google data analytics capstone project solution

• 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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Ultimate Google Data Analytics Capstone: Complete a Case Study Guide 2024

What is the Google Data Analytics Capstone?

The Google Data Analytics Capstone is a comprehensive project that allows learners to apply the skills and knowledge they have gained throughout the Google Data Analytics Professional Certificate program. It serves as the final step in completing the certificate and showcases the learner's ability to analyze real-world data and provide valuable insights.

How to complete the Google Data Analytics Capstone successfully?

To successfully complete the Google Data Analytics Capstone, learners need to follow a structured approach. Here are the steps:

1. Understand the problem: Begin by thoroughly understanding the problem statement provided for the capstone project . This will help you identify the key objectives and requirements of the project.

2. Gather and assess the data: Collect the relevant data required for the analysis. Ensure that the data is clean, accurate, and complete. Perform an initial assessment of the data to identify any issues or anomalies that may impact the analysis.

3. Explore the data: Dive deep into the data to gain insights and identify patterns. Use various data exploration techniques such as data visualization , statistical analysis, and data profiling to understand the data better.

4. Clean and transform the data: Cleanse the data by addressing any missing values, outliers, or inconsistencies. Transform the data into a format suitable for analysis, ensuring that it is structured and organized.

5. Analyze the data: Apply appropriate analytical techniques to extract meaningful insights from the data. Use statistical methods, data modeling, and machine learning algorithms to uncover patterns, trends, and relationships.

6. Interpret the results: Once the analysis is complete, interpret the findings and draw conclusions. Clearly communicate the insights and recommendations derived from the analysis, ensuring they align with the project objectives.

7. Create a compelling presentation: Present the analysis and findings in a visually appealing and concise manner. Use data visualization tools and storytelling techniques to effectively communicate the results to stakeholders.

8. Review and refine: Review your work, ensuring that it meets all the requirements and objectives of the capstone project. Make any necessary refinements or improvements to enhance the quality and accuracy of your analysis.

9. Submit the capstone project: Finally, submit your completed capstone project for evaluation. Ensure that you meet all the submission requirements and deadlines specified by the course instructors.

Why is the Google Data Analytics Capstone important?

The Google Data Analytics Capstone is an essential component of the Google Data Analytics Professional Certificate program. It provides learners with a hands-on opportunity to apply their skills and knowledge to real-world scenarios, preparing them for careers in data analytics

What are the benefits of completing the Google Data Analytics Capstone?

Completing the Google Data Analytics Capstone offers several benefits:

1. Practical experience: The capstone project allows learners to gain practical experience in analyzing real-world data. This experience is highly valuable when seeking employment in the data analytics field.

2. Portfolio showcase: The capstone project serves as a showcase of the learner's skills and abilities. It can be included in their portfolio and presented to potential employers as evidence of their proficiency in data analytics.

3. Problem-solving skills: The capstone project challenges learners to solve complex problems using data analytics techniques . This enhances their problem-solving skills and ability to think critically.

4. Collaboration and teamwork: In many cases, the capstone project is completed in teams, fostering collaboration and teamwork. This simulates real-world work environments and prepares learners for collaborative projects in their careers.

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5. Industry recognition: The Google Data Analytics Capstone is recognized by industry professionals and employers. Completing the capstone project demonstrates a commitment to professional development and can enhance job prospects

How to choose a case study for the Google Data Analytics Capstone?

Choosing a suitable case study for the Google Data Analytics Capstone is crucial for a successful project. Here are some tips to help you select an appropriate case study:

1. Relevance:

Choose a case study that is relevant to your career goals or interests. This will keep you motivated throughout the project and allow you to apply your skills in a meaningful context.

2. Availability of data:

Ensure that the case study has sufficient data available for analysis. Without adequate data, it will be challenging to perform a comprehensive analysis and derive meaningful insights.

3. Complexity:

Select a case study that offers a reasonable level of complexity. It should provide enough challenges to demonstrate your skills but not be too overwhelming that it becomes unmanageable within the given timeframe.

4. Feasibility:

Consider the feasibility of the case study in terms of resources, time, and expertise required. Choose a case study that you can realistically complete within the given constraints.

Look for a case study that has the potential to make a significant impact or provide valuable insights. This will add value to your analysis and showcase your ability to generate actionable recommendations.

What are the key components of a Google Data Analytics Capstone project?

A Google Data Analytics Capstone project typically consists of several key components:

1. Problem statement:

The problem statement defines the objective and scope of the capstone project. It outlines the specific questions or challenges that need to be addressed through data analysis

2. Data collection and assessment:

This component involves gathering the relevant data required for the analysis. The data should be assessed for quality, accuracy, and completeness to ensure its suitability for analysis.

3. Data exploration:

Data exploration involves examining the data to gain insights and identify patterns. Various techniques such as data visualization, statistical analysis, and data profiling are used to understand the data better.

4. Data cleaning and transformation:

Data cleaning and transformation involve addressing any issues or anomalies in the data. This includes handling missing values, outliers, and inconsistencies. The data is then transformed into a format suitable for analysis.

5. Data analysis:

Data analysis is the core component of the capstone project. It involves applying appropriate analytical techniques to extract meaningful insights from the data. Statistical methods, data modeling, and machine learning algorithms may be used to uncover patterns, trends, and relationships.

6. Results interpretation:

Once the analysis is complete, the results need to be interpreted and translated into actionable insights. This component involves drawing conclusions from the analysis and making recommendations based on the findings.

7. Presentation and communication:

The final component of the capstone project is presenting the analysis and findings in a clear and concise manner. Data visualization tools and storytelling techniques are used to effectively communicate the results to stakeholders.

What are some tips for successfully completing the Google Data Analytics Capstone?

Completing the Google Data Analytics Capstone can be a challenging task. Here are some tips to help you successfully complete the project:

1. Plan your time:

Break down the project into smaller tasks and create a timeline for completion. Allocate sufficient time for each task, considering the complexity and scope of the project.

2. Stay organized:

Keep your data, analysis, and documentation well-organized throughout the project. This will make it easier to track your progress and ensure that you have all the necessary information at hand.

3. Seek guidance:

If you encounter any challenges or have questions, don't hesitate to seek guidance from instructors or fellow learners. They can provide valuable insights and help you overcome any obstacles you may face.

4. Use appropriate tools:

Make use of relevant data analytics tools and software to streamline your analysis. Familiarize yourself with tools such as SQL, Python, R, or Excel, depending on the requirements of your project.

5. Validate your findings:

Ensure that your analysis is accurate and reliable by validating your findings. Double-check your calculations, perform sensitivity analyses, and consider alternative explanations for your results.

6. Communicate effectively:

Pay attention to how you present your analysis and findings. Use clear and concise language, and support your conclusions with visualizations and evidence. Tailor your communication style to the intended audience.

7. Iterate and refine:

Don't be afraid to iterate and refine your analysis as you progress. Continuously review and improve your work to enhance the quality and accuracy of your findings.

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8. Practice storytelling:

Develop your storytelling skills to effectively communicate the narrative behind your analysis. Use data visualizations, narratives, and compelling storytelling techniques to engage your audience and convey your insights.

What are some examples of Google Data Analytics Capstone projects?

The Google Data Analytics Capstone projects cover a wide range of industries and topics. Here are some examples of capstone projects:

1. E-commerce sales analysis:

Analyze sales data from an e-commerce company to identify trends, customer preferences, and opportunities for revenue growth. Provide recommendations on pricing strategies, product promotions, and customer retention

2. Social media sentiment analysis:

Analyze social media data to understand customer sentiment towards a brand or product. Identify key themes, sentiment trends, and influencers. Provide recommendations on improving brand perception and engagement.

3. Supply chain optimization:

Analyze supply chain data to identify bottlenecks, optimize inventory levels, and improve delivery efficiency. Provide recommendations on streamlining processes, reducing costs, and enhancing customer satisfaction

4. Customer segmentation and targeting:

Analyze customer data to segment the customer base and develop targeted marketing strategies . Identify customer segments based on demographics, behavior, and preferences. Provide recommendations on personalized marketing campaigns and customer retention.

5. Fraud detection and prevention:

Analyze transactional data to detect patterns indicative of fraudulent activities. Develop models and algorithms to identify potential fraud cases and prevent financial losses. Provide recommendations on strengthening fraud prevention measures.

How does the Google Data Analytics Capstone contribute to career development?

The Google Data Analytics Capstone plays a significant role in career development for aspiring data analysts . Here's how it contributes:

1. Practical experience:

The capstone project provides learners with hands-on experience in analyzing real-world data. This practical experience is highly valuable when applying for data analytics roles

2. Portfolio enhancement:

Completing a capstone project adds a valuable asset to your professional portfolio. It showcases your ability to apply data analytics techniques to solve complex problems and provides evidence of your skills to potential employers.

3. Skill validation:

The capstone project validates your skills and knowledge in data analytics. It demonstrates your ability to gather, clean, analyze, and interpret data, which are essential skills for a career in data analytics.

4. Networking opportunities:

During the capstone project, you may have the opportunity to collaborate with other learners or industry professionals. This allows you to expand your professional network and potentially open doors to future career opportunities

5. Industry recognition:

Completing the Google Data Analytics Capstone demonstrates your commitment to professional development and continuous learning. It is recognized by industry professionals and employers, enhancing your credibility and job prospects.

The Google Data Analytics Capstone is a crucial component of the Google Data Analytics Professional Certificate program. It allows learners to apply their skills and knowledge to real-world scenarios, showcasing their ability to analyze data and provide valuable insights. By following a structured approach, choosing a suitable case study, and effectively completing the key components of the capstone project, learners can successfully complete the capstone and enhance their career prospects in the field of data analytics.

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The Google Data Analytics Capstone is a project-based course offered by Google that allows learners to apply their data analytics skills to a real-world case study.

How do I complete a case study for the Google Data Analytics Capstone?

To complete a case study for the Google Data Analytics Capstone, you will need to analyze a provided dataset, identify key insights, and present your findings in a clear and concise manner.

What are the benefits of completing a case study for the Google Data Analytics Capstone?

Completing a case study for the Google Data Analytics Capstone allows learners to demonstrate their data analytics skills, gain practical experience in analyzing real-world data, and showcase their abilities to potential employers.

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Asim is the CEO & founder of AtOnce. After 5 years of marketing & customer service experience, he's now using Artificial Intelligence to save people time.

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Google Advanced Data Analytics Capstone

This course is part of Google Advanced Data Analytics Professional Certificate

Taught in English

Instructor: Google Career Certificates

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(509 reviews)

What you'll learn

Examine data to identify patterns and trends

Build models using machine learning techniques

Create data visualizations

Explore career resources

Skills you'll gain

• Data Analysis
• Python Programming
• Machine Learning
• Technical Interview Preparation
• Executive Summaries

Details to know

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Build your Data Analysis expertise

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Earn a career certificate

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Share it on social media and in your performance review

There are 3 modules in this course

You’re almost there! This is the seventh and final course of the Google Advanced Data Analytics Certificate. In this course, you have the opportunity to complete an optional capstone project that includes key concepts from each of the six preceding courses. During this capstone project, you'll use your new skills and knowledge to develop data-driven insights for a specific business problem.

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Create and/or update your resume -Create and/or update your professional portfolio -Develop a data frame -Compose data visualizations -Use statistics to analyze and interpret data -Build, interpret, and evaluate regression models -Utilize machine learning techniques in Python

Capstone Project

To start, you’ll be provided with an overview of the optional capstone project and how it differs from the end-of-course projects. You’ll also receive helpful suggestions for successfully completing the capstone project. Finally, you'll learn how to incorporate your completed capstone project into your professional portfolio.

What's included

3 videos 5 readings 3 quizzes 1 discussion prompt 2 ungraded labs

3 videos • Total 4 minutes

• Introduction to Course 7 • 1 minute • Preview module
• What to expect in the capstone project • 1 minute
• Capstone project wrap-up and tips for ongoing career success • 1 minute

5 readings • Total 90 minutes

• Helpful resources and tips • 20 minutes
• Course 7 overview • 20 minutes
• Introduction to the Course 7 capstone project • 20 minutes
• Activity Exemplar: Course 7 capstone project exemplar • 10 minutes
• Incorporate your project into a portfolio • 20 minutes

3 quizzes • Total 105 minutes

• Did you complete the capstone project? • 5 minutes
• Create your Course 7 capstone project • 30 minutes
• Assess your Course 7 capstone project • 70 minutes

1 discussion prompt • Total 10 minutes

• Reflect on your capstone project • 10 minutes

2 ungraded labs • Total 80 minutes

• Activity: Course 7 Salifort Motors project lab • 60 minutes
• Exemplar: Course 7 Salifort Motors project lab • 20 minutes

Data-focused career resources

You’ll review data-focused career resources designed to help you effectively navigate the job market. You'll also get useful tips for polishing your resume and preparing for interviews.

10 videos 7 readings 4 quizzes

10 videos • Total 38 minutes

• The data professional hiring process • 3 minutes • Preview module
• Incorporate project work in your data analytics portfolio • 2 minutes
• Refine your resume • 3 minutes
• Daniel: Create a compelling resume • 2 minutes
• Prepare for interviews • 4 minutes
• Eva: Data careers can start anywhere • 2 minutes
• Why an online presence is important • 3 minutes
• Tips for enhancing your online presence • 3 minutes
• Create a compelling resume • 9 minutes
• Make your resume unique • 3 minutes

7 readings • Total 100 minutes

• Communicate your skills and assets • 10 minutes
• Resume writers’ workshop • 20 minutes
• Activity Exemplar: Optimize your resume • 10 minutes
• Proactive approaches to the interview process • 20 minutes
• Prepare for interviews with Interview Warmup • 10 minutes
• Get started with LinkedIn • 20 minutes
• Build connections on LinkedIn • 10 minutes

4 quizzes • Total 102 minutes

• Activity: Optimize your resume • 30 minutes
• Test your knowledge: Resumes to launch your future in a data career • 6 minutes
• Test your knowledge: Interview success • 6 minutes
• Activity: Build a resume • 60 minutes

Put your Advanced Data Analytics Certificate to work

You’ll complete the final tasks necessary to earn your Google Advanced Data Analytics Certificate badge. Congratulations!

2 videos 3 readings 1 quiz 1 plugin

2 videos • Total 5 minutes

• Congratulations on completing the Google Advanced Data Analytics Certificate! • 2 minutes • Preview module
• Exploring professional opportunities • 3 minutes

3 readings • Total 30 minutes

• Showcase your work • 10 minutes
• Claim your Google Advanced Data Analytics Certificate badge • 10 minutes
• Advanced Data Analytics Certificate glossary • 10 minutes

1 quiz • Total 2 minutes

• End-of-certificate checklist • 2 minutes

1 plugin • Total 10 minutes

• End of course survey • 10 minutes

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Reviewed on Dec 20, 2023

The Course was very effective which increased my skills, knowledge and confidence level.

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excelent course for developing skills on EDA process and good start on creating machine learning models that fits the data.

Reviewed on Sep 14, 2023

I learned a great deal. This is a well-designed online course. I'd highly recommend it to others seeking an entry point into the data realm.

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Frequently asked questions

What is data science and advanced data analytics.

Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace. 

Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.

What do data professionals do?

A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models. 

Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.

Why start a career in data science or advanced data analytics?

Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.

Which jobs will this certificate help me prepare for?

The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.

What tools and platforms are taught in the curriculum?

During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.

What background is required?

This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders. 

The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate Opens in a new tab . These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.

Why enroll in the Google Advanced Data Analytics Certificate?

You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analyti

Do I need to take the course in a certain order?

We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.

When will I have access to the lectures and assignments?

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

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Lindner College of Business » Programs » Graduate Programs & Certificates » Specialized Master's Programs » MS in Business Analytics

Master of Science in Business Analytics

The University of Cincinnati’s Master of Science in Business Analytics program is nationally recognized and has a proven track record with placing students at successful, high-profile companies. Predictive Analytics Today named UC as the No. 1 MS Data Science school in the country four successive years. Financial aid is available in the form of scholarships and assistantships and is awarded on a competitive basis.

Lindner's Master of Science in Business Analytics program provides you with expertise in descriptive, predictive and prescriptive analytics. This program begins in August and classes are offered full- or part-time. Full-time students typically complete the program in nine months or one year. Classes are available in the late afternoon, evening and Saturday for part-time students.

How long does it take to complete the Master of Science in Business Analytics program?

The program can be completed in two semesters but many students use a third semester to complete the capstone project.

Is the Master of Science in Business Analytics a designated STEM Program?

Yes, the Master of Science in Business Analytics program has been officially designated as a STEM (Science, Technology, Engineering, and Mathematics) program.

When is the application deadline?

Please visit the  program's admission page  for deadlines.

Why earn a Business Analytics master’s degree?

Businesses across the country and world are looking for business analysts with at least a master’s degree. A Master of Science not only puts you ahead of the competition, but it also provides the skills needed to successfully impact business decisions. At the University of Cincinnati, we feature on-campus employer seminars and recruiting. Each week, major employers seeking analysts and technical talent present seminars, recruit, or interview MS Business Analytics students. Some companies include Tata Consultancy Services, Great American Insurance, GE Digital, Teradata, PWC, DMI, 84.51, and Fifth Third Bank.

Our convenient class options allow students to earn a life-changing degree without drastically changing their schedule. Full-time students can earn their degree in a year or less, allowing them to apply their skills in a real-world setting in just a couple of semesters. Recent graduates earn an average starting salary of more than $115,000.

Another benefit of our MS program is the corporate and academic partnership with the Center for Business Analytics which provides real-world data analytics experience and career opportunities for students and promotes industry leadership and research to advance the field of business analytics.

Below is a select list of employers who have recently Lindner Master of Science in Business Analytics graduates:

• Barclays PLC
• Capital One Financial Corporation
• Cincinnati Bell Inc.
• Cincinnati Children's Hospital
• Fifth Third Bank
• IBM Corporation
• Johnson & Johnson, Inc.
• JP Morgan Chase
• Macy's, Inc.
• Procter & Gamble
• The Walt Disney Company

Paul Bessire

Employer: PredictionMachine.com Title: Co-founder

Paul is one of the foremost authorities on mathematically modeling and analyzing sports. He has turned his longtime hobby of predicting and writing about sports outcomes into a full-time profession.

Today Paul is an adjunct professor at the University of Cincinnati where he teaches “Bracketology” with Mike Magazine, a guest lecturer for “Sports by the Numbers,” and a frequent guest analyst on fantasy sports and talk shows across the country on networks such as ESPN and CBS.

"The Business Analytics program at the University of Cincinnati is not just a world-class analytics program. It’s a world-class program at the intersection of big data, technology, and efficient decision-making. The applications of the program should continue to grow in importance within businesses in all industries.”

Employer: Cincinnati Reds Title: Business Analytics Developer

Mark leads data visualization development for the Cincinnati Reds using Tableau software. Mark has built a number of meaningful dashboards for the Reds, some of which incorporate a map he created to plot all seats within the ballpark. The granular detail of his map allows leadership to view data a number of ways, including visualizing where certain types of ticket packages are selling, detecting notable pricing differences between the box office and secondary market, and identifying potential sponsorship opportunities.

"Cincinnati's program is second to none in its ability to prepare students for careers in a wide variety of analytical fields. While offering a strong mathematical foundation via exposure to large datasets and the opportunity to solve real-world problems, its focus on logical reasoning and adaptive problem-solving skills result in an overarching strength, preparing you for any scenario you may face in the workplace."

Liberty Holt

Employer: TriHealth Title: Manager of Predictive Analytics

After graduation from the University of Cincinnati business analytics program, Liberty accepted a position as a data scientist at TriHealth in collaboration with Hatton Research where she was responsible for developing and implementing the use of predictive models. Liberty was soon promoted to manager of predictive analytics, where she is able to mentor others and influence the future direction of analytics at TriHealth. She has the opportunity to explore cutting edge analytics  using machine learning in combination with Watson Content Analytics, Hadoop, and other developing technologies. She is also developing a team to ensure that any models or analytics that are developed translate into opportunities to give the best care to the patients of her organization.

“I will forever be grateful for the opportunity to complete the UC analytics program. I gained skills and learned a vast array of analytic techniques, but beyond that it also offered many opportunities to grow both professionally and personally.”

The University of Cincinnati Master of Science in Business Analytics program is designed to provide you with a strong foundation in all areas of business analytics while allowing you flexibility to tailor the program based on your individual interests and career plans. Our program is nationally ranked yet competitively-priced and our business analytics faculty is comprised of both world-class instructors who conduct advanced analytics research and real-world industry practitioners.

MATH 1061: Calculus I, MATH 1062: Calculus II, and MATH 2063: Multivariable Calculus These courses are not the applied courses typically taken by business or social science majors. Topics of coverage should include: functions; limits and continuity; derivatives; applications of the derivative; the integral and applications; exponential and logarithmic functions; inverse functions; techniques of integration; polar coordinates; conic sections; Taylor's formula; improper integrals; sequences and series; vectors; lines; planes; vector-valued functions; partial derivatives; multiple integrals; calculus of vector fields.

MATH 2076: Linear Algebra Topics should include: systems of linear equations; matrices; vector spaces; bases and dimension; orthogonality; linear transformations; determinants; eigenvalues and eigenvectors; diagonalization.

Fundamental Computing Knowledge This includes facility in a procedural programming language like Ruby, Python, C, C++, Matlab, Java, Visual Basic, Pascal, or FORTRAN. It is also assumed that a student is already comfortable with spreadsheets, word processing, e-mail, web browsers, etc.

Core courses

BANA 6037: Data Visualization This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.

BANA 7020: Optimization An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and nonlinear optimization applications. Solution techniques and analyses covered include graphical approaches, the simplex method, duality, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and Newton’s method and gradient search for nonlinear optimization.

BANA 7025: Data Wrangling This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

BANA 7030: Simulation Modeling and Methods Building and using simulation models of complex static and dynamic, stochastic systems using both spreadsheets and high-level simulation software. Topics include generating random numbers, random variates, and random processes, modeling systems, simulating static models in spreadsheets, modeling complex dynamic stochastic systems with high-level commercial simulation software, basic input modeling and statistical analysis of terminating and steady-state simulation output, and managing simulation projects. Applications in complex queueing and inventory models representing real systems such as manufacturing, supply chains, healthcare, and service operations.

BANA 7031: Probability Modeling PROBABILITY MODELS: Events, probability spaces and probability functions; Random variables; Distribution and density functions; Joint distributions; Moments of random variables; Special expectations; Moment generating functions; Conditional probability and conditional moments; Probability inequalities; Independence; Special probability distributions including: binomial, negative binomial, multinomial, Poisson, gamma, chisquare, normal, beta, t, F, mixture distributions, multivariate normal; Distribution of functions of random variables; Order statistics; Asymptotic results including: convergence in distribution, central limit theorem, convergence in probability, Slutsky's theorem. STOCHASTIC MODELS: Discrete time Markov processes, Markov pure jump processes, Birth and death processes, Branching processes, Poisson process, Pure birth processes, Yule process; applications in several areas, e.g. queuing models, machine repair models, inventory models, etc.

BANA 7042: Statistical Modeling Nonlinear regression and generalized linear model. Logistic regression for dichotomous and polytomous responses with a variety of links. Count data regression including Poisson and negative binomial regression. Variable selection methods. Graphical and analytic diagnostic procedures. Overdispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models.

BANA 7046 Data Mining I This is a course in the statistical data mining with emphasis on hands-on case study experience using various data mining/machine learning methods and major software to analyze complex real world data. Topics include: Data Preprocessing. k-Nearest Neighbors. Linear Regression and Generalized Linear Regression. Subset and LASSO Variable Selection. Model Evaluation. Cross Validation. Classification and Regression Trees.

BANA 7047: Data Mining II This is a course in the statistical data mining with emphasis on hands-on case study experience using various data mining/machine learning methods and major software to analyze complex real world data. Topics include: Advanced Trees: Bagging, Random Forests, Boosting. Nonparametric Smoothing methods. Generalized Additive Models. Data Preprocessing/Scaling. Neural Networks. Deep Learning. Cluster Analysis. Association Rules.

BANA 7051: Applied Statistical Methods This course covers applied statistical methods, including topics of frequency distributions, estimation, hypothesis testing, point and interval estimation for mean and proportion; comparison of two populations; goodness of fit tests, one factor ANOVA. Major statistical software is used.

BANA 7052: Applied Linear Regression This course covers applied linear regression, including topics of fitting and drawing inferences from simple and multiple linear regression models; residual diagnostics; model correction procedure for linear regression; variable selection. Major statistical software is used.

BANA 8083: Capstone This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in an essay that is reviewed and approved by two faculty members. The essay can describe: (1) a research project based onan idea proposed independently by the student or with faculty input; (2) an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. The essay must describe the student's contribution to the research or case.

IS 6030: Data Management This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.

BA 7077: Career Management All full-time Lindner graduate students are required to register for the course BA7077 Graduate Career Management. This course includes both in-class meetings and deliverables such as resume revision, LinkedIn profiles, mock-interviews, etc. These will be graded on a Pass/Fail basis. This course provides Lindner College of Business graduate students with an advanced set of necessary skills and tools for continuous professional development and/or conducting a strategic job search in his/her field of choice.

BANA electives

BANA 6043: Statistical Computing This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

BANA 6044: Applications Development using VBA The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment. See your college advisor for prerequisite details.

BANA 7048: Multivariate Statistical Methods This is a course in the analysis multivariate data with emphasis on appropriate choice of estimation and testing methods. Vectors and matrices, Multivariate probability distributions and their parameter, Multivariate normal distributions, Maximization and minimization of multivariate functions, The "shape" of multivariate normal data, Correlation, prediction and regression, Sample statistics and their sampling distributions for multivariate normal data; Estimation and tests for correlation, Tests of independence, Estimation and tests for multivariate means and covariance matrices, ower of multivariate tests, multivariate linear models, canonical correlation analysis, Principal components analysis, Factor analysis, Classification and discrimination analysis.

BANA 7050: Forecasting and Time Series Methods This is a course in the analysis of time series data with emphasis on appropriate choice of forecasting, estimation, and testing methods. Univariate Box-Jenkins methodology for fitting and forecasting time series. ARIMA models, Stationarity non-Stationarity, auto-correlation functions, partial and inverse autocorrelation functions, Estimation and model fitting, Diagnosing time series models, Forecasting: Point and interval forecasts, Seasonal time series models,Transfer function models, Intervention models, Modeling volatility with ARCH, GARCH, and other methods, Modeling time series with trends, Multiequation time series models: Vector Auto Regression (VAR), Cointegration and error correction models, Nonlinear time series models, State space time series models, Bayesian time series and forecasting.

BANA 7095: Graduate Case Studies in Business Analytics Real organizational problems or challenges will be presented to students by client companies. Students in groups will work with a client to develop a solution or solutions to the problems using advanced analytic techniques. Students will present the solutions to the client in both oral and written reports.

BANA 8090: Special Topics in Business Analytics This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.

Non-BANA elective options

ECON 8021: Micro Theory Topics

FIN 7045: Portfolio Management This course will give students an understanding of the implications of standard asset pricing models, market efficiency, optimization, asset allocation, and portfolio risk management.

IS 7012: Web Development with .Net This course is an introduction to the development of web-based applications, using Microsoft's Visual Studio and covering ASP.Net using Visual C#. Students will be expected to develop a simple web application that incorporates these technologies. Students will learn how to integrate the frontend (web site) with the back end (database) of an application. The course will cover the implementation of navigational structures, input and validation controls, and data controls in web applications.

IS 7034: Data Warehousing and Business Intelligence This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.

IS 7038: Managing Business Intelligence Projects This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.

MKT 7012: Marketing Research for Managers Explores the role of marketing research in marketing management. Involves hands-on activities to perfect understanding of methods for collecting, analyzing, and summarizing data pertinent to solving marketing problems.

OM 7061: Managing Project Operations This course covers detailed issues related to managing product development and projects in organizations. The course covers, in two separate modules:-Concepts of project planning and organization, budgeting and control, and project life cycles and concepts related to organizational workflow including the staffing process, and project planning elements; related concepts of organizational forms, conflict resolution, and issues related to leadership and task management in a project environment.-Advanced concepts of project scheduling, including WBS, CPM, PERT, simulation, project budgeting, earned value analysis, project tracking and resource constrained scheduling. This includes setting up projects on Microsoft Project and using the information for budgeting, resource management, tracking and ongoing communication and evaluation of projects.

OM 7083: Supply Chain Strategy and Analysis Presents an overview of issues relating to the design and operation of an organization's supply chain. Information is presented as a mix of technical models and applied case studies. Topics may include inventory planning, logistics, sustainability, global operations, supply chain collaboration and contracting.

Master of Science in Business Analytics program requires the completion of a capstone experience. Students will describe their capstone experience in an essay of eight to fifteen pages. The essay will be based on one of the following:

• Research Project:  The content of the essay must be substantive in terms of containing technical, quantitative modeling, analysis, or programming/coding aspects and not a survey or exposition of the work of others. The appendices of the essay may contain supporting figures and tables, computer files that contain the data used, model formulations and computer code.
• Extension of a Course Case Analysis/Project:  This essay is an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. Even if the original case analysis/project was a group effort, the essay must still be an individual effort. The content must extend the original work and a copy of the original work must also be submitted. Acceptable extensions can include the application of different modeling methodologies to the same data set to compare results or the use of additional data to generate new insights or to confirm the original findings.
• Description of an Internship Project:  This essay describes the student’s contribution to a project completed during a one or two semester internship taken as part of the student's MS-Business Analytics course work. The content must demonstrate the student’s knowledge of Business Analytics concepts and the student’s ability to implement those concepts. It is understood that for proprietary reasons, the essay may not contain the level of detail expected in a Research Project or Case Analysis essay.

Examples of past Business Analytics capstone projects are available for review.

These are typical MS Business Analytics schedules, and they assume all Basic Business Knowledge (BBK) prerequisites have been fulfilled. The program consists of 33 total credits; 25 from core BANA courses (24 credits for formal coursework, one credit for BANA 8083 capstone), and eight from electives, at least four of which must be BANA-prefixed courses at the 6000 level or above. All electives must be approved by the academic director.

Full-time study

One year program.

*BANA 8083 should be taken in the semester in which the student will graduate. BANA 8084 should be taken in place of BANA 8083 if an internship will be used for the Capstone.

Part-time study

Part-time students typically, though not always, complete the program in two years.

Michael Platt

MS BANA Academic Director, Assistant Professor-Educator, Department of Operations, Business Analytics, and Information Systems

3459 Carl H. Lindner Hall

Julie Glassmeyer

MS BANA Assistant Academic Director

1330 Carl H. Lindner Hall

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google-data-analytics

Here are 72 public repositories matching this topic..., ksgr5566 / google-data-analytics.

Notes for the Google Data Analytics course.

• Updated Dec 25, 2021

katiehuangx / Google-Data-Analytics-Capstone

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

• Updated Aug 10, 2021
• Jupyter Notebook

b06601024 / Coursera-Google-Data-Analytics

Google Data Analytics Professional Certificate

• Updated Jan 16, 2022

shivamgarg444 / Cyclistic-Case-Study

Case study under capstone project of Google Data Analytics Certificate

• Updated Jun 10, 2021

skramazan / GDA_Capstone_Project_Cyclistic_Bike-share

This analysis is for the Google Data Analytics Capstone Project (Cyclistic).

• Updated Dec 20, 2022

cosmoduende / r-google-location-history

Explore your activity on Google with R: How to analyze and visualize your Location History. Find out how and how much you have allowed Google to track you, using a copy of your personal data.

• Updated Aug 1, 2021

Gui-lherme-Oliv / Google_Data_Analytics-Estudo_de_caso_Bellabeat

Projeto final do programa Google Data Analytics, onde foi concluído um estudo de caso utilizando a linguagem R

• Updated Dec 2, 2023

krishna0306 / Udemy-Courses-Stas-2023-Prices-Data_analysis

Udemy Course Data Analysis Explore the world of online learning with the Udemy Course Data Analysis project. Dive into the depths of Udemy's vast collection of courses to uncover insights about course prices, popularity, and more. Leveraging Python and data visualization techniques, this project provides a comprehensive overview of Udemy's course o

• Updated Aug 28, 2023

marcoshsq / GoogleDataAnalyticsCapstone

This repository contains my three case studies for the Google Data Analytics Professional Certificate Capstone Project. 📈📊📉

• Updated Mar 25, 2024

sophiechin / Google-Data-Analytics-Professional-Certificate

This repository is created for the review of the Google Data Analytics Professional program. To complete the whole program, the Case Studies for the Google Data Analytics Capstone are also included.

• Updated Jul 19, 2023

DmytroNorth / Data_Driven-Marketing_Strategies-SQL

Used bike-sharing company data to design marketing strategies aimed at converting casual riders into annual members.

• Updated Jul 21, 2021

elmaandimahmoud / Google_Data_Analytics_Capstone

A case study on the cyclistic data set

• Updated Mar 5, 2022

sam-is-curious / Google-Data-Analytics-Capstone-Project-Cyclistic-Bike-Share

Exploratory Data Analysis on the fictional company, Cyclistic, a bike-share company out of Chicago. "R" was used to complete this analysis for the Google Data Analytics Professional Certification.

• Updated Oct 25, 2022

NikoSeino / Bellabeat-case-study

Google Data Analytics capstone project

• Updated May 3, 2023

srivastav-gaurav / Case-study-Google-Data-Analytics-Bike_Share

• Updated Jul 15, 2021

AnuttaraR / google-bellabeat-capstone

Hello! Welcome to my first ever data analysis project! I am completing the last course of the Google Data Analytics Cetificate and I chose to do the "Case Study 2: Can a Wellness Company Play it Smart?" which pertains to the Bellabeat Capstone.

• Updated Aug 31, 2022

quangcaophan / CYCLISTIC-BIKE-SHARE

• Updated Apr 13, 2023

morgan-turner / google-data-analytics-capstone

For my Google Data Analytics Professional Certificate capstone project, I used the tidyverse package in R to identify usage differences between members and casual riders in Cyclistic bike-share data.

• Updated May 23, 2022

AayushSameerShah / Google-Data-Analytics-Certificate

This includes my Files and Case Studies done in the Google Data Analytics certificate.

• Updated Nov 6, 2021

sunooja-suhara / capstone_bike_share

Data Analysis of a Bike Share Company using R.

• Updated Aug 8, 2022

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