Learn & Deploy Data Science Web Apps with Streamlit

Welcome to the course Learn Streamlit for Data Science

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps.

On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.

In this course, we start out with the Streamlit basics. We will learn how to download and run demo Streamlit apps, how to edit demo apps using our own text editor, how to organize our Streamlit apps, and finally, how to make our very own. Then, we will explore the basics of data visualization in Streamlit. We will learn how to accept some initial user input, and then add some finishing touches to our own apps with text. At the end of this course, you should be comfortable starting to make your own Streamlit applications.

In particular, we will cover the following topics:

• Why Streamlit?

• Installing Streamlit

• Organizing Streamlit apps

• Streamlit

• Text Elements

• Display Data

• Layouts

• Widgets

• Data Visualization

  • Integrating Widgets to Visualizations

  • Plotly

  • Bokeh

  • Streamlit

• Data Science Project

• Deploy Data Science Web App in Cloud