Fig 1.
A new Python 3 notebook with 3 empty cells denoted by the grey rectangles.
The currently selected cell is highlighted in green.
Fig 2.
A simple function that returns the value of the sum of 2 numbers showing different kernels (programming languages) in the notebooks.
(Left) Python, (middle) Julia, and (right) R.
Fig 3.
Example of a markdown cell (left) and the output of the styled cell when the cell is run (right).
Fig 4.
Output of LaTeX math notation producing the formula for the population standard deviation.
Table 1.
Some useful Python libraries for numerical and scientific computing.
Fig 5.
The files and folders tab seen when launching Jupyter notebooks locally.
A new notebook is created by selecting the new dropdown option and choosing the required language.
Fig 6.
The NBextensions tab for selecting the various notebook extensions.
Fig 7.
Enabled notebook extension icons shown in red box.
Fig 8.
Line and cell magic’s used to add SQL functionality to a Python notebook.
SQL, Structured Query Language.
Fig 9.
Example of notebook interaction.
Fig 10.
An interactive drop-down list created using a Python list.
Fig 11.
Interactive plot generated with the “plotly” module that can be rotated and zoomed with individual data points selected.
Fig 12.
Example using the “exercise2” extension to create a task.
When the “show solution” button is pressed, the answer is displayed below.
Fig 13.
Example of the prescribing dashboard the teams would add functionally to following the Scrum framework.
Fig 14.
List of notebooks covering the various topics of programming with Python.
Fig 15.
Example of task from notebook.
Clicking the “Show Solution” button reveals the model answer.
Fig 16.
Example of notebook on variables and strings programming topics.
Fig 17.
Results of notebook student survey (n = 12).