Lilly Tech Systems
Learn Jupyter Notebook
Master the open-source interactive computing environment used by millions of data scientists, researchers, and educators. Create notebooks that combine live code, visualizations, equations, and narrative text.
What You'll Learn
By the end of this course, you'll be proficient with Jupyter Notebook and JupyterLab for data science workflows.
Interactive Computing
Write and execute code in cells, mix with Markdown documentation, and create reproducible computational narratives.
Installation & Setup
Install Jupyter via pip or conda, configure kernels for Python, R, and Julia, and set up your environment.
Advanced Features
Use widgets, interactive plots, extensions, nbconvert, and parameterized notebooks for professional workflows.
JupyterLab
Explore JupyterLab, the next-generation interface with multi-tab editing, terminals, and a full IDE experience.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
What is Jupyter Notebook? History from IPython to Jupyter, use cases, and the Jupyter ecosystem overview.
2. Installation
Install via pip or conda, start the server, explore the interface, create kernels, and set up JupyterHub.
3. Notebook Basics
Cell types, running cells, keyboard shortcuts, Markdown formatting, magic commands, and output types.
4. Advanced Features
Widgets, interactive plots, extensions, nbconvert, parameterized notebooks, and testing strategies.
5. JupyterLab
JupyterLab vs Jupyter Notebook, multi-tab interface, extensions, terminals, and Git integration.
6. Best Practices
Notebook organization, version control, reproducibility, converting to scripts, and common mistakes.
Prerequisites
What you need before starting this course.
- Python installed on your machine (or use Anaconda)
- Basic command line / terminal familiarity
- Basic Python knowledge (helpful but not required)
- A text editor or IDE for occasional file editing