Instructor Notes

The content of this course might be provided following different schedule, in parts or in a different order. Here we list different possibilities

Teaching schedules

Six times one hour.

1. Introduction: what is this course, what are the motivation to include data science practices in research, what do we mean with data science practices.
2. Reproducibility, provenance and version control.
3. Setting up a project and its management tools.
4. Research data management in a computational project.
5. Code quality control.
6. Publication and open science.

One day workshop (8h)

Following the blocks above, but adding ice breaking and review exercises.

Advertise the course

Content of the introduction part (chapter 1-3) may be reused to give a 10 minutes overview of the objectives.

• It might be best to start a workshop with the chapter 2: motivations.

Introduction to this course

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Better and faster research !

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What is special in data science project ?

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Reproducibility

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An introduction to version control

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Setting up a computational project

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Implementing tools and methods during the project

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Research Data Management

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Fostering documentation

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Scientific rigour with code

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Coding basics

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Code testing and Review

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Code Modularity

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Publication and release

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Open Science Practices

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Data and code citation

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