Jupyter Tutorial

Jupyter notebooks are growing in popularity with data scientists and have become the de facto standard for rapid prototyping and exploratory analysis. They inspire experiments and innovations enormously and as well they make the entire research process faster and more reliable.

“A spike in Jupyter Notebooks use shows that open source underscores a growing community, especially as Python surges to become the most used language … Since 2018, we have seen the use of Jupyter Notebooks steadily grow—and that growth surged in 2022 as research and experimentation with generative AI and machine learning took off. Since 2022, Jupyter Notebooks usage … has spiked more than 170%. And since last year, usage has increased by 92%. Data scientists and machine learning researchers commonly use the open source application for machine learning, data visualization, and more.”

– Octoverse: AI leads Python to top language as the number of global developers surges

In addition, many additional components are created that expand the original limits of their use and enable new uses.

However, the Jupyter tutorial is only part of a series of tutorials on data analysis and visualisation:

• Python basics

• Python for Data Science

• PyViz Tutorial

• cusy Design-System: Data visualisation

All tutorials serve as seminar documents for our harmonised training courses:

Duration	Topic
3 days	Introduction to Python
2 days	Advanced Python
2 days	Design patterns in Python
2 days	Efficient testing with Python
1 day	Software documentation with Sphinx
2 days	Technical writing
3 days	Jupyter notebooks for efficient data science workflows
2 days	Numerical calculations with NumPy
2 days	Analysing data with pandas
3 days	Read, write and provide data with Python
2 days	Cleanse and validate data with Python
5 days	Visualising data with Python
1 days	Designing data visualisations
2 days	Create dashboards
3 days	Versioned and reproducible storage of code and data
Subscription of 2 hours per quarter	News from Python for data science