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Google Data Analytics Capstone: Complete a Case Study

This course is part of Google Data Analytics Professional Certificate

Taught in English

Some content may not be translated

Instructor: Google Career Certificates

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Sponsored by Google

448,102 already enrolled

(13,901 reviews)

Recommended experience

Beginner level

  No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.

What you'll learn

Differentiate between a capstone project, case study, and a portfolio.

Identify the key features and attributes of a completed case study.

Apply the practices and procedures associated with the data analysis process to a given set of data.

Discuss the use of case studies/portfolios when communicating with recruiters and potential employers.

Details to know

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Build your Data Analysis expertise

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate from Google

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

This course is the eighth and final course in the Google Data Analytics Certificate. You’ll have the opportunity to complete a case study, which will help prepare you for your data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn about useful job hunting skills, common interview questions and responses, and materials to build a portfolio online. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Learn the benefits and uses of case studies and portfolios in the job search. - Explore real world job interview scenarios and common interview questions. - Discover how case studies can be a part of the job interview process. - Examine and consider different case study scenarios. - Have the chance to complete your own case study for your portfolio.

Learn about capstone basics

A capstone is a crowning achievement. In this part of the course, you’ll be introduced to capstone projects, case studies, and portfolios, and will learn how they help employers better understand your skills and capabilities. You’ll also have an opportunity to explore the online portfolios of real data analysts.

What's included

3 videos 5 readings 1 quiz 1 discussion prompt 1 plugin

3 videos • Total 14 minutes

• Introducing the capstone project • 4 minutes • Preview module
• Rishie: What employers look for in data analysts • 2 minutes
• Best-in-class • 7 minutes

5 readings • Total 100 minutes

• Course 8 overview: Set your expectations • 20 minutes
• Explore portfolios • 20 minutes
• Your portfolio and case study checklist • 20 minutes
• Revisit career paths in data • 20 minutes
• Next steps • 20 minutes

1 quiz • Total 20 minutes

• Data journal: Prepare for your project • 20 minutes

1 discussion prompt • Total 10 minutes

• Introduce yourself • 10 minutes

1 plugin • Total 10 minutes

• Refresher: Your Google Data Analytics Certificate roadmap • 10 minutes

Optional: Build your portfolio

In this part of the course, you’ll review two possible tracks to complete your case study. You can use a dataset from one of the business cases provided or search for a public dataset to develop a business case for an area of personal interest. In addition, you'll be introduced to several platforms for hosting your completed case study.

3 videos 9 readings 1 quiz 4 discussion prompts 1 plugin

3 videos • Total 7 minutes

• Get started with your case study • 3 minutes • Preview module
• Unlimited potential with analytics case studies • 1 minute
• Share your portfolio • 2 minutes

9 readings • Total 150 minutes

• Introduction to building your portfolio • 10 minutes
• Choose your case study track • 20 minutes
• Track A details • 10 minutes
• Case Study 1: How does a bike-share navigate speedy success? • 20 minutes
• Case Study 2: How can a wellness company play it smart? • 20 minutes
• Track B details • 10 minutes
• Case Study 3: Follow your own case study path • 20 minutes
• Resources to explore other case studies • 20 minutes
• Create your online portfolio • 20 minutes

1 quiz • Total 60 minutes

• Hands-On Activity: Add your portfolio to Kaggle • 60 minutes

4 discussion prompts • Total 40 minutes

• Case Study 1: How does a bike-share navigate speedy success? • 10 minutes
• Case Study 2: How can a wellness company play it smart? • 10 minutes
• Case Study 3: Follow your own case study path • 10 minutes
• Optional: Share your portfolio with others • 10 minutes
• Capstone roadmap • 10 minutes

Optional: Use your portfolio

Your portfolio is meant to be seen and explored. In this part of the course, you’ll learn how to discuss your portfolio and highlight specific skills in interview scenarios. You’ll also create and practice an elevator pitch for your case study. Finally, you’ll discover how to position yourself as a top applicant for data analyst jobs with useful and practical interview tips.

6 videos 7 readings 1 quiz

6 videos • Total 27 minutes

• Discussing your portfolio • 4 minutes • Preview module
• Scenario video: Introductions • 7 minutes
• Scenario video: Case study • 5 minutes
• Scenario video: Problem-solving • 3 minutes
• Scenario video: Negotiating terms • 3 minutes
• Nathan: VetNet and giving advice to vets • 3 minutes

7 readings • Total 110 minutes

• Introduction to sharing your work • 10 minutes
• The interview process • 20 minutes
• Scenario video series introduction • 20 minutes
• What makes a great pitch • 10 minutes
• Top tips for interview success • 10 minutes
• Prepare for interviews with Interview Warmup • 20 minutes
• Negotiate your contract • 20 minutes
• Self-Reflection: Polish your portfolio • 20 minutes

Put your certificate to work

Earning your Google Data Analytics Certificate is a badge of honor. It's also a real badge. In this part of the course, you'll learn how to claim your certificate badge and display it in your LinkedIn profile. You'll also be introduced to job search benefits that you can claim as a certificate holder, including access to the Big Interview platform and Byteboard interviews.

3 videos 4 readings 2 quizzes 1 discussion prompt 1 plugin

3 videos • Total 5 minutes

• Congratulations on completing your Capstone Project! • 1 minute • Preview module
• From all of us ... • 1 minute
• Explore professional opportunities • 3 minutes

4 readings • Total 80 minutes

• Showcase your work • 20 minutes
• Claim your Google Data Analytics Certificate badge • 20 minutes
• Sign up to the Big Interview platform • 20 minutes
• Expand your data career expertise • 20 minutes

2 quizzes • Total 4 minutes

• Did you complete a case study? • 2 minutes
• End-of-program checklist • 2 minutes
• Connect with Google Data Analytics Certificate graduates • 10 minutes
• End-of-program survey • 10 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

Grow with Google is an initiative that draws on Google's decades-long history of building products, platforms, and services that help people and businesses grow. We aim to help everyone – those who make up the workforce of today and the students who will drive the workforce of tomorrow – access the best of Google’s training and tools to grow their skills, careers, and businesses.

Why people choose Coursera for their career

Learner reviews

Showing 3 of 13901

13,901 reviews

Reviewed on Nov 10, 2022

An elevator pitch gives potential employers a quick, high-level understanding of your professional experience. What are the key considerations when creating an elevator pitch? Select all that apply.

Reviewed on Aug 12, 2022

I found a new passion in data analytics. I already signed up for a data analytics boot camp to further develop my data analytics team. Thank you to the amazing Google team that taught the courses.

Reviewed on Aug 6, 2023

I enjoyed the course. Getting to know the basics of SQL, Tableau, and R was a challenge at first but was explained in great detail and definitaly helped that it was a streamlined process.

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Final Project Presentation: Coursera Data Science Capstone

Kathy Targowski Ashenfelter

1. Introduction

1.1 assignment: final project submission.

Instructions

The goal of this exercise is to create a product to highlight the prediction algorithm that you have built and to provide an interface that can be accessed by others. For this project you must submit:

• A Shiny app that takes as input a phrase (multiple words) in a text box input and outputs a prediction of the next word.
• A slide deck consisting of no more than 5 slides created with R Studio Presenter pitching your algorithm and app as if you were presenting to your boss or an investor.

1.2 Review criteria

Data Product

• Does the link lead to a Shiny app with a text input box that is running on shinyapps.io?
• Does the app load to the point where it can accept input?
• When you type a phrase in the input box do you get a prediction of a single word after pressing submit and/or a suitable delay for the model to compute the answer?
• Put five phrases drawn from Twitter or news articles in English leaving out the last word. Did it give a prediction for every one?

1.3 Slide Deck

• Does the link lead to a 5 slide deck on R Pubs?
• Does the slide deck contain a description of the algorithm used to make the prediction?
• Does the slide deck describe the app, give instructions, and describe how it functions?
• How would you describe the experience of using this app?
• Does the app present a novel approach and/or is particularly well done?
• Would you hire this person for your own data science startup company?

2. Natural Language Processing and Predictive Text

2.1 the predictive text app.

This app features a user-friendy open text entry box. User can enter up to two characters, and the app will apply the predictice algorithm to generate a table of suggested next words.

2.1 Methods

First, I read in and sampled from the HC Corpora data and cleaned it for text mining and NLP (e.g., text must be converted to lower case, punctuation is stripped, stop and profanity words are deleted, and unreadable characters associated with numeral digits and URLs are removed) to create a usable corpora.

The tokenization algoritm was then applied to create n-gram, which are defined as all combinations of adjacent words (or letters as the case may be) of length n that exist in a dataset. 1-grams are called Unigrams, 2-grams are called bigrams, and 3-grams are called trigrams.

2.1 Methods Continued

• These n-grams are the key component in the functionality of the app; they are used to predict the next word for the user based on his or her input and to present a a frequency distribution of predicted words based on the n-gram data tables generated by the algorithm.

2.2 R Packages Utilized

This utilized the “wordcloud”, “tm”, and quanteda packages most heavily.

2.3 Session Information for Reproducibility

[1] “2018-08-06 21:05:59 EDT” setting value version R version 3.5.1 (2018-07-02) system x86_64, mingw32 ui RStudio (1.2.747) language (EN) collate English_United States.1252 tz America/New_York date 2018-08-06 sysname release version nodename “Windows” “>= 8 x64” “build 9200” “USARRKASHENFEL7” machine login user effective_user “x86-64” “kashenfelter” “kashenfelter” “kashenfelter”

3 Appendices

Source files and app location.

The app app is hosted on shinyapps.io: https://kashenfelter.shinyapps.io/FinalProjectKTA/

The whole code of this application can be found in this GitHub repo: https://github.com/kashenfelter/Data_Science_Capstone_Final_Project

3.2 References

• Data Science Capstone Final Project Deployment by Kathy Targowski Ashenfelter August 6, 2018

Winning Space Race with Data Science

November 1, 2022

This is the presentation of the capstone project in the IBM Data Science Professional Certificate .

Note that this presentation is much more detailed and technical than regular high-level and abstracted presentations for executive teams.

I assume the role of a Data Scientist working for a startup intending to compete with SpaceX , and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting results to stakeholders.

In this capstone, we will predict if the Falcon 9 first stage will land successfully, SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

Executive Summary

Introduction

Methodology

Data collection API notebook

Web scraping notebook

Data wrangling notebook

EDA with Visualization notebook

EDA with SQL notebook

Launch Sites Locations Analysis with Folium notebook

Interactive Dashboard with Ploty Dash

Machine Learning Prediction notebook

Insights Drawn from EDA

Launch Sites Proximities Analysis

Build a Dashboard with Plotly Dash

Predictive Analysis (Classification)

Conclusions

For notebooks, datasets and scripts, follow this GitHub repository link: Applied Data Science Capstone

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In this project, we predicted if the Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders.

chuksoo/IBM-Data-Science-Capstone-SpaceX

Folders and files, repository files navigation, ibm data science capstone project - spacex, introduction.

In this capstone, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

This capstone project course will give you a taste of what data scientists go through in real life when working with real datasets. You will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection , data wrangling , exploratory data analysis , data visualization , model development , model evaluation , and reporting your results to stakeholders. You are tasked with predicting if the first stage of the SpaceX Falcon 9 rocket will land successfully.

Business Problem

SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if you can accurately predict the likelihood of the first stage rocket landing successfully, you can determine the cost of a launch. With the help of your Data Science findings and models, the competing startup you have been hired by can make more informed bids against SpaceX for a rocket launch.

• To apply data science toolkit and machine learning in order to accurately predict the likelihood of the first stage rocket landing successfully, and thus determine the cost of a launch.
• Explore the data in order to obtain more insight from the data.

Business metric

Classification accuracy - number of correct prediction divided by the total number of prediction defined as: $$Accuracy = \frac{TP+TN}{TP+FP+TN+FN}$$

Deliverables

• Accurate predictive algorithms
• Business case report to stakeholders
• Jupyter Notebook 100.0%

Help Articles

Capstone projects, learner help center may 17, 2022 • knowledge, article details.

Capstone Projects are hands-on projects that let you apply what you've learned in a Specialization to a practical question or problem related to the Specialization topic.

Some  Specializations end with a Capstone Project. Others include projects throughout the Specialization.

Examples of Capstone Projects include:

• Analyzing a business case study and making strategy recommendations
• Developing an original web or mobile application
• Writing an in-depth research paper

You'll need to complete the Capstone Project to get credit for the Specialization.

Enroll in a Capstone Project

If the Capstone Project for a Specialization is a separate project, you'll need to enroll in the project just like a course.

Many capstone courses require you to have earned Course Certificates for all other courses in that Specialization. If you are having trouble enrolling in a capstone course, check your Accomplishments page to make sure you have Course Certificates for all the other courses in that Specialization.

To join a Capstone once you've earned Course Certificates for all the courses in that Specialization:

• From your Coursera account, click Enrollments .
• Click on the Specialization you want to enroll in the Capstone Project for.
• Click the Capstone Project course title at the bottom of the Specialization course list.
• To join the course, click Enroll .

If you paid in advance for the entire Specialization, your Capstone course fee is included in your Specialization fee. If you're paying by course, you'll need to pay for the Capstone when you enroll.

Capstone Project grading

Some Capstone Projects are peer-graded using Coursera's standard peer grading system . Other Capstone Projects are automatically graded or graded by experts.

In some Specializations, you will be evaluated only on your final project submission. In other Specializations, you will get separate grades for each section of the project.

To receive a passing grade for the Capstone, you will need to:

• Receive a passing grade for the final project and any required sections. 
• Complete all required evaluations of your peers' work.

Length of Capstone Projects

Capstone Projects vary in length, but you can expect to spend about 4 to 8 weeks working on your project, making revisions, and reviewing the work of your peers. Most Capstone Projects require about 40 hours of total work time.

Awards and incentives

In some Specializations, learners who submit outstanding Capstone Projects may be eligible for awards and incentives provided by academic and industry partners.

Examples of awards and incentives include:

• Virtual discussions with instructors or industry leaders
• Promotion of the project in key publications or channels
• Fee waivers for partner programs

Incentives are offered at the discretion of the Specialization creators and sponsors, and are not guaranteed in all Specializations or Capstone courses.

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Data Science with R - Capstone Project

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In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate. Read more.

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