Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases

Chapter 9: 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 then 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