Streamlit Module — Building Data Apps in Pure Python

Audience: shared by BS Data Science 3rd semester (Introduction to Data Science) and MS Data Science 1st semester (Tools and Techniques in Data Science) · Level: Beginner Prerequisites: pandas, Matplotlib/Seaborn, basic scikit-learn (Module A/B) No HTML, CSS, or JavaScript required — Streamlit apps are 100% Python.

What this module is

Streamlit turns a plain Python script into an interactive web app. This module teaches the complete beginner path: from "what is it and why do data scientists use it" to a working machine-learning prediction app and a full mini dashboard.

Module map

#	Document	You learn	Example
1	streamlit-introduction.md	What/why Streamlit, install, run, first app	1 — Hello Data Science
2	streamlit-tutorial.md	Guided 45-min tour: app structure, reruns, first widgets	build your first app
3	streamlit-widgets.md	All input widgets: button, checkbox, selectbox, slider, file uploader…	2 — Interactive calculator
4	streamlit-pandas.md	Dataframes, tables, metrics, CSV viewer	3 — CSV data viewer
5	streamlit-visualization.md	Built-in charts, Matplotlib & Seaborn in apps	4 — Interactive EDA dashboard
6	streamlit-ml-app.md	Caching, session state, ML predictions	5 — ML prediction app
7	streamlit-dashboard-project.md	Sidebars, navigation, combining everything	6 — Mini data science dashboard
8	streamlit-faq.md	Common questions and gotchas	—
9	streamlit-cheatsheet.md	One-page reference of every element taught	—
10	labs/	4 practical labs (student version + solutions)	—

The six progressive examples

Example	File	Skill added
1 · Hello Data Science	streamlit-introduction.md	first app, text elements
2 · Interactive calculator	streamlit-widgets.md	number input, selectbox, button
3 · CSV data viewer	streamlit-pandas.md	file upload, dataframe, metrics
4 · Interactive EDA dashboard	streamlit-visualization.md	charts + interactive filters
5 · ML prediction app	streamlit-ml-app.md	caching, session state, predictions
6 · Mini data science dashboard	streamlit-dashboard-project.md	sidebar, navigation, full pipeline

Each example has: explanation → code → expected result → exercise → challenge.

Where this fits in the course

• Assignment 2 (../assignments/assignment-02-api-streamlit-app/) — the app you build there uses exactly the skills from documents 3–5; its bundled streamlit-tutorial.md is a shorter, assignment-specific version of document 2.
• Module C topic 11 (../module-c/topic-11-ai-in-streamlit.md) — how to add an AI chat box (local Ollama) to the apps you build here.
• Final project — the required interactive Streamlit app (../projects/final-project/) is documents 4–7 combined with real data.

Quick start

pip install -r requirements.txt        # includes streamlit
streamlit hello                        # demo app — proves the install
streamlit run app.py                   # run your own app (Ctrl+C to stop)

Every example in this module is a complete, runnable script. Save it as app.py and run streamlit run app.py.