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Learn how to build and deploy data science applications in Python
Updated on Sep, 2026
Programming, Data Science, Python
Duration - 9.5 hours
Training 5 or more people ?
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Analyzing data and building machine learning models is one thing. Packaging these analyses and models so that they are shareable is a different ball game altogether.
This course aims to teach 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:
Introduction to Streamlit
Integrating Matplotlib 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.
Check out the detailed breakdown of what’s inside the course
Derrick Mwiti is a data scientist who has a great passion for sharing knowledge. He is an avid contributor to the data science community.
Experienced in data science, machine learning, and deep learning with a keen eye for building machine learning communities.
Derrick works as a machine learning developer advocate, where he helps companies build products that developers want. It involves getting feedback to the companies as well as getting feedback to developers.
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