About

What will you learn?

You will learn how to complete an outbreak analytic task following a practical step-by-step guide to start your own journey.

Who are these lessons for?

We thought about Lucia, Patricia, and Anya as learner personas in designing the contents of this training:

  • Lucia, a Field Epidemiologist in a National Health Agency that uses R for data cleaning, plotting and automated reports.

  • Patricia, A PhD student learning to use R and analyse Outbreak data for her research project.

  • Anya, a Professor who needs ready-to-use teaching material for her research and to pass on to students.

What design principles we follow for these lessons?

This section aims to capture the decisions about why a material is the way it is.

  • A Tutorial documentation format
    • Easy to consume in a self-paced manner,
    • Be self-explanatory,
    • Show common mistakes and misconceptions, and
    • Write assessment exercises with diagnostic power for those common misconceptions.
  • Add links to related Explanation documentation.
  • Show the outbreak analytics pipeline approach connecting common policy questions with analysis tasks, data inputs and outputs.
  • Order the content to promote motivation: first the content that requires the less time to master and most useful once mastered. Aligned with the datasciencebox design principles.
  • Facilitate the material maintainability. It should be cheaper to update than to replace it.
  • Use the lesson folder structure from The Carpentries workbench, designed accordingly to their design principles.
  • Facilitate a multimodal experience:
    • Create visuals to explain related concepts. Vision gathers the most information in the short term memory
    • Create slides or other teacher document (e.g. visual qmd files) to facilitate it’s reuse by other instructors for in-person workshops or online trainings.
    • Create interactive videos to create a sense of presence.
    • Add an interactive chatbox for effective one-to-one timely feedback.
    • Use callout blocks for complementary info and refer to existing materials from the epidemiology and data science training community: reconlearn, appliedepi, graphnet, rstudio, stackoverflow, github issues and discussions.

What is not included in this material?

Topics that are out of the scope of these lessons include:

  • How to use Git and GitHub to contribute in Open science projects.

  • How to create a reproducible analysis project.

  • How to build R packages for data analysis tasks.

Materials you will need

We will use R. For this you will need to use these instructions:

How to contribute?

Improvements and additions very welcome, and can be made through the GitHub repository.

Any questions?

If you need any assistance installing the software or have any other questions about the training, please send an email to

References