Introduction
Tu.it dataLabs Jupyter as a Service offers lecturers and students a well integrated environment for data science, machine learning and programming classes. Each class gets its own fully customized JupyterHub with guarantied hardware resources necessary to solve the given exercises.
Features
- Tuwel integration
- Exercise distribution and grading with nbgrader
- Individual software stack for each class
- Virtual Desktop for GUI applications
- Theia IDE
- Powerful hardware platform
Hands on video
Useful Links
- https://jupyter-notebook.readthedocs.io/en/stable/
- https://jupyterlab.readthedocs.io/en/latest/
- https://nbgrader.readthedocs.io/en/stable/
- https://jupyterbook.org/intro.html
Get a JupyterHub for your lecture
Lectures with TU.it dataLab JupyterHubs
SS 2022
- 120.081 Klima- und Umweltfernerkundung (summer)
- 120.112 einführung in das programmieren ii für geodäsie und geoinformation
- 101.275 Einführung in das Programmieren für Technische Mathematik (summer/winter)
- 101.953 AKNUM Scientific Computing für Finite Elemente Methoden
- 186.143 Informationsvisualisierung
- 330.282 Knowledge Integration in Cyber Physical Production Systems
- 105.632 Model-based Decision Support
- 105.730 Modellierung dynamischer Umweltsysteme
- 138.128 Machine Learning in Physics
- 120.113 Python-Programmierung für Geowissenschaften
- 194.035 Recommender Systems
WS 2021
122.424 Introduction to Programming I for Geodesy, Geoinformation and Environmental Engineering
- 191.116 Scientific Programming with Python
- 194.039 Intelligent Audio and Music Analysis
101.507 Numerics of partial differential equations: stationary problems
- 120.109 Topographic Models
360.239 Introduction to Computational Science and Engineering
105.736 Introduction to Python for Interdisciplinary Mathematics