Scientific Computation

for Research and Teaching (with Jupyter)

Michael Pilosov, University of Colorado Denver

Background

  • Open-source community growing rapidly
    • MOOCs, textbooks
  • Disrupting traditional software stacks
    • no licenses/contracts!
  • Students often feel intimidated by coding
    • installations vary widely
  • Address scientific reproducibility crisis

Simplify Setup for Students

  • Get username/password from professor
  • Log in from ANY computer
    • Platform independent
  • “All” software is pre-installed
    • Additional packages can be installed by students
  • work/ folder allows data to persist
  • Computations are performed “in the cloud”
    • better battery life!
    • cheaper computers, labs

Features

  • Students are “isolated” in their environments
    • Restricted permissions
    • Harder for them to “break” things
  • Data and Application are divorced
    • “Light” server-load due to containerization
    • Backups are “easier”
  • Can scale with class-size

Intuitive

Extensible

Customizable

shortcut-gif

Feature-Rich

Professional Document Editor

Source

Version Control (git)

git-gif

Serious Potential

  • Can scale class sizes
  • Automatic-grading, other features
  • Interactive Textbooks/Notebooks/Labs
  • Drop outdated technology contracts (MyMathLab)
  • Students using cutting-edge open-source software
    • can start coding “early”
    • looks great on resume
  • Professors/IT people can avoid cross-platform software support

Questions?

Homepage