Master Streamlit: Build Interactive Data Apps with Python

Name: Master Streamlit: Build Interactive Data Apps with Python
Rating: 4.5 (20 reviews)

Go from Python scripts to interactive web apps and dashboards. Learn widgets, layouts, vizualization, APIs & deployment

Highest Rated

Created byAndy Bek

Last updated 1/2026

English

What you'll learn

Build interactive data dashboards with Python, using Streamlit, without needing web development expertise
Transform static data analyses (Pandas, NumPy) into engaging web applications that can be shared and explored by othersBI Professionals/Report Builders
Create custom data-driven tools and visualizations, using Streamlit components, to prototype ideas and explore datasets rapidly
Deploy interactive Streamlit apps to the cloud and share them with anyone, using free and easy-to-use hosting platforms
Integrate external data sources (CSV, JSON, APIs) into Streamlit apps to create dynamic and up-to-date visualizations
Design intuitive user interfaces for data exploration using Streamlit's layout options (columns, tabs, expanders)
Customize the appearance of Streamlit apps using themes and custom CSS to match branding or personal preferences

Course content

9 sections • 78 lectures • 9h 14m total length

Welcome!1:44
All Course Resources0:10

Why Streamlit?3:45
Setting Up Our Dev Environment7:18
Set up your development environment by installing Python 3.8+, creating and activating a virtual environment with venv, then installing and verifying Streamlit 1.41.1 for your data apps.
Our First App4:49
Create your first Streamlit app by writing a simple hello world in app.py, run it locally with Streamlit, and view live updates on localhost:8501.
Text And Markdown Elements10:19
Skill Challenge: Personal Bio App1:48
Solution2:23

Section Resources0:03
Displaying Data Tables8:27
Display data tables in streamlit apps by loading a csv with pandas into a data frame, then render interactive or static tables with a single line.
Basic Charts with Streamlit9:24
Leveraging Matplotlib & Plotly7:21
Explore how to enhance Streamlit apps by integrating Matplotlib and Plotly for customizable, interactive charts, covering setup, figure and axis creation, and multi-type visualizations beyond Streamlit's built-in charts.
Creating Interactive Charts with Plotly9:04
EXTRA: Bespoke Chart Customization with Matplotlib19:24
Master advanced matplotlib customization to turn a simple line chart into a bespoke visualization using figure size, markers, colors, axis labels, title, grid, legend, annotations, and spines.
Skill Challenge: Data Vizualization App2:10
Solution12:10
Solution Continued: Correctly Handling Ordered Categorical Variables3:29

Section Resources0:03
Introduction to Streamlit Widgets4:21
Buttons, Text Input, Sliders9:21
Selectbox, Radio, Checkbox18:28
Using Input to Filter & Update Data11:54
Skill Challenge: Interactive Data Filter App1:59
Build a streamlit app to filter products by category via select box, price range via slider, minimum rating, and a case-insensitive name search with automatic filtering and no apply button.
Solution13:22

Section Resources0:03
Columns for Side-by-Side Content7:41
Tabs for Content Segmentation6:12
Master tabs in Streamlit to segment content into distinct, navigable sections with tabbed interfaces, where switching unmounts previous content. Create tab containers and use widgets for interactive content.
Expanders for Hiding Content8:31
Use streamlit expanders to hide or reveal content on demand, keeping interfaces clean while providing access to advanced options and raw data. Control default expansion state and nest widgets inside.
Container Elements and Advanced Layouts10:30
Sidebars and Multiselects for Enhanced Filtering14:29
Customizing App Appearance with Themes8:00
Skill Challenge - Interactive Sales Dashboard4:57
Solution14:42

Section Resources0:12
Understanding the Anatomy of a Custom Component6:07
Setting Up the Component Development Environment4:35
Component Templating With Cookiecutter12:53
Customizing the Frontend: Defining a New Component12:16
Define a front end rating component from scratch in a Streamlit app using React and TypeScript. Learn to handle hover and click interactions and send data back to the backend.
Writing The HTML, TSX and CSS21:26
Wiring Up To The Python Backend12:55

Data Types2:35
Variables8:27
Arithmetic And Augmented Assignment Operators7:16
Ints And Floats8:54
Booleans And Comparison Operators5:12
Strings7:52
Makes strings in Python, a sequence of characters, using single, double, or triple quotes; escape quotes, concatenate, repeat, and explore string polymorphism.
Methods6:29
Containers I: Lists6:08
Lists vs. Strings6:53
Explore how lists and strings in python are ordered sequences, learn indexing and slicing, and compare mutability: strings are immutable while lists are mutable.
List Methods And Functions7:54
Containers II: Tuples4:43
Containers III: Sets10:32
Containers IV: Dictionaries5:15
Dictionary Keys And Values8:14
Membership Operators4:28
Learn how to use Python's in and not in membership operators across dictionaries, lists, tuples, sets, and strings to test presence or absence of items or characters.
Controlling Flow: if, else, And elif8:21
Truth Value Of Non-booleans3:28
For Loops5:05
The range() Immutable Sequence5:10
Understand the range construct as an immutable sequence of integers with start, stop, and step, where start is inclusive and stop is exclusive, and range object acts as an iterable.
While Loops5:55
Break And Continue4:15
Zipping Iterables3:39
List Comprehensions7:47
Defining Functions10:18
Function Arguments: Positional vs Keyword6:54
Lambdas5:28
Importing Modules5:38

Requirements

Basic computer literacy. No prior programming experience is required. We'll cover all the necessary Python and Streamlit concepts from the ground up.

Description

Are you a data scientist, analyst, engineer, or researcher who works with Python? Do you want to share your data insights in a more engaging and interactive way, without having to learn complex web development frameworks? Then this course is for you!

Streamlit is a revolutionary open-source Python library that makes it incredibly easy to build beautiful, interactive web applications for data science and machine learning. With Streamlit, you can turn your data scripts into shareable web apps in minutes, using only Python. No need for HTML, CSS, or JavaScript!

This comprehensive course will guide you from the very basics of Streamlit to building and deploying sophisticated, interactive data dashboards and tools. You'll learn how to:

Get Started: Set up your development environment and create your first Streamlit app.
Display Data: Work with text, tables, and a wide variety of charts (line charts, bar charts, area charts, and more) using Streamlit's built-in functions and popular libraries like Matplotlib and Plotly.
Add Interactivity: Use Streamlit's powerful widgets (buttons, sliders, selectboxes, text inputs, etc.) to create dynamic applications that respond to user input.
Control Layout: Organize your apps with columns, tabs, expanders, and containers for a clean and intuitive user interface.
Work with Data: Load data from CSV files, JSON files, and even external APIs.
Persist State: Store user preferences and data across sessions using cookies.
Deploy Your Apps: Share your creations with the world using Streamlit Sharing and other cloud deployment options.
Go Beyond the Basics: Learn how to extend the capabilities of Streamlit by building custom components using React, opening up endless possibilities for creating unique and powerful data applications.

This course emphasizes hands-on learning, with numerous examples, practical exercises, and skill challenges to reinforce the concepts.

By the end, you'll be able to confidently build and deploy your own interactive data apps with Streamlit, transforming the way you work with and communicate data. Whether you're a seasoned data professional or just starting your journey, this course will empower you to create compelling data-driven web applications with ease.

And if you're new to Python, don't fret! There is a full-length introduction to Python included as an Appendix which is included to get anyone up and running writing pythonic code in no time.

See you inside!

Who this course is for:

Anyone interested in turning Python scripts into interactive web applications
Individuals with some Python experience who want to rapidly prototype data-driven applications and dashboards
Data professionals (analysts, scientists, engineers) who want to share their work interactively and create internal tools, without complex web development
Researchers, academics, and educators who want to create interactive visualizations and simulations to explain concepts or share findings effectively

Master Streamlit: Build Interactive Data Apps with Python

What you'll learn

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Course content

A Very Quick (And Warm) Welcome!2 lectures • 2min

Introduction To Streamlit6 lectures • 30min

Displaying Data & Charts9 lectures • 1hr 12min

Interactive Widgets & Input7 lectures • 59min

Layout & Organization9 lectures • 1hr 15min

Working With Files & Media8 lectures • 1hr 3min

Creating Custom Streamlit Components7 lectures • 1hr 10min

Deployment & Next Steps3 lectures • 10min

Appendix A - Rapid-Fire Introduction to Python27 lectures • 2hr 53min

Requirements

Description

Who this course is for: