Improving Job Applications with Streamlit

At this point in this book, you should already be an experienced Streamlit user. You have a good grasp of everything – from Streamlit design to deployment, to data visualization, and everything in between. This chapter is designed to be application-focused; it will show you some great use cases for Streamlit applications so that you can be inspired to create your own! We will start by demonstrating how to use Streamlit for Proof-of-Skill Data Projects. Then, we will move on to discuss how to use Streamlit in the Take-Home sections of job applications.

In this chapter, we will cover the following topics:

• Using Streamlit for proof-of-skill data projects
• Improving job applications in Streamlit

Technical requirements

The following is a list of software and hardware installations that are required for this chapter:

• streamlit-lottie: We already installed this library in our Components chapter, but if you have yet to install it, now is a great time! To download this library, run the following code in your Terminal:

  pip install streamlit-lottie
  

  Interestingly, streamlit-lottie uses the lottie open-source library, which allows us to add web-native animations (such as a GIF) to our Streamlit apps. Frankly, it is a wonderful library that you can use to beautify Streamlit apps and was created by Fanilo Andrianasolo, a prolific Streamlit app creator.

• The job application example folder: The central repository for this book can be found at https://github.com/tylerjrichards/Streamlit-for-Data-Science. Within this repository, the job_application_example folder will contain some of the files that you will need for the second section of the...

Using Streamlit for proof-of-skill data projects

Proving to others that you are a skilled data scientist is notoriously difficult. Anyone can put Python or machine learning on their résumé or even work in a research group at a university that might involve some machine learning. But often, recruiters, professors you want to work with, and data science managers rely on things on your résumé that are proxies for competence, such as having attended the “right” university or already having a fancy data science internship or job.

Prior to Streamlit, there were not many effective ways to show off your work quickly and easily. If you put a Python file or Jupyter notebook on your GitHub profile, the time it would take for someone to understand whether the work was impressive or not was too much of a risk to take. If the recruiter has to click on the right repository in your GitHub profile and then click through numerous files until they find a Jupyter...

Improving job applications in Streamlit

Often, data science and machine learning job applications rely on take-home data science challenges to judge candidates. Frankly, this is a brutal and annoying experience that companies can demand because of the dynamic between the applicant and the employer. For instance, it could take a candidate 5–10 hours to fully complete a data science challenge, but it might only take the employer 10 minutes to evaluate it. Additionally, an individual virtual or telephone interview might take 30–45 minutes for the employer, plus an extra 15 minutes to write up feedback, compared to the same 30–45 minutes for the applicant. Because getting 5–10 hours of work gives them a very high signal per minute of employee time, employers have trended toward including these challenges within their job applications.

You can use the opportunity here to use Streamlit to stand out from the crowd by creating a fully functioning application...

Summary

This chapter is the most application-focused chapter we have created so far. We focused heavily on job applications and the application cycle for data science and machine learning interviews. Additionally, we learned how to password-protect our applications, how to create applications that prove to recruiters and data science hiring managers that we are the skilled data scientists that we know we are, and how to stand out in take-home data science interviews by creating Streamlit apps. The next chapter will focus on Streamlit as a toy, and you will learn how to create public-facing Streamlit projects for the community.

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