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Binder

Introduction to Data Science using Python

This repository contains teaching materials for a 3-day workshop on using Python for data science.

Computational environment

This workshop is run using the Anaconda Python distribution. A conda environment to run all the materials can be created using

conda create -n ds_python python=3.7 anaconda

The packages plotly and altair are needed for one document, but are otherwise not covered in the materials.

Teaching materials and documents

Long-form manuals for the material covered in this workshop are available in the top folder, and administrative materials for running the workshop are in the workshop_documents folder. The workshop is conducted by live-coding, choosing select materials in sequence form the long-form manuals. This can be adapted depending on the audience and the available time.

The teaching materials are stored primarily as synchronized Python and RMarkdown files. These seem an interesting choice for a Python workshop, but it enabled me to use RStudio, bookdown and some of it's editing tools as my IDE when needed. These materials can also be synced to Jupyter notebooks using the jupytext package (see below). I wrote the materials with a combination of RMarkdown and Jupyter notebook, synced using jupytext, which was a nice workflow for me.

These materials can also be consumed as live notebooks using the Binder link above. This connects this repository to Binder, where the RMarkdown files are converted to Jupyter notebooks on-the-fly and deployed as live notebooks on the web.

Converting materials to Jupyter notebooks for local consumption

All the RMarkdown files can be converted to Jupyter notebooks using the jupytext Python package. To do this, first install the jupytext package.

conda install -c conda-forge jupytext

Then, from the terminal, go to the docs folder and type

jupytext --sync *.Rmd

This will generate all the corresponding Jupyter notebook files. Now you can edit either the notebooks or the Rmd files, and it will be reflected in the other. See the jupytext documentation for more details.