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Introduction to Streamlit

Transform your Python skills with Streamlit! Enrol in our free course now to effortlessly create dynamic web applications. Elevate your projects and skills – start today!

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0.75 Hrs

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About this Free Certificate Course

Introduction to Streamlit is a free course diving into the simplicity and power of this Python library. Uncover Streamlit's features, from intuitive syntax for data visualization to effortlessly deploying Python-based web apps. Whether you're a beginner or an experienced developer, this course guides you through creating interactive applications, and making complex tasks easy. 

 

Learn to harness Streamlit's capabilities, turning your projects into accessible web apps. Join us on this journey to streamline your Python development and unlock the potential for dynamic, user-friendly applications. 
 

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Course Outline

Introduction to Streamlit

This module provides an introduction to Streamlit, which is an open-source Python library. It simplifies the process of creating and sharing beautiful, custom web apps for machine learning and data science.

Features of Streamlit

This module covers the various features of Streamlit, such as interactive widgets, data visualization, and model deployment.

Deployment with Sreamlit App

This module demonstrates the deployment of the Sreamlit App, which covers the installation of the Straemlit and various steps to deploy the app.

Streamlit using Python

This module talks about the various codes that help in building the Streamlit app, such as Click event app, EDA app, etc.

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Ratings & Reviews of this Course

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Hari Prasath M

5.0

“Amazing Experience with the Course”
Simple explanations, just enough to kickstart my learning of Streamlit.
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Rajeswari Alagappan

5.0

“Amazing Experience: A Great First-Time Learning”
I loved the way you briefed about the course and covered topics properly and clearly.
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Introduction to Streamlit

0.75 Learning Hours . Beginner

Why upskill with us?

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1000+ free courses
In-demand skills & tools
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Free life time Access

Frequently Asked Questions

What are the prerequisites required to enrol in this Free Introduction to Streamlit course?

You do not need any prior knowledge to enrol in this Introduction to Streamlit course. 
 

How long does it take to complete this Free Introduction to Streamlit course?

It is a 0.5 hour long course, but it is self-paced. Once you enrol, you can take your own time to complete the course.
 

Will I have lifetime access to the free course?

Yes, once you enrol in the course, you will have lifetime access to any of the Great Learning Academy’s free courses. You can log in and learn whenever you want to.
 

Will I get a certificate after completing this Free Introduction to Streamlit course?

Yes, you will get a certificate of completion after completing all the modules and cracking the assessment. 
 

How much does this Introduction to Streamlit course cost?

It is an entirely free course from Great Learning Academy.

Is there any limit on how many times I can take this free course?

No. There is no limit. Once you enrol in the Free Introduction to Streamlit course, you have lifetime access to it. So, you can log in anytime and learn it for free online.
 

Who is eligible to take this Free Introduction to Streamlit course?

You do not need any prerequisites to learn the course, so enrol today and learn it for free online.

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Introduction to Streamlit

In the world of data science and web application development, Streamlit has emerged as a game-changing tool that simplifies the process of creating interactive and data-driven applications. With its ease of use, Python integration, and rapid development capabilities, Streamlit has gained popularity among data scientists, developers, and professionals from various fields. In this article, we'll delve into what Streamlit is, its key features, and its impact on the development of data-centric applications.

What is Streamlit?

Streamlit is an open-source Python library that enables data scientists and developers to create web applications with minimal effort. It was first introduced in 2018 and has since gained a strong community of users and contributors. Streamlit's primary aim is to make it as simple as possible to turn data scripts into shareable web apps. By providing a high-level, intuitive interface, it empowers individuals to focus on data analysis and visualization, rather than getting bogged down in complex web development.

Key Features of Streamlit

  • Simplicity: One of Streamlit's core strengths is its simplicity. With just a few lines of Python code, you can turn your data analysis or machine learning script into an interactive web application. This low barrier to entry makes it accessible to data scientists and developers of all levels of expertise.
  • Rapid Prototyping: Streamlit allows you to quickly prototype and iterate on your ideas. You can see the changes in your application in real-time as you edit your code, making it ideal for experimentation and refining your projects.
  • Data Integration: Streamlit seamlessly integrates with popular data science libraries such as Pandas, NumPy, and Matplotlib. This enables you to easily load, manipulate, and visualize data in your applications.
  • Customization: While Streamlit focuses on simplicity, it also provides ways to customize the appearance and layout of your applications. You can use CSS, HTML, and various widgets to tailor the user interface to your specific needs.
  • Sharing and Deployment: Once you've built your application with Streamlit, you can easily share it with others. You can deploy your apps on various platforms, including Streamlit Sharing, Heroku, and AWS, allowing for widespread access to your creations.

Use Cases for Streamlit

Streamlit has found applications in various fields, including:

  • Data Visualization: Data scientists and analysts use Streamlit to create interactive dashboards and reports that make data exploration and communication more accessible.
  • Machine Learning: Streamlit simplifies the process of showcasing machine learning models, enabling data scientists and engineers to deploy and share their models with a wider audience.
  • Data Exploration and Analysis: Analysts can use Streamlit to develop tools for exploring data, enabling stakeholders to gain insights without needing in-depth data knowledge.
  • Education: Educators use Streamlit to build interactive tutorials and educational materials, allowing students to engage with data science concepts in a hands-on way.
  • Prototyping and Proof of Concept: Developers and product managers use Streamlit for quickly prototyping ideas and demonstrating proof of concepts, which helps in the decision-making process.

Challenges and Limitations

While Streamlit offers numerous advantages, it is essential to recognize its limitations. Streamlit is best suited for lightweight, single-page applications. It may not be the ideal choice for complex web applications that require extensive back-end functionality. Additionally, its simplicity can be limiting for developers who need more control and customization.

Conclusion

Streamlit has become a valuable tool in the world of data science and web application development. Its simplicity, rapid prototyping capabilities, and Python integration make it accessible to a broad audience. Whether you're a data scientist looking to share your findings, a developer prototyping a new application, or an educator creating interactive materials, Streamlit empowers you to create interactive and data-driven applications with ease. As the Streamlit community continues to grow, it is likely that we will see even more innovation and development in this space, further enhancing its capabilities and impact.
 

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