Complete Guide to Data Science Applications with Streamlit

Learn how to build and deploy data science applications in Python

What you’ll learn

Complete Guide to Data Science Applications with Streamlit

Building Data Applications with Streamlit
Integrating Matptlotlib & Seaborn in Streamlit
Plotly Visualizations in Streamlit
Authenticating Streamlit Applications
Deploying Streamlit Applications
Using Streamlit Components
Altair Visualizations in Streamlit

Requirements

Basic Python Programming, however, a Python crash course is included

Description

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether.

This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don’t need any experience in building front-end applications for this. Here are some of the things you can expect to cover in this course:

Python Crash Course
NumPy Crash Course
Introduction to Streamlit
Integrating Matplotlit and Seaborn in Streamlit
Using Altair and Vega-Lite in Streamlit
Understand all Streamlit Widgets
Upload and Process Files
Build an Image Processing Application
Develop a Natural Language Processing Application
Integrate Maps with Streamlit
Implement Plotly Graphs
Authenticate Your Applications
Laying Out your Application in Streamlit
Developing with Streamlit Components
Deploying Data Applications

At the end of the course, you will have built several applications that you can include in your data science portfolio. You will also have a new skill to add to your resume.

The course also comes with a

30-day money-back

guarantee. Enroll now and if you don’t like it you will get your money back no questions asked.