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 11: Using Streamlit for Teams

Throughout the past two chapters, we have deeply explored how to use Streamlit for things such as personal data projects, projects for resume building, and even for creating apps for take-home job applications. In this chapter, we will focus on using Streamlit at your place of work, as a data scientist, machine learning engineer, or data analyst. We already know that Streamlit can be used as a convincing tool to influence those around us through thoughtful and interactive analyses, and we will work on applying that to the work data scientists actually do. 

Streamlit is both a company and an open source library and makes revenue by being such an excellent tool in a data science toolkit that companies are convinced to pay for special features and customizations that increase the productivity of their own data scientists. The best part about this is that the company is directly incentivized to make the experience of using the tool as useful...