This is a hands on workshop. It is better if you start coding along with me during the workshop, experiment bugs and try to understand your errors. Learn by doing and avoid copy/pasting.
Feel free to ask questions at any time during the workshop.
The workshop assumes no prior knowledge of the Python programming language. Familiarity with R, vectors, data frames, and basic statistical analyses, such as linear regression is helpful but not required.
- Students are expected to have a computer or a laptop to follow along the coding section of each session.
This workshop is meant to provide a brief introduction to Python with a focus towards Data Science and Reproducible Research. The workshop is divided into three parts: The first part provides an introduction the Python programming language. The second part of the workshop will guide you through data acquisition, cleaning and exploration. In the third and last part, you will learn how to train, save, load, and make predictions your own machine learningmodels.
- Minimal Package Structure
- Project Templates
- Packaging and Dependency Management with Poetry
- Guidelines to document your code
- Guidelines to choose a licencse
Science should be open and shared. This workshop is inspired and built on top of other open licensed material, so unless otherwise noted, all materials for this workshop are licensed under the Creative Commons Attribution Share Alike 4.0 International License.
The source for the materials of this workshop is on GitHub at mickaeltemporao/reproducible-research-in-python.
For any follow-up questions:
- twitter: @mickaeltemporao
- email: mickael [dot] temporao [at] gmail [dot] com