This course will introduce researchers to Jupyter notebooks as a tool for conducting research and for communicating their work.
The Jupyter ecosystem is a rich environment for interactive computing which provides intuitive and powerful tools for data-analysis, computation and collaboration. We will explore the features of the jupyter notebook and the Python(3) programming language. Specifically, we will look at:
- Notebooks: Markdown, Code, Output, widgets, %magics
- Python: Types, Structures, Loops, Control Flow, Logic
- Some useful modules
- numpy
- matplotlib
- pandas
- Others (not covered here)
- beautiful soup
- domain specific: astropy, nltk, ...
- Examples
- Useful links:
- A gallery of interesting Jupyter Notebooks
- mybinder.org Turn a Git repo into an interactive notebook
- Jupyter Blog
- Python Data Science Handbook
- JupyterLab (part of) the future for Jupyter
We will use some popular Python packages (Pandas, Numpy, Matplotlib, ...) to work through a series of examples and exercises to see how they can be used to work with external data sources, produce insights and visualizations and help create compelling showcases of your work.
This course does not have any firm requirements, but some familiarity with Python would be helpful. I recommend the following order
Skipping any subjects you're already familiar with.