Download Learn & Deploy Data Science Web Apps with Streamlit

Learn & Deploy Data Science Web Apps with Streamlit


Learn & Deploy Data Science Web Apps with Streamlit is a training course for designing, developing and implementing web applications based on data science with the Streamlit framework, which has been published by Udemy Academy. Streamlit is a framework based on the Python programming language that is used to develop machine learning and data science applications. Applications of this framework can be used to share analytical results, build complex and interactive projects, and describe machine learning models. The Streamlit framework greatly reduces the time and costs of developing such applications.

What you will learn in the Learn & Deploy Data Science Web Apps with Streamlit training course:

  • Download and run Streamlit demo applications
  • Edit demo and pre-made applications with dedicated text editors
  • Organizing and sorting Streamlit applications
  • Design and development of exclusive applications with Streamlit
  • Data visualization and drawing charts and graphs with Streamlit
  • Receiving user input data and validating them
  • Editing and page layout of Streamlit web applications
  • Basic installation of Streamlit
  • Elements and text-based elements in Streamlit
  • Display data in different ways in applications
  • Various Streamlit widgets
  • The final implementation of Streamlit applications on cloud spaces
  • And …

Course details

Publisher: Yudmi
teacher: G Sudheer Data Science Anywhere and Convolution Innovations
English language
Training level: introductory to advanced
Number of courses: 71
Training duration: 5 hours and 29 minutes

Course topics on 9/2022

Course prerequisites

Beginner to Python
Must know Pandas for Data Analysis

Course images

Learn & Deploy Data Science Web Apps with Streamlit

Course introduction video

Installation guide

After Extract, view with your favorite Player.

English subtitle

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 691 MB

Password file(s):


1.7 GB

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