Google Data Analytics Professional

[Google Data Analytics Capstone: Complete a Case Study]

WEEK1 - 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, 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.

Learning Objectives

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

Introduction to the capstone

Course syllabus

Refresher: Your data analytics certificate roadmap

Introducting the capstone project

Hi there. I'm so glad you're joining me for this last part of the program. This is an end-of- certificate project that Coursera and other learning platforms usually call a capstone. The capstone brings everything you've learned together. You'll have the opportunity to take all of your new knowledge and put it into practice with a data analytics case study.

In this video, we'll talk more about what that entails and how it can help you stand out during a job search. Case studies are practice data analytics projects. When you're job hunting, you might be asked to do a case study after the pre-screen call or the first interview.

The case study is a common way for employers to assess job skills and gain insight into how you approach common data-related challenges.

Different employers might send you different kinds of case studies. For example, you might be asked to clean and analyze a data set, offer a proposal around how to measure the success of a project, or figure out and define metrics of success for a specific product.

Usually, there's a time limit for the case study you've been asked to do. For example, a potential employer might give you some sample data and project questions and ask you to create a presentation or memo with your recommendations in 24 to 48 hours. That time limit can be a little challenging. But the good news is, your answer to the case study doesn't have to be perfect.

What's important is that you show off your thought process so that the interviewers can understand how you approach the problem. You can use the data analysis process we've learned throughout this program to guide you.

Let's check out an example and break down all of the parts.

This case study has all of the information we'd need to perform this task.

It starts here, with the title and the industry focus: predicting employee attrition rate for human resources.

It also includes a problem statement outlining what the overall goal is.

In this case, they're asking for a deep dive into key data analytics concepts to predict the employee attrition rate in the organization, and which factors influence an employee to leave the organization. Basically, this case study is interested in predicting the rate at which employees might leave the organization and why. There's some more specific goals in the next section.

It's asking us to find the probability of an employee leaving the company over the next five years. That's pretty straightforward, but they're also interested in ways to improve employee retention.

This next section is really key. The deliverables are what we'll actually give them once we've completed the case study. In this example, they're asking for a presentation outlining our findings and recommendations.

Finally, they've included some sections about the data we'll use for this task. Here, it's a dataset that we can download.

Now we know more about case studies and how they might be presented to us in a job application process. But people who are passionate about data analytics will sometimes do case studies on their own time and add them to a personal portfolio.

A portfolio is a collection of case studies that can be shared with potential employers. Portfolios can be stored on public websites like GitHub, Kaggle, or Tableau, or on your blog. Your portfolio can also be linked in your resume. This will give you examples of how you approached data tasks in the past that you can talk about in your interview. These portfolios showcase your skills and help you stand out in job applications. On top of the case study, we'll talk about building your portfolio and how to share it. This will be a great building block that you can use to build up your resume. Coming up, we'll check out some great examples of case studies and portfolios that will hopefully inspire you as you start your own.

Explore some real-world portfolios (Reading)

Data Journal: Prepare for your capstone project (Practice Quiz)

Rishie: What employers look for in data analysts

Google is a company that's built on data. It's all data-driven. And the idea behind it is that every person, whether you're an engineer, whether you're a marketer, a seller, or even you're working in admin, where you're handling logistics and paperwork and payrolls, everybody is dealing with data in some form or the other.

We're trying to acknowledge the fact that 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.

My name is Rishie, and I'm the Global Analytics Skills Curriculum Manager.

Introduce yourself (Discussion Prompt)

Best-in-class

Welcome back. Earlier, we talked about what a case study was and why adding one to your portfolio could help you stand out in a job search.

Now let's talk about some best practices for building case studies and portfolios and check out some great examples of other analysts' work.

When it comes to case studies there's a few important tips you'll want to keep in mind. First, make sure your case study answers the question being asked.

Let's check out a sample case study for a company we'll call Data Partners Real Estate. They asked job applicants, "How would you rate Data Partners Real Estate's resale performance in 2020, what's driving these trends, and what would your action plan be?" The company gave job applicants a market dataset including things like active listings, visits, resell contracts, price points, and geocodes.

Applicants had a day to go through the data analysis process and share a proposal. Here's a presentation one applicant came up with. Slide 2 lays out the question. The job candidate has identified poor performance in a specific housing price band that the company could improve. Including a quick overview of their findings here helps keep the case study focused on the task at hand.

On top of answering the question, you also want to make sure that you're communicating the steps you've taken and the assumptions you made about the data. One of the reasons potential employers are interested in case studies is because they show your thought process and problem-solving skills. Showing the steps you took to reach your conclusion can help them get a good idea of how you work.

Here, we've got an explanation of the metrics they use to perform the analysis. And in each slide after this, they use the title to tell their story and explain the steps of their analysis. They state the overall market share of this companies resell contracts has remained steady and they explained that this is the result of high growth in one area and losses in another. Then they explain this gap and outline some potential causes. And in the speaker notes, they've added some key assumptions they've made. To wrap it all up, they've acted on the data by providing recommendations for the business to consider. Their metrics were clearly defined, their data findings were organized in a logical, step-by-step order and they've made sure to explain any background information about their data that their audience may not know. In this case, the job candidate also shared documentation of their analysis, including their SQL queries and spreadsheets. This is a great example of how a case study can showcase an analyst's thought process.

Now, any case study you complete during a job application usually needs to be kept private. But you can also complete case studies on your own time and add these to your personal portfolio.

As we talked about earlier, your portfolio is a collection of case studies you want to show off and there are some best practices you can use for creating your portfolio too. The best portfolios are personal, unique, and simple. You've learned different ways that you can post and share your portfolio, like on a blog, GitHub, or Kaggle.

Let's explore some portfolios so we can understand what personal, unique and simple really means. As you might recall, these examples were also featured in a reading, so feel free to go back and check them out yourself. Your portfolio is a chance to show people who you are, what you're interested in, and what's important to you. Here's an example portfolio. Right away we can tell how personal this is from the title, Sharing my cancer story with data viz. This data viz showcases this analyst's health journey as they prepared for a marathon while also undergoing treatment for their cancer. It's a very personal and powerful story and he talks more about this project in his blog post. But it's also showing off his personality in the data viz itself.

Let's read some of these notes: "Mom if reading send more cookies." "Fitbit died, didn't care to charge it nine days."

In addition to the personal story this data tells, we also get these insights into the analyst's personality. Making your portfolio personal doesn't mean the focus has to be completely on you, but it is an opportunity for other people to get to know your better. It's good to add things to your portfolio that you care about, things that are interesting to you and stuff you'd love to share. This will highlight your technical skill and how you approach technical problems too.

Making your portfolio personal also helps make it unique. By highlighting the things you're interested in, you can stand out from the crowd. Let's check out another example. This is a Kaggle user's profile with some of the notebooks she's created. Each one of these is basically a case study that she's completed for her own enjoyment. She's got a few notebooks where she's worked with the palmer penguins data we used in R. But she's also got notebooks where she did an analysis on a video game she likes.

Using common examples can be great practice and show off practical job skills, but adding some unique and interesting case studies to your portfolio make it cool and memorable. In general, you want to keep your portfolio pretty simple. Our goal is to highlight our skills as data analysts, so we don't want to distract anyone who's visiting our portfolio with unnecessary clutter.

Here's an example of a portfolio on GitHub. This user's created a master list of R tutorials they created. It's simple and straightforward. There's a table of contents that leads to different pages to keep the portfolio landing page simple and easy to navigate. This doesn't mean this page is boring. They've added this fun cover art and talk about their own experiences with R here. But even with all that, we're not distracted by a messy webpage.

Finally, you want to make sure that your portfolio is relevant and presentable. If you know you're interested in a certain kind of data analyst position, you can tailor your portfolio to highlight those skills. Make sure you keep it up-to-date, ready for an employer to see and most importantly, that you're proud of what you've put together.

When it comes to case studies, you want to make sure that you're answering the question and communicating the steps you've taken. As you build your portfolio, remember: keep it personal, unique, and simple. Now that we have some ideas about how to create great case studies and portfolios, you're ready to start working on your own.

Coming up, we'll take our first step towards building our own case study. See you soon.

Your portfolio and case study checklist (Reading)

Revisiting career paths in data (Reading)

Test your knowledge on professional case studies (Practice Quiz)

Next steps (Reading)