Python for Data Course

This repository contains materials for an intermediate-level python course (delivery dates/information about self-taught options below).

Aim

The overall aim of this course it to:

1. introduce key Python libraries for data wrangling, data analysis and data visualisation within the comfort of the Jupyter framework
2. introduce concepts of code reproducibility and ways of sharing analyses with those without coding ability

Philosophy

Our general philosophy for this course is

1. teach in small chunks starting by introducing theory, demonstrating an example, working through a simple case and then setting an exercise. Each exercise is then gone through as a group.
2. teach through errors, error messages and documentation - so that trainees can debug their own codes after they leave the course
3. create a safe environment for asking any and all questions.

Contributors

• Chas Nelson
• Mikolaj Kundegorski

Self-taught On-line Version (12+ hours)

We have designed this course in such a way that it should be easy to follow and work through on your own. Each notebook stands alone and should provide you with all the information needed to complete the tasks (blue boxes) and exercises (yellow boxes).

In order to aid working through the notebooks we have provided short videos for all tasks and exercises. These videos provide complete answers for every task and should be viewed after attempting each task or exercise.

We suggest you work through each notebook in turn, attempting at least the tasks on your first run-through. You can then use the exercises to revisit and revise topics when you go through the notebooks again in the future. As with all languages, practice makes perfect.

In Person Course Delivery Dates (1 day course)

• 2022-08-12: Python for Data, Royal Society of Biology, London, UK

Developer Information

When taught in person we recommend getting participants to install Anaconda (see above); all the dependencies are pre-installed this way. However, for developing (and presenting) the course we use pipenv to ensure that we're using the same version of packages as Anaconda but without having to pollute our own computers with a tool that we find great for using to teach but not great for real life.

To install this repository into a clean system (assuming you have pipenv installed):

pipenv install --dev

To run Jupyter Lab:

pipenv run lab

To present notebook 01_running-python.ipynb:

pipenv run present notebooks/01_running-python.ipynb