capstone project data analyst

• 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.

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IBM Data Analyst Capstone Project

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• Data Collection
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• In this module, you will be focusing on the cleaning of your dataset with various techniques. With these techniques you will be identifying duplicate rows, finding missing values, and normalizing the data.
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• In module 4 of the Capstone, you will be required to create visualizations using the developer survey data. The visualizations you create should highlight the distribution of data, relationships between data, the composition of data, and comparison of data.
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• In this module, you will create a dashboard using IBM Cognos Analytics or Google Looker Studio. This platform will give you the ability to create various charts while assembling a dashboard that is appealing and easy to understand. Your dashboard will contain your data analysis, which should be intuitive and allow for the drill-down of data.
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• You have analyzed the data in the previous modules, and now it is time to demonstrate your storytelling skills. In this module, you will create a compelling story that helps to clarify your analysis in an easy-to-understand presentation.
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How to Become a Business Analyst in 2024

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Launching a career as a business analyst requires a blend of analytical skills and business acumen. The main objective of the role is to bridge the gap between business needs and technical solutions.

Business analysts are responsible for understanding the business processes, identifying areas for improvement and defining the requirements for new systems or upgrades. Specifically, this could involve creating data models, communicating with different departments about their needs, researching new technologies, and creating reports or presentations of results.

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Whether you’re researching your first job, transitioning from a different area of business or embarking on a complete career change, this guide will provide all the information you need to become a successful business analyst. It covers:

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Disclaimer: This article is sponsored by Coursera.

What does a business analyst do?

A business analyst is responsible for a number of tasks to help improve business efficiency, including:

• Conducting interviews, workshops and surveys with stakeholders to understand their requirements and goals.
• Creating documentation of business requirements and use cases.
• Using SQL and Excel to identify trends, issues and opportunities for improvement in large data sets.
• Compiling charts, tables and other elements of data visualization.
• Evaluating technical solutions to determine which one best meets the business needs.
• Forecasting, budgeting and performing variance and financial analysis to support decision making.
• Supporting staff through changes and organizing testing and quality checks after changes have been made.

SEE: 87 Excel Tips and Tricks: From Beginners to Pros

What is the average business analyst salary?

According to Glassdoor, the average annual salary of a business analyst in the U.S. is between $70,000 and $118,000, with an average additional pay, including any bonuses or profit sharing, of $36,000.

The average salary for a business analyst in the UK is between £32,000 and £54,000 a year, depending on location and level of experience, while additional pay is an average of £3,000.

What skills are required to be a business analyst?

While many business analyst roles do not require a specific set of skills, there are a number of skills that will assist greatly with day-to-day tasks or that you may be required to learn on the job. These skills include:

• Data analysis: Proficiency in analyzing datasets, identifying trends and making decisions based on the data is often required in the role.
• SQL: Structured Query Language is used for data extraction and manipulation in databases. Its use can significantly speed up the process of data analysis.
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• Data visualization: Tools like Power BI and Tableau are used to create interactive and visually appealing data visualizations, making it easier to interpret complex data sets.
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• Business acumen: A solid understanding of business operations, strategies and financial principles will help an analyst make more suitable decisions.
• Communication: Communication skills, both written and verbal, are key for business analysts for clearly conveying ideas and findings to stakeholders.
• Project coordination: Proficiency in this area comes from experience assisting in the planning, execution and monitoring of projects, as well as innate organizational skills.

Be sure to highlight these skills or experiences where you have demonstrated them on your résumé or CV when applying for your first business analyst role.

SEE: How to Query Multiple Tables in SQL

How do you launch a career as a business analyst?

As the role of business analyst is so varied, so are the routes into it. There is no set formula to landing that first job; however, there are a number of actions that will take you in the right direction.

Earn a degree

Hiring managers will typically consider a wide range of degree types when assessing candidates for a business analyst role, as there are many transferable skills that can be picked up in different fields of study.

An undergraduate degree in a business-related field (e.g., accounting, business information systems, business studies, economics or finance) will likely be the most relevant and also show that you are interested in the industry.

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Obtain work experience

Work experience is another great way of earning the skills necessary to get your first job as a business analyst and demonstrate your interest in the role. It also allows you to assess whether the job is right for you. This work experience can be obtained through a short-term placement, internship or apprenticeship.

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• Glassdoor .
• ACCA jobs .

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• Find an apprenticeship service . The government also offers a business analyst apprenticeship program .
• Gradcracker .
• Amazon Business Analyst apprenticeship or internship .
• QA Business Analyst apprenticeship .
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• McKinsey & Company Business Analyst internship .

Take a course or certification

There are numerous courses available that are either specific to business analysts or will provide you with some of the necessary skills to break into the industry.

Professional qualifications are offered by the International Institute of Business Analysis and range from entry level to ones that require a number of years of experience as a business analyst. Such courses also involve quite significant financial contributions. For example, the exam alone for the IIBA Entry Certificate in Business Analysis is currently available for $350.

While expensive, having a recognised certification can make your application stand out and show the hiring manager that you have the skills, knowledge and passion necessary to do a good job. However, there are a number of other relevant qualifications that can be taken online, such as those offered by Coursera , for a much lower price or for free.

SEE: The All-Inclusive Business Analytics Certification Training Bundle

What are the best courses for aspiring business analysts?

Microsoft business analyst professional certificate — coursera.

The Microsoft Business Analyst Professional Certificate course prepares aspiring business analysts with the skills necessary for an entry-level position. Modules cover techniques for identifying business problems, using Microsoft Excel for data analysis and visualization, modeling business processes and data and communicating findings to stakeholders.

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Three months at 10 hours a week.

Business Analytics with Excel: Elementary to Advanced — Coursera

The Business Analytics with Excel course focuses on the essential skills for data analysis using Microsoft Excel. It covers basic to advanced Excel functions, data management techniques and descriptive statistics. It introduces learners to linear and integer optimization, decision analysis and risk modeling — all of which are common analytical frameworks used for business decision making.

Modules are structured with videos, practical exercises and quizzes to ensure the skills can be applied in real-world business cases. The course was prepared and is taught by academics from Johns Hopkins University, including associate mathematics professor Dr. Joseph Cutrone. Like the Microsoft course, this can be completed from home and at one’s own pace.

$49 USD or £39 for a shareable certificate, or free for just the course materials.

Three weeks at seven hours a week.

Entry Certificate in Business Analysis — IIBA

The ECBA is a well-recognized certificate from the International Institute of Business Analysis designed specifically for individuals new to business analysis. It covers key knowledge areas from the BABOK Guide — the globally recognised standard for the practice of business analysis — including business analysis planning, requirements life cycle management, strategy analysis and more.

Gaining full certification involves completing at least 21 hours of professional development and passing a 50-question exam. This course requires a more significant financial commitment than those on Coursera.

The certification exam costs $350 (£275), but the cost for the learning materials depends on their format (in-person course, guide book, etc). A two-day online course from The Knowledge Academy is currently priced at $2,523 (£1,995).

This is dependent on the resources and the learner themselves, but 4–6 weeks of self study is recommended.

Professional Certificate in Agile Business Analysis — BCS (UK only)

Agile methodology is a project management approach that breaks a large project down into smaller incremental steps known as sprints. This certification from the British Computer Society teaches the skills to effectively perform business analysis in agile environments. It covers agile methods in general before looking at specific agile analysis, modeling and documentation techniques. Learners will also gain an understanding of how to analyze business goals by breaking them into sprints and how to evaluate whether agile methods are appropriate for the situation.

This course requires a more significant financial commitment than those on Coursera.

It depends on the practitioner, but prices tend to range from £1,300 to £2,500.

Three days, before a 90-minute exam.

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IBM Data Analyst Professional Capstone project

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Analysis Dashboard Link: 🔗

Project Presentaion Link: 🔗

In this project, assuming myself as the Data Analyst for the company, I undertook the tasks of collecting data from multiple sources, performing exploratory data analysis, data wrangling and preparation, statistical analysis and mining the data, creating charts and plots to visualize data, and building an interactive dashboard using IBM Cognos Analytics.

Key requirements of analysis are identifying skills requirements for future, top programming languages, database skills in demand and IDEs in demand

At the end of project,I prepared a presentation of my data analysis report, with an executive summary for the various stakeholders in the organization including Human Resource and IT Head.

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