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)
1
Section 1: Creating Basic Streamlit Applications
7
Section 2: Advanced Streamlit Applications
11
Section 3: Streamlit Use Cases

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...