Streamlit data applications for dynamic data visualization

What you’ll learn:
Create Streamlit applications using Python
Sketch and script back and front end code
Refactor code for optimal development
Aggregate time series data for dashboard reporting

Requirements:
You’ll get the solutions to learn comfortably on your own.
We will explain everything step by step, starting from blank notebooks.
No prior experience is required. However, if you already know Python or data analysis, you will be more comfortable.

Description:
This course teaches you how to convert a Jupyter Notebook into a fully functional Streamlit web application. Starting with existing code, you’ll see how to organize and migrate it into the Streamlit environment. You’ll learn to handle data, create interactive visualizations, and develop user-friendly interfaces.We’ll show you step-by-step how to:1. Set up and run a Streamlit application.2. Integrate data selection widgets for dynamic updates.3. Use Plotly for data visualization.4. Implement multi-select options for data granularity.5. Style your DataFrames for better visual appeal.6. Debug common issues like data path errors and improper widget setup.By the end of this course, you can create robust web applications to explore and present your data, which you can share with colleagues, friends, or customers. You’ll gain practical experience to enhance your portfolio, making you a strong candidate for data-driven roles. Join us to transform your data analysis workflow with Streamlit!Sign up today and elevate your data presentation skills! With our comprehensive guidance, you’ll become proficient in building interactive web applications. This course covers everything you need to know, from setting up your first Streamlit app to mastering advanced visualization techniques. You’ll also learn to troubleshoot common problems, ensuring a smooth development process. Our step-by-step approach ensures that even complex concepts are easy to grasp. By the end of the course, you’ll have a powerful, shareable web application and the skills to continue building more. Don’t miss this opportunity to advance your data science career!

Who this course is for:
Professionals to automate their data reports programmatically with Python.
Students to learn with hands-on exercises and practical projects.
Job seekers looking to add end-to-end projects to their portfolio.
Data enthusiast who’d like to take a step further by developing data applications to showcase their findings, instead of leaving the visualizations in a notebook.