Complete Guide to Data Science Applications with Streamlit


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.

Who this course is for:

  • Individuals interested in building data science and machine learning applications in Python


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

Last Updated 12/2020

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