Course 8‐2 - Forestreee/Data-Analytics GitHub Wiki
Google Data Analytics Professional
[Google Data Analytics Capstone: Complete a Case Study]
WEEK2 - Building your portfolio
In this part of the course, you’ll get an overview of 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 and 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.
Learning Objectives
- Apply the practices and procedures associated with the data analysis process to a given set of data.
- Discuss the expectations involved in completing a data analysis case study.
- Move their portfolio to Kaggle, post and make it public
- Add R Code to a Kaggle Notebooks
- Recall the different types of Kaggle Notebooks
Getting started
Intro to building your portfolio (Reading)
Getting started with your case study
Hello! Great to see you again. Now that we've checked out some example case studies and portfolios, it's time to start creating your own. Coming up, you'll do an activity that'll help you get started. But before that, I wanted to tell you a little bit about the different approaches you can use to start your project.
There's two possible tracks you can use to frame out your case study and help you get started. In Track 1, you'll be able to choose a business question similar to the kind that interviewers might ask. There are several different options for you to choose from, with specific business tasks and different datasets for you to use.
In Track 2, you'll be asked to find a public dataset to explore something you're personally interested in. This could be anything. From analyzing a video game you like, to a study on a wildlife population you care about. This is the more flexible option, and you'll have more freedom to build something that's really personal to you. Depending on what you want to do with your case study, you might choose one over the other.
For example, if you want to create a case study that you can use to demonstrate your job skills for future interviews, then Track 1 might be more useful for you but if you have something you're personally interested in that you'd like to explore more, Track 2 can help you build a flexible portfolio piece. Or if you're interested in Track 1 and 2, you can do both.
Once you've decided on the track you're most interested in, you'll use the case study outline to help you start your project. The outline follows the phases of the data analysis lifecycle that we've been using throughout this program. You'll complete each phase from asking the right questions to preparing, processing, and analyzing your data, until you finally build your presentation and share it in your portfolio. Each phase will have key questions and activities to help guide you through the process. If you ever need to review something, you can always go back to any part of the program to help you.
As a quick reminder, the data you'll be working with for this project will be public and open-source. This data is great for demonstrating your skills as a data analyst but it's essential to avoid plagiarism by citing your sources.
Public, open-source data can be easily searched, and we don't want to pass it off as our own work. Plagiarism can have serious negative consequences, legally and personally. The beauty of our work as data analysts is that we can share and collaborate with each other. So let's remember to give credit to our sources. I hope you're excited about starting your case study. I'm really excited to see what you'll put together. After this, you'll be able to start working on the outline. Then we've got some other activities that'll help guide you. After that, we'll talk about sharing your portfolio. Good luck.
Next steps, choosing your track (Reading)
Capstone roadmap (Ungraded Plugin)
Case study track 1: Working with existing questions and datasets
Track 1 details
Case Study 1: How does a bike-share navigate speedy success?
Case Study 2: How can a wellness company play it smart?
Case Study 1: How does a bike-share navigate speedy success? (Discussion Prompt)
Case Study 2: How can a wellness company play it smart? (Discussion Prompt)
Case study track 2: Choosing your own questions and dataset
Track 2 details (Reading)
Case Study 3: Follow your own case study path (Reading)
Resources to explore other case studies (Reading)
Case Study 3: Follow your own case study path (Discussion Prompt)
Sharing your case study and portfolio
Unlimited potential with analytics case studies
Congratulations, you've officially completed your personal case study and started building your portfolio. That's an amazing accomplishment. And it's all because of the hard work you've put into practicing your data analyst skills. If you really enjoyed this process, feel free to try a different track or choose another business scenario. The guide you used to finish your first case study can be used over and over again. And the more you use it, the more you'll find yourself improving in some unexpected ways. Plus, adding more case studies to your portfolio will make it even stronger.
Coming up, we'll change gears a little. Now that you've got a portfolio over the completed case study, we can start talking about how to discuss your portfolio with recruiters, and some ways you can highlight your skills. This is where we start really putting the portfolio to good use. And you'll have a lot of opportunities to practice this. Great job, and I'll see you next time.
Sharing your portfolio
Hi there. Welcome back. Finishing the case study is a big step, but we still need to create a portfolio and share our analysis online. You have a lot of options when it comes to your own online portfolio. So, let's talk about where you can post your case study and how to make that decision. When you're thinking about where you want to share your portfolio, there's two questions that can help you decide.
First, what platforms align with your interests and passions? And second, where do you want to spend more time after this program? You have a few options. You could use Kaggle, GitHub, a blog or Tableau to share your work.
Now, let's talk about what each of these have to offer.
Kaggle has a broad data science community you could join. It hosts a lot of competitions for users to join in and offers all kinds of learning opportunities. This is a great option if you enjoy connecting with other data analysts.
GitHub's primarily used for programming languages like R or Python. It has a more technical setup than other platforms. But it's a great place to share your code and the how behind your analysis with other users. And if you want to learn from other data analysts' work, GitHub's a great place to be.
Blog platforms like Medium, WordPress and Google Sites are personalized and ownable. Blogs aren't as code-focused as Kaggle and GitHub, so you'll have to store your code somewhere else. And there might be a few extra steps you'll have to take to display code on blogs. But you can show off your expertise, write about your process in your own voice, and show thought leadership in your field.
Finally, you might choose Tableau to host your work. You've already got some experience with Tableau from our work here. It's a great option if you're focused on the data viz side of things. Plus, you can create interactive dashboards using Tableau's tools that are easily shareable.
Choosing where to host your portfolio is an important decision, but hopefully now you have some ideas about how each platform could be useful. And you might end up using multiple platforms over time to fit your specific needs.
The important thing is to remember the two questions we talked about earlier. What platforms align with your interests and passions? And where do you want to spend more time after this program?
Creating your online platform to share is one of the final steps in this capstone project.
Coming up, we've got a few activities to help walk you through that process. Then we'll meet back here to start talking about your next steps.