Welcome to the Ultimate Guide to Streamlit: From Python Newbie to Web App Pro! This course is designed to take you on a journey from being a Python beginner to becoming proficient in building web applications using Streamlit.
Streamlit is an open-source app framework that allows you to create and share beautiful, custom web apps for machine learning and data science in just a few minutes. It enables you to build and deploy interactive, web-based dashboards, tools, and applications using Python, with minimal coding required. Streamlit is an excellent tool for data scientists, machine learning engineers, and researchers who want to share their work and insights with others in an engaging and interactive way.
In this course, we will start by introducing Streamlit, its features, and why you should use it. We will cover the prerequisites and setup required to get started with Streamlit. Next, we will dive into the basics of Streamlit, including installing it, creating your first Streamlit app, and understanding the basic structure of a Streamlit app. We will also cover the Streamlit Command Line Interface (CLI) and how to use it to manage your apps.
As we progress through the course, we will explore the core components and layouts of Streamlit, including displaying text, titles, headers, markdown, code, and LaTeX. We will also cover displaying data, tables, dataframes, metrics, and interactive widgets such as buttons, checkboxes, sliders, and selectboxes. We will then move on to advanced Streamlit features, including customizing layouts, columns, and containers, integrating charts and graphs, handling user input, forms, and callbacks.
We will also cover connecting to data sources, including reading and writing files, database connections, SQL and NoSQL databases, and APIs and web scraping. We will then explore the deployment and sharing options available for Streamlit apps, including local deployment, cloud deployment, Streamlit sharing, Heroku, AWS, and GCP. We will also cover best practices for deployment, troubleshooting, and optimization.
Finally, we will cover case studies and real-world applications of Streamlit, including data analysis dashboards, machine learning model interfaces, and interactive reports. We will also provide resources and further learning opportunities to help you continue your Streamlit journey. By the end of this course, you will have a solid understanding of Streamlit and be able to create and deploy your own web apps using Python.
So, whether you're a Python beginner or an experienced data scientist looking to create engaging web apps, this course is for you. Let's get started on your Streamlit journey today!