Chicago with a range of bicycles, including accessible options.
• Users: Primarily Casual Riders (single-ride or full-day pass users) and Members (annual membership holders).
• Goal: To convert Casual Riders into annual Members, enhancing growth and profitability.

For more information about this case study, please refer to Case Study 1: How does a bike shared navigate speedy success.pdf for more detail.

Question

For this case study, we are exploring one question: How do annual members and casual riders use Cyclistic bikes differently? I will explore this question by using two ways of data analysis.

Approach

For this case study, I have used BOTH ways to complete this analysis: Spreadsheet and Python.

Spreadsheet

At first, I used a spreadsheet to analyze this case study. A spreadsheet, which is based on a data tool, is easy to get started. But as the amount of data is enormous, the spreadsheet can't handle them as the amount of data keeps increasing. And since the data are too massive, I only analyzed one month for references.

For more details about the analysis step, please refer to Spreadsheet Analysis Visualization.

Python

This is an advanced way to analyze data. Previously, we talked about how spreadsheets can't handle massive amounts of data, and that's why Python comes.

Whole analysis and Visualization are based on Python. During the analysis process, I thoroughly cleaned and analyzed the data for an entire year.

Please note Python analysis is not taught in this course; this is content for the advanced class.

For more details about the analysis step, please refer to Python Analysis Visualization.

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Additional Information

This is the additional information I want to add here, notice this part is NOT related to this capstone projects at all.

The additional info I want to add is a project I did two years ago(a high school English project), and I’m doing a case study similar to this one. We were told to select topics we wanted, write a thesis, use statistics(found on our own) to support our idea and come up with a conclusion. Finally, we need to make slides and present them in class. I just want to show visualization here.

Here is some keys info:

Topics: Social Media Addiction

Question: Why are younger generations addicted with the number of followers and likes?

Thesis: Younger generations are addicted to the number of followers and likes, and they are very addicted to social media.

Some key statistics:

• 4.48 billion: Number of people use social media in global world
• 210+ Million: Estimates people suffer from internet and social media addictions worldwide
• 2.42 HR: The time do people spend on social media each day globally for users aged 16 to 64 on any device
• 8.4: The average of a person have social media accounts in worldwide And some additional statistics such as gender, age, country.

Conclusion:

Social media can be beneficial when used in ways that help build deeper connections between us.

Unfortunately, social media is quickly becoming one of the strongest forces that divide us. We are drawn into the race for likes, competing with our followers, constantly comparing ourselves to an artificial ideal. We need to be conscious of how we use social media platforms so they can bring us together rather than divide us.

Getting rid of social media altogether is not the solution. The problem is not social media itself, but rather, the way we use social media.

For more information, please see visualization here:

presentation mode(With Annotation): click here.

PDF mode(Quick view): Download the file