I’m pleased to share that I now have a JupyterLite instance running at jupyter.nickmeyer.phd

Description of JupyterLite

  JupyterLite brings much of the Jupyter Notebook experience directly into your web browser. Unlike a traditional Jupyter server, JupyterLite runs entirely on the client side, meaning there is nothing to install and no account required to get started.  

What You Can Do

  The environment is particularly useful for:  

  • Writing and running Python code directly in your browser
  • Creating and editing Jupyter notebooks
  • Exploring computational and mathematical ideas interactively
  • Practicing programming concepts without setting up a local Python installation
  • Quickly testing code from class, homework assignments, or independent projects  

Because the environment runs entirely in the browser, it’s an easy way to get started with Python on virtually any device, although it works best on an iPad or larger device.  

Preinstalled Packages

  To make the environment useful right away, several commonly used scientific computing, symbolic mathematics, data analysis, and visualization libraries are available by default:  

  • NumPy – numerical computing and array-based programming
  • SymPy – symbolic mathematics and algebra
  • Pandas – data analysis and manipulation
  • Matplotlib – foundational plotting and visualization
  • Plotnine – grammar-of-graphics plotting inspired by ggplot2
  • Altair – declarative statistical visualization
  • Bokeh – interactive browser-based visualizations
  • Plotly – interactive charts and dashboards   These packages support a wide range of applications in mathematics, statistics, data science, and STEM coursework, making the platform useful for everything from introductory programming exercises to exploratory data analysis and mathematical modeling.  

Why I Set This Up

  One of the recurring barriers students face when learning programming is software installation and configuration. By providing a browser-based environment, students can focus on learning computational thinking and problem-solving rather than troubleshooting setup issues.   I also see this as an investment in future courses. As I continue developing and refining computational components of my curriculum, this platform will provide students with immediate access to the tools they need on day one. Whether you’re experimenting with NumPy arrays, creating visualizations, exploring symbolic computations in SymPy, or analyzing real-world data with Pandas, you’ll be able to jump right into the material without worrying about setup.  

A Few Notes

  • The service is intended primarily as a learning and experimentation platform for my students.
  • Browser storage is used to save notebooks, so important work should be backed up regularly.
  • Performance may differ from a full local Python installation, particularly for larger computations.
  • Not all Python packages are available, but the included scientific computing stack should support a wide variety of introductory and intermediate projects.  

Looking Ahead

  This is just the beginning. I set up this JupyterLite instance with future courses in mind, and I expect the platform to evolve over time as new instructional needs arise.   Keep an eye out for future updates, including additional packages, example notebooks, course-specific resources, and other improvements designed to support computational learning across mathematics and STEM disciplines.  

Give It a Try

  Visit jupyter.nickmeyer.phd and start experimenting.   If you encounter any issues or have suggestions for additional features, resources, or packages, please let me know. I’m excited to see how students and colleagues make use of this new computational resource.