capstone project google data analytics

• Oct 18, 2021
• 10 min read

Google Data Analytics Capstone Project

Updated: Jul 5, 2023

I worked on the Google Data Analytics Capstone Project, Track 1, Case Study 1. I will be diving into the background, my full process of cleaning, analyzing and visualizing the data, along with my final suggestions and summary of the data.

Quick Links :

Tableau Dashboard | Github R Code for Analysis | Github R Code for Tableau Visualization | LinkedIn Post

Below is a table of contents in case you want to go to a specific section.

Table of Contents:

Microsoft excel.

Finished Project

Summary of Data

Business Suggestions

What I Learned

Cyclistic is a bike sharing program which features more than 5,800 bikes and 600 docking stations. It offers reclining bikes, hand tricycles, and cargo bikes, making it more inclusive to people with disabilities and riders who can't use a standard two-wheeled bike. It was founded in 2016 and has grown tremendously into a fleet of 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.

Previously, Cyclistic's marketing strategy tried to build the general awareness and appeal to broad consumers. It has flexible pricing plans: single-ride passes, full-day passes, and annual memberships. Those who purchase single-ride or full-day passes are referred to as casual riders while those who purchase annual memberships are Cyclistic members .

My Role : In this scenario I am a junior data analyst at Cyclistic and my team has been tasked with the overall goal (see below) of designing marketing strategies

Overall Goal : Design marketing strategies aimed at converting casual riders into annual members.

Business Question : "How do annual members and casual riders use Cyclistic bikes differently?"

Below I will describe step-by-step the process I used to for this project. If you want to skip ahead to the business suggestions move onto the section "Insights".

Overview : I first analyzed the data separately (each month) in Excel, then used R to analyze the data as a whole (one year). Finally I created a dashboard in Tableau and used Figma to support the design elements.

I initially wanted to gather and analyze my data in Excel because it was the tool I was most familiar with and I could get a general understanding of the data quicker. I did not combine all of the spreadsheets into one because that would've taken more processing power than my computer had.

I began downloading the data from divvy-tripdata , and turning the .csv files into excel spreadsheets. I downloaded the most recent year of data which was at the time of starting my project:

August 2020

September 2020

October 2020

November 2020

December 2020

January 2021

February 2021

Added two columns to all of the months:

ride_length calculated the total ride length for each trip using the start_at column which was: ending time minus starting time.

day_of_week calculated the day of the week for each trip using the start_at column date.

Went over the business task and the information I had at hand and how that could be used to figure out how members and casual riders use the bike service differently

Came up with metrics to look at such as :

total number of rides per hour, per day of the month, per season, per day of the week, and for different bike types

Average ride length between members and casual

For every month in Excel created pivot tables and charts to go with the analysis on (this took the longest):

Total Rides per Weekday - calculated the total rides for members and casual and separated it by day of the week; used a cluster column chart

Average Ride Length - calculated the average ride length for members and casual and separated it by day of the week; used a cluster column chart

Total Rides per Hour - calculated the total rides for members and casual separated by the time of the day (24hr); used a line comparison chart

Total Rides per Day - calculated the total rides for members and casual separated by the day of the month; used a line comparison chart

Total Rides per Bike Type - calculated the total rides for members and casual separated by Bike type; used stacked column chart

I also created a Google docs Notes list where I wrote down the exact steps for each month (had a checklist) and included my insights for each month

Time Spent:

535 minutes or just under 9 hours to complete.

I originally wanted to use SQL but the files were too big to upload and I couldn't figure out how to utilize Google Cloud Platform. Instead I used R to analyze the data because it could handle all of the information quicker than Excel, and I wanted to work on my R skills. Below is my general process in R, I didn't include my mistakes/missteps or errors for the sake of brevity.

View my full code on my Github for this capstone project here .

Load all of the libraries I used: tidyverse, lubridate, hms, data.table

Uploaded all of the original data from the data source divytrip into R using read_csv function to upload all individual csv files and save them in separate data frames. For august 2020 data I saved it into aug08_df, september 2020 to sep09_df and so on.

Merged the 12 months of data together using rbind to create a one year view

Created a new data frame called cyclistic_date that would contain all of my new columns

Created new columns for:

Ride Length - did this by subtracting end_at time from start_at time

Day of the Week

Time - convert the time to HH:MM:SS format

Season - Spring, Summer, Winter or Fall

Time of Day - Night, Morning, Afternoon or Evening

Cleaned the data by:

Removing duplicate rows

Remove rows with NA values (blank rows)

Remove where ride_length is 0 or negative (ride_length should be a positive number)

Remove unnecessary columns: ride_id, start_station_id, end_station_id, start_lat, start_long, end_lat, end_lng

Calculated Total Rides for:

Total number of rides which was just the row count = 4,152,139

Member type - casual riders vs. annual members

Type of Bike - classic vs docked vs electric; separated by member type and total rides for each bike type

Hour - separated by member type and total rides for each hour in a day

Time of Day - separated by member type and total rides for each time of day (morning, afternoon, evening, night)

Day of the Week - separated by member type and total rides for each day of the week

Day of the Month - separated by member type and total rides for each day of the month

Month - separated by member type and total rides for each month

Season - separated by member type and total rides for each season (spring, summer, fall, winter)

Calculated Average Ride Length for:

Total average ride length

Type of Bike - separated by member type and average ride length for each bike type

Hour - separated by member type and average ride length for each hour in a day

Time of Day - separated by member type and average ride length for each time of day (morning, afternoon, evening, night)

Day of the Week - separated by member type and average ride length for each day of the week

Day of the Month - separated by member type and average ride length for each day of the month

Month - separated by member type and average ride length for each month

Season - separated by member type and average ride lengths for each season (spring, summer, fall, winter)

Then using all of this data I created my own summary in my case notes and took note of the: total rides for each variable, average ride lengths for each variable, and the difference between members versus casual riders. I originally wanted to create a report using R Markdown as well but for the sake of time (I had already spent over 20 hours on the project so far), I decided to skip this step, and write this article instead.

1045 minutes or about 17 and a half hours to complete.

While I learned the basics of Tableau in the Google Course I wanted more practice with visualizing data and creating dashboards.

To view my completed dashboard click here .

I created a separate R code (you can view it here on Github) that made some changes for specifically the Tableau portion.

For ride length I rounded the digits by 1, meaning my numbers were 29.8 or 12.5.

Revised how I created my "month" column. I used mutate() to create a column that had the month in ___ format and not number format. So instead of 01 it would say "January"

Cleaned the data: removed rows with NA values, removed duplicate rows, removed where ride_length was 0 or negative and removed unnecessary columns like: ride_id, start_station_id, end_station_id, start_lat, start_long, end_lat, end_lng

Created a new dataframe with this information so I could test the difference between the original data frame (cyclistic_date) that I used for my analysis and the data frame I would use for Tableau (cyclistic_tableau).

In this new data frame I removed more columns to make calculations quicker in Tableau. I removed: start_station_name, end_station_name, time, started_at, ended_at

Downloaded this data frame into a .csv file which I uploaded to Tableau

Created graphs similar to those I created in Excel but added a few:

Total Rides by Bike Type

Ride Length by Weekday

Total Rides by Weekday

Total rides by hour, total rides by month.

Then I created a basic dashboard with all of that information, a prototype for me to view while I was creating the final dashboard ( Figure 1 below).

Created a prototype mockup in Figma

Created a final version of the mockup in Figma

Edited Dashboard in Tableau to reflect design in Figma

Edited graphs in Tableau

Made bar graphs round

Added annotations

Highlights to specific important notes

Got rid of labels for visual purposes

Combined Figma and Tableau (used dashboard created in Figma as the background for my Tableau Dashboard) to create a final prototype ( Figure 2 below)

Made minor edits to design elements and created final dashboard ( Figure 3 - Cyclistic Dashboard V1 )

On April 24, 2023 I decided to update my dashboard (See Finished Project , image Final Dashboard - Cyclistic Dashboard V2 ). All of the analysis is the same. The only changes have been to the dashboard. Which include:

Adding horizontal grid lines to a few of the charts

Updating the tool tips.

Making all of the top metric values (e.g. Total Rides, Average Ride Length, etc.) interactive in Tableau instead of in Figma.

765 minutes or almost 13 hours to complete.

Tableau Prototype

Below was my first draft of the dashboard only using Tableau.

Prototype using Figma Background

Combined Figma and Tableau (used dashboard created in Figma as the background for my Tableau Dashboard) to create a final prototype.

Final Dashboard V1

Made minor edits to design elements and created final dashboard. This was the original final dashboard.

I am including the other tools I used.

Figma to create my background and help develop the dashboard aesthetics.

Google Docs helped me keep track of all of my documents for this project like:

Date Log - I wrote down what I did that day related to my project

Resources - A list of resources I frequently used

Case Notes - Notes for the case study including the final insights, what I was looking for, and anything else having to do with the case

Evernote to draft this article before I uploaded it here.

FINISHED PROJECT

Here is my finished project: Google Capstone Project (V2) . You can view the links to my R code on Github used for analysis here and the code for Tableau here .

Note: This is V2 with a few minor changes to the dashboard. Including:

SUMMARY OF DATA

Those who purchase single-ride or full-day passes are referred to as casual riders while those who purchase annual memberships are Cyclistic members .

Total Rides by User Type

Average Ride Length per User Type

Average Ride per Weekday

Members had more rides with 2,328,763 total rides or 56% and casual riders had 1,823,376 total rides or 43%.

Total Rides per Bike Type

Both casual riders and members used the classic bike the most with 1,777,593 rides or 43% of total rides, followed by docked bikes with 1,545,936 rides or 37% of total rides, and lastly with electric bikes at 828,610 rides or 20% of total rides.

Average Ride Length by User Type

The total average ride length was 24 minutes. For casual riders it was longer at 27 minutes while members was 14 minutes.

Average Ride Length per Weekday

For the average ride length per weekday both casual riders and members had an increase in the average ride length on the weekends. For both Sunday was the longest at 31 minutes.

Saturday was the most popular weekday combining casual riders and member rides with 784,239 rides or 19% of total rides. But for member rides only Wednesday was the most popular day with 356,060 rides, 5,407 rides more than Saturday.

5PM or 17:00 was the busiest hour for both members and casual riders with 426,685 rides or 10% of the total rides. Typically rides began increasing in the morning at 6AM and rose until 5PM then dropped afterwards. The afternoon was the busiest for both rider types with 1,905,797 rides or 45% of total rides. 4AM was the least popular hour.

July was the busiest month combining casual riders and member rides at 691,476 rides or 16% of total rides. While summer was the most popular season for both at 1,903,446 rides or 46% of total rides. Looking at just members August is actually the busiest month with 323,140 rides, 816 rides more than July. Winter is the least popular season and February is the least popular month.

Final Summary

The most popular bike among with riders was the classic.

Busiest time was afternoon and the peak time was at 5PM for both casual riders and members.

Busiest weekday was Saturday, casual riders used the service the most on the weekends.

Busiest season was Summer for both types of riders.

Most rides by User Type was members but casual riders weren't far behind.

The average ride length was 24 minutes but casual riders on average rode 23 minutes longer than members.

BUSINESS SUGGESTIONS

This was the hardest part for me for the whole project. I have never provided suggestions for a business nor worked in marketing. Any feedback here would be appreciated.

These are my suggestions for the marketing team to convert casual riders to annual members:

Personalize discounts and show perks in the membership program based on their preferences and riding habits.

Emphasize the benefits of memberships, including discounts during busy times of the year like during Summer, or on the weekends.

Have existing members to share their stories about how using Cyclistic's system has changed their life, to create a sense of community, offer a discount if they do so this will help encourage new riders to join the program.

WHAT I LEARNED

Below is what I learned/practiced from over 40 hours spent on this project:

Pivot Tables in Microsoft Excel

Practice using R for data analysis and cleaning specifically using the tidyverse package for data analysis

Graphs in Tableau, edited visual elements along with creating different charts and filters.

Design elements of an effective dashboard

Combining the design feature of Figma with the functionality of Tableau

R portion of my project I found Itamar's case study on Kaggle using R as well, a helpful resource.

Tableau portion I used Navneet Singh's Tableau Dashboard as inspiration.

• Data Analytics
• Portfolio Projects

Recent Posts

The Power of Collaborative Projects in Data Analytics

Deep Work Dashboard 2023

Behind the Scenes: Creating a SQL Course

1 comentario

Wow Kelly. This really is impressive. Glad to see you are really into this process. You have definitely found your niche!!

Here to help you grow

Whether you're looking to build your business, develop your career, or pick up a new digital skill, we can help you get started.

What can we help you with?

And what would you like to do?

• Show me everything
• Prepare for a new job
• Develop communication skills
• Increase my productivity
• Learn about digital marketing
• Learn coding & development skills
• Get started with artificial intelligence
• Get started with cloud computing
• Stay safe online
• Learn design skills
• Improve my digital wellbeing
• Champion diversity
• Learn about sustainability
• Understand my audience
• Start selling online
• Expand internationally
• Keep my business safe online

Grow your career

Whether you're writing your first CV or deepening your technical knowledge, our library is full of ways to sharpen your digital skillset.

Google Career Certificates

Earn a Google Career Certificate to prepare for a job in a high-growth field like Data Analytics, UX Design, and more.

Introductory digital skills courses

Get started with a range of digital skills, with entry level courses in everything from online marketing to coding.

Cloud computing fundamentals

From intro to advanced-level learning, find out more about cloud computing principles and career paths.

Google product trainings

Learn how to get the most out of the Google products you use, like Google Ads or Analytics.

Grow your business

From bringing your business online for the first time to growing its reach internationally, our library of online learning and tools can help you take your business further.

Your Digital Essentials Guide

Get an introduction to the products, tools and tips that can help you build an online presence for your small business.

Flexible online training

Learn online, at your own pace, with a library of training made to help strengthen your business with digital skills.

Resources for startups

Google for Startups connects you to the right people, products and best practices to help your business thrive.

Helpful tools for small business owners

Google Business Profile

Manage how your business shows up on Google Search and Maps to help new customers find you more easily.

Market Finder

Identify new potential markets and start selling to customers at home and around the world.

Growth stories

Meet people all over Europe who are using technology to adapt and grow their business or career.

About Grow with Google

Grow with Google is a programme that helps people to grow their careers or businesses by learning new skills and making the most of digital tools. We partner with governments and local organisations to develop digital skills and tools where they are needed most.

Google Data Analytics Capstone: Cyclistic

Check my project on Kaggle

I decided to use R and Tableau (for the visualization part) for this project.

INTRODUCTION

Cyclistic is a bike-share company operating in Chicago. The director of marketing wants to maximize the number of annual memberships.

The goal is to understand how casual riders and annual members use Cyclistic bikes differently. Then, design a new marketing strategy to convert casual riders into annual members.

In order to answer the key business questions, I’m going to follow the steps of the data analysis project: ask, prepare, analyze, share and act.

How do annual members and casual riders use Cyclistic differently?

Annual members are much more profitable than casual riders. I need to find a strategy to convert casual riders into annual members through the help of Data Analysis

Data location: Divvy Tripdata

Data are public and organized in .csv, available for download for everybody ( more information about the data ).

First of all, I’m going to install the and load the packages I need for my analysis

Importing all the data sets available

Before binding our data sets, I need to make sure that the operation is doable

The previous function returned a FALSE as a result.

At the current status, I can’t put our data sets together as in the first 8 data sets, end_station_id and start_station_id have an inconsistent data type.

Going to change them into character.

Let’s check again if everything is working now

The previous function returned TRUE this time. I can proceed to bind together all the data sets

Now that my data set is ready, I can process the data.

First, I’m going to remove the columns that I don’t need in this analysis

Removing rows contating NA and NULL values

With a lighter dataset, I now calculate the length of every ride

For my analysis, I decided to extract the starting time (only the hour) of the ride along with the day of the week and the month of the ride.

After this, I’m going to remove the short rides and the rides with a negative value in their length

Finally, I can export the processed data set

For the Analyze phase, I used Public Tableau .

What type of bikes do Cyclistic customer prefer?

The docked bikes are the most used, followed by classic and electric.

Casuals and Members share the same preferences about the type of bike

When do Cyclistic customers use our bikes the most? (hour analysis)

The peak of rides is in the afternoon, between 4PM and 6PM for both Casuals and Members.

Members use our bikes much more than Casuals in the morning, between 6AM and 9PM. Casuals use our bikes more than Members after 9PM (until around 4 AM).

When do Cyclistic customers use our bikes the most? (daily analysis)

From Monday to Friday, Members use our bikes much more than Casuals. On the weekend, the situation is the opposite. Saturday show a big increment of Casuals compared to other days, especially from Monday to Thursday.

When do Cyclistic customers use our bikes the most? (monthly analysis)

The busiest months for both Casuals and Members are the mid-year months with a peak in June and July. Members use our bikes more than Casuals on every month except for July.

Following, a bar chart showing Casuals and Members month and day of the week preferences.

How long are Cyclistic customers rides? (daily analysis)

Casuals rides are always longer than Members rides.

How long are Cyclistic customers rides? (monthly analysis)

What are the most used stations by Casuals?

Casuals and Members use different starting stations for their rides. Streeter Dr & Grand Ave is by far the most used station by Casuals

The findings are going to be showed to the stakeholders through a presentation.

The stakeholders team is composed by:

• Lily Moreno: Cyclistic director of marketing
• Cyclistic marketing analytics team
• Cyclistic executive team

Thanks to the Data Analysis I was able to find out “How do annual members and casual riders use Cyclistic differently”

According to the findings, I recommend to act as follow to maximize the number of annual memberships, converting the Casuals into Members:

• Incentivate the use of our bikes on weekdays and non mid-year months through special offers for new subscribers
• Offer discounts for longer rides
• Act on the most used stations

Related posts:

Capstone Project - Google Data Analytics Certification

Leah williams.

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com .

Google Data Analytics Certificate - Capstone Project

This project contains code and report for the Google Data Analytics Capstone Project as part of the certificate program.

The results have been documented in the Analysis Cyclistic 2019 Riders R Markdown Notebook.

Project Overview

In this project the trip data of the Cyclistic bike-share program has been analyzed to perform customer conversion. Cyclistic has casual leisure riders and annual membership riders, the goal is to gain more annual members.

This project has two steps:

Determine how casual riders and annual member riders use Cyclistic bikes.

Identify strategies to convert casual riders into annual members.

Data Description

The data has been provided by Coursera through the resource site. The dataset contains four (4) files containing trip data by the bike user type for each quarter to make up a calendar year of trip data for the year 2019.

Technical Overview

The project has been divided into steps which include:

• Data Exploration and Cleaning

• Data Analysis

• Create a Presentation of the Deliverable

• Create a Portfolio Site

• Add the Capstone Project Resource Links to Resume

• Submission to Kaggle

Requirements

All the project requirements are provided Coursera.

Guiding Questions

• What is the problem you are trying to solve?

• How can your insights drive business decisions?

• Identify the business task.

• Identify final conclusions based on analysis findings.

Dependencies

The main dependency for this project is the package tidyverse.

Collect Data

Upload dataset files.

Wrangle Data: Organize

Inspect the column names.

Rename the columns.

Inspect the data frame.

Bind rows to combine files.

Clean and Adjust Data

Inspect the table created.

Format rider types.

Format date times.

Perform Calculations.

Format ride_length.

Clean Data and Create New Data Frame.

Analyze Data

Analyze ride_length.

Analyze rider_types.

Analyze average duration.

Analyze average duration by weekday and rider type.

Analyze weekday use.

Process Data

Check for Data Errors.

Visualize Data

Visualize Data for Rides by Rider Type.

Visualize Data for Average Duration.

Create a file.

Share Findings

Portfolio Site

Cyclystic 2019 Riders https://sites.google.com/view/leahsprojects

Navigation Menu

Search code, repositories, users, issues, pull requests..., provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications

Exploratory Data Analysis on Bellabeat fitness tracker app using Python. Capstone project from Google Data Analytics Professional Certification.

katiehuangx/Google-Data-Analytics-Capstone

Folders and files, repository files navigation, google data analytics: capstone, bellabeat fitness tracking app analysis.

This analysis is an optional Capstone project from the Google Data Analytics Professional Certificate on Coursera.

Background:

Bellabeat is a high-tech manufacturer of beautifully-designed health-focused smart products for women since 2013. Inspiring and empowering women with knowledge about their own health and habits, Bellabeat has grown rapidly and quickly positioned itself as a tech-driven wellness company for females.

The co-founder and Chief Creative Officer, Urška Sršen is confident that an analysis of non-Bellebeat consumer data (ie. FitBit fitness tracker usage data) would reveal more opportunities for growth.

Business Task:

Analyze FitBit Fitness Tracker App data to gain insights into how consumers are using the FitBit app and discover trends and insights for Bellabeat marketing team.

Business Objectives:

• What are the trends identified?
• How could these trends apply to Bellabeat customers?
• How could these trends help influence Bellabeat marketing strategy?

Python for Data Cleaning, Data Transformation, Data Visualisation and Data Analysis

The data set is publicly available on Kaggle .

Medium: Google Capstone Project: How Can Bellabeat, A Wellness Technology Company Play It Smart?

Kaggle: Bellabeat Case Study

• Jupyter Notebook 100.0%