Course 8 – Google Data Analytics Capstone: Complete a Case Study

1. Capstone basics: A capstone project in Coursera refers to a final project at the end of a study program. In the real world, these types of projects are more often referred to as case studies, Case studies are common ways for employers to assess the skills of prospective job candidates. In this part of the course, you will explore the benefits of using capstone projects, case studies, and portfolios to showcase your new skills to potential employers.
2. Building your portfolio: In this part of the course, you will be introduced to two tracks (and possible cases for you to use) to complete your capstone project. Depending on which track you choose, you will then be directed to specific lessons and instructions that are applicable to the track you selected. The final deliverable in either track is a finished case study for your online portfolio.
3. Using your portfolio: Having a case study in your portfolio is a first step. In this part of the course, you will focus on how you will use your portfolio to highlight skills that employers are looking for. You will develop an elevator pitch for your case study that enables people to quickly grasp a high-level understanding of what you did. Then, you can practice incorporating aspects of your case study into answers for different types of interview questions.

Your final deliverables will include the following:

• Completed case study
• Online portfolio
• Elevator pitch (for your case study)
• Updated LinkedIn profile

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

• Identify the key features and attributes of a completed case study
• Differentiate between a capstone, case study, and a portfolio

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• A common way for employers to assess job skills and gain insight into how you approach common data related challenges

Item	Content
Title	Predicting Employee Attrition Rate
Industry focus	HR
Problem statement	Deep dive into key data analytics concepts to predict the employee attrition rate in the organization, as well as which factors influence an employee to leave the organization.
Business use case (what are you solving for?)	1. Predicting individual employee attrition 2. Depiction important factors for an employee to leave the organization 3. Improving employee retention
Goals / metrics	Find the probability of an employee leaving the firm for the next 5 years and identify how to improve employee retention
Deliverables	A presentation outlining your finding and recommendations
Are databases available?	Yes
Dataset list	The data set can be downloaded from the link given in the below section
Website to scrape the data needed	https://www.kaggle.com/jacksonchou/hr-data-for-analytics

Earlier, you learned that a portfolio contains samples that you share with potential employers. Case studies are practice or example data analytics projects that you can create for your portfolio. After you have created your online portfolio, you can add a link to it on your resume. Having a portfolio to showcase who you are and demonstrate your skills will help you stand out to potential employers. The case study that you will complete in this course can be one of the examples that you add to your portfolio.

In this reading, you will learn some important things to keep in mind when building your portfolio. You will also explore GitHub and Kaggle, which are platforms that can host your portfolio. You will view the professional profiles of two data analysts and what they include in their portfolios on Kaggle.

First and foremost, your portfolio should represent your own work. While getting ideas from other portfolios is inspiring, directly copying (or only slightly modifying) others’ work and sharing it in your own portfolio is never acceptable.

Additionally, if you work on a project as a data analyst, keep in mind that the work you do for an employer or client belongs to their business. In many cases, you can’t share that work publicly in your personal portfolio without direct and explicit permission from them beforehand.

Finally, be cautious even with open or public datasets. Unless you are using data that you personally collected, ask the owner of the data for permission before you post anything related to the data in your portfolio. You should always take full responsibility for what you publish by getting the right permissions as needed.

Now, let’s review three platforms you can use to host your portfolio.

Creating a personal website to host your portfolio is a great option because you can also use it to showcase aspects of your personality or background that contribute to your professional brand. For example, you might share a compelling experience that reflects your ability to collaborate, be resilient, or not give up. Whatever you choose to share, make sure that it is something you wouldn’t mind other people knowing about you.

For example, this visualization from data analyst Bill Yost’s website demonstrates his ability to create a Tableau visualization but also tells a very personal story about his battle with cancer. Potential employers get an idea of his skills and find out a lot more about who he is at the same time.

Notice that although the annotations in the visualization appear somewhat crowded in the white space (per guidelines in the Share Data Through the Art of Visualization course), the concept of sharing a personal story is the main takeaway.

GitHub is a hosted platform primarily used by developers as a repository for code, but it can also be used as a repository for documentation. One of the tips you have been given in this program is to keep an electronic journal of things to remember, especially for SQL or R syntax. If something in your electronic journal is particularly useful, you can create a document for your portfolio in GitHub. For inspiration, check out this R usage tips readme document a GitHub user posted.

If you have an account on Kaggle, you can also use it as a platform to host your portfolio and personal background. Check out these profile examples:

• Jesse Mostipak's profile
• Meg Risdal's profile

Their profiles showcase competitions they have participated in, datasets they have created, and discussions they have contributed to. Kaggle competitions are challenges that people take on at any stage of their programming and machine learning careers. Check out this YouTube video to learn how to enter a Kaggle competition. Both Jesse’s and Meg’s profiles also include links to follow them on other social media platforms, like LinkedIn and Twitter.

Jupyter Notebook is an open-source web application that you can use to create and share documents that contain live code, equations, visualizations, and narrative text. Kaggle supports a Jupyter Notebook environment that can be accessed from a browser. Jesse and Meg also have notebooks in Kaggle. You can use Kaggle to create your own notebooks for potential employers to view.

• Click Jesse Mostipak's Notebooks link to view her notebooks
• Click Meg Risdal's Notebooks link to view her notebooks

"In any occupation, across the globe, across various industries, having a knack and understanding of data is going to be crucial for everyone.

"When you are in an interview, what I personally look for, what even my colleagues look for is the way they think about this creatively. When people hear the word "data analyst," they think about engineers or someone who is extremely technical, and it's all about working with data and numbers. But I implore people to rethink about that perception that being a data analyst is not being a scientist, but that it's also being an artist. The entire world is your canvas. The way that you approach it and even sometimes challenge the traditional norms of solving a problem, I think that seems to be very powerful, and it actually puts you on the edge as compared to other people when you're interviewing for such roles.

"There's a misconception or a myth that when you're applying for a job, you should know all the right answers. You should answer every question that they ask correctly. But that's false. What every interviewer is looking for is how you think, what's your thought process, what is your way of looking at a certain problem, and how do you approach solving those problems. When you express that, I mean, you talk a little bit more about how you think about in a certain perspective, why you think about in a certain perspective, it speaks a lot about you as a person and also what's your professional capability to be in that position.

"One of the fascinating things about being a data analyst is you are a storyteller. You look at the data and every data point out there has a story to tell. If you are able to perfect that skill, you can tell some amazing stories. What people will remember is not just the data, but how you tell those stories to the people or to your audience. If you talk about the core essence of the story that this is what the data is telling me, or this is what the data tells you to do, you become a lot more successful. And I guarantee you that you will progress in being a data analyst and your career will prosper indefinitely."

• Rishie, Global Analytics Skills Curriculum Manager at Google

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Even if you don’t have previous data analytics work experience, you can still craft a great portfolio that represents your new skills and offers insight into who you are. Be sure to include the following in your portfolio:

• Biography: The main focus of your portfolio is to introduce yourself in a strong and memorable way. Write a concise and clear introduction of yourself. The goal is to capture your audience’s interest and compel them to want to meet you to learn more.
• Contact page: Be sure to include a way for others to get in touch with you, whether it be via email, phone call (if you are comfortable), or social media handles (especially LinkedIn). You might find that your website has its own built-in contact form if you use common website builders.
• Resume: In previous readings, Adding Professional Skills to your Resume and Adding Soft Skills to your Resume, you learned how to craft a resume that reflects your skills and your experience. Be sure to include a resume somewhere in your portfolio.
• Accomplishments: You are not just limited to your past experiences. Any present career-worthy highlights you can think of should be included. This can be any certifications you have earned, data analytics events you have attended, or even blog posts you have published.
• An image of you (optional): Add a personal touch with your photo. All you need is a simple, clear photo that represents you well.

During your interview process, you will very likely encounter the case study interview. In this interview, you will be provided with a business-related scenario where you analyze a problem and come up with the best solution. You will have a certain amount of time to solve this so it is best to be prepared for any scenario you are given. A great case study will include the following:

• Introduction: Make sure to state the purpose of the case study. This includes what the scenario is and an explanation on how it relates to a real-world obstacle. Feel free to note any assumptions or theories you might have depending on the information provided.
• Problems: You need to identify what the major problems are, explain how you have analyzed the problem, and present any facts you are using to support your findings.
• Solutions: Outline a solution that would alleviate the problem and have a few alternatives in mind to show that you have given the case study considerable thought. Don’t forget to include pros and cons for each solution.
• Conclusion: End your presentation by summarizing key takeaways of all of the problem-solving you conducted, highlighting what you have learned from this.
• Next steps: Choose the best solution and propose recommendations for the client or business to take. Explain why you made your choice and how this will affect the scenario in a positive way. Be specific and include what needs to be done, who should enforce it, and when.

Get a better idea of an employer's expectations from the case study questions in this blog article: 4 Case Study Questions for Interviewing Data Analysts at a Startup.

In a reading in the Foundations: Data, Data, Everywhere course, you learned about three different career paths in data science: data analyst, data scientist, and data specialist. This reading revisits the data analyst career choice (first column in the table below) to explore how the skills you have learned in this program match up with real job requirements.

=	Data Analyst	Data Scientists	Data Specialists
Problem Solving	Use existing tools and methods to solve problems with existing types of data	Invent new tools and models, ask open-ended questions, and collect new types	Use in-depth knowledge of databases as a tool to solve problems and manage data
Analysis	Analyze collected data to help stakeholders make better decisions	Analyze and interpret complex data to make business predictions	Organize large volumes of data for use in data analytics or business operations
Other relevant skills	- Database queries - Data visualization - Dashboards - Reports - Spreadsheets	- Advanced statistics - Machine learning - Deep learning - Data optimization - Programming	- Data manipulation - Information security - Data models - Scalability of data - Disaster recovery

The skills you gain with the Google Data Analytics Certificate align with skills that data analyst jobs require. When you create your resume, the way you present your skills can capture the attention of a recruiter or a hiring manager. Many career counselors recommend that you customize your resume each time you apply for a job so that your experience and skills align as closely as possible with the requirements listed in the job description.

For each of the relevant skills in the previous table, consider the following:

• Possible phrases from job descriptions
• Examples of matching skills from this certificate

Let’s go through the skills for data analysts and examine common phrases you might find in job descriptions.

Job description phrase	Skills from this program you could include in your resume
Collect data by using a scripting language such as SQL	- Perform SQL queries - Sort and filter data using SQL queries - Convert data types using SQL functions

Job description phrase	Skills from this program you could include in your resume
Visualize data insights and communicate your findings to teams in other organizations	- Create data visualizations using Tableau - Create visuals in spreadsheets - Create presentations from data analysis results

Job description phrase	Skills from this program you could include in your resume
Build and train users of new dashboards	- Identify the data needs of users - Create dashboards using Tableau - Use design thinking to improve dashboards

Job description phrase	Skills from this program you could include in your resume
Create comprehensive reports	- Create data cleaning reports - Create and maintain change logs - Create reports in R Markdown

Job description phrase	Skills from this program you could include in your resume
Explore and analyze datasets with spreadsheets	- Clean data in spreadsheets - Sort and filter data in spreadsheets - Create pivot tables in spreadsheets

Job description phrase	Skills from this program you could include in your resume
Knowledge of some programming languages and an organized and methodical approach to work	- Install and use the tidyverse package in R - Run scripts in RStudio - Create data visualizations in RStudio

This is an area where you can potentially distinguish yourself from other candidates when you apply for a data analyst position. Programming is considered a more advanced or higher-level skill and might not even be in a job description for a junior data analyst role. You learned to use R for data analysis as part of this program, and adding programming skills to your resume might make your application stand out.

If your goal is to work in a more technical role like a data scientist, the Google Data Analytics Certificate is a good starting point. But you might need to pursue additional learning opportunities to advance your skills, such as:

• Completing other professional certificates (Coursera offers many)
• Registering for college courses as a part-time or full-time student and applying for paid internships
• Continuing your education in a four-year college degree program like computer science, data science, or management information systems

For more information about career paths in data science, including roles that are more technical, refer to this article:

Career Paths Within Data Science

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TOTAL POINTS 5

Fill in the blank: A _____ is a collection of case studies that you can share with potential employers**.**

• portfolio
• personal website
• problem statement
• capstone

Correct. A portfolio is a collection of case studies that you can share with potential employers.

Which of the following are important strategies when completing a case study? Select all that apply.

• Document the steps you’ve taken to reach your conclusion
• Communicate the assumptions you made about the data
• Use a programming language
• Answer the question being asked

Correct. When completing a case study, it’s important to answer the question being asked. It’s also important to communicate the steps you’ve taken to reach your conclusion and the assumptions you made about the data.

To successfully complete a case study, your answer to the question the case study asks has to be perfect.

• True
• False

Correct. To successfully complete a case study, your answer to the question the case study asks does not have to be perfect. It’s more important to show off your thought process so that the interviewers can understand how you approach the problem.

Which of the following are qualities of the best portfolios for a junior data analyst? Select all that apply.

• Unique
• Personal
• Large
• Simple

Correct. The best portfolios are personal, unique, and simple. Your portfolio’s a chance to show people who you are and what you’re interested in. You want to keep your portfolio pretty simple, and focus on your skills as a data analyst.

Which of the following are places where you can store and share your portfolio? Select all that apply.

• Tableau
• RStudio
• GitHub
• Kaggle

Correct. Portfolios can be stored and shared on public websites, including Github, Kaggle and Tableau, or on your personal website.

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The following diagram provides an overview of the next steps that you will take to finish this course. You will complete a case study, create an online portfolio, update your LinkedIn profile, and prepare to interview for data analyst jobs.

Sections of this reading will direct you to resources in this course and other courses in the program that will help you complete each step.

In this course, you have options for selecting and developing a case study. You can choose one of two possible tracks.

The first track has two cases already defined. You can pick one of these cases and follow through on the data analysis to answer the questions presented to address business problems. For more information, refer to the track 1 details.

The second track allows you to design your own case study about a topic that you are interested in. You can practice all but the Act phase of the Data Analysis Process: Ask, Prepare, Process, Analyze, Share, and Act. For more information, refer to the track 2 details. You can also return to the Six steps of data analysis video to review the steps of the Data Analysis Process.

After familiarizing yourself with the details of each track, refer to the information in Next steps: choosing your track and decide which track you want to follow.

After completing your case study, you will create an online portfolio to store and display it. Refer to Creating your online portfolio. It provides an overview of platforms that can potentially host your portfolio and case study.

In an earlier course, you learned about creating an online presence with a LinkedIn account in Getting started with LinkedIn. Access your LinkedIn profile again to add a link to your online portfolio in your profile.

You can start to prepare for interviews by creating an elevator pitch for your case study. Refer to What makes a great pitch to understand the kinds of questions to prepare for. Try to include aspects of your elevator pitch in your answers to the sample questions provided in the reading. Then, practice pitching your case study as part of your planned responses to commonly asked interview questions.