Based on the skills that you wish to focus on, you may leverage freely available online resources to help build your curriculum. Building a curriculum is more important for the Data Clubs than it is for the Data Clinics, as the Data Clinics are informed by specific topics that the participants would like to discuss. Nonetheless, some useful resources are:
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The “EpiR Handbook” is a free online R learning resource developed by Applied Epi, a non-profit organisation strengthening epidemiological practice through training, tools and support: https://epirhandbook.com/en/
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Additional reading on using R for Data Science Welcome | R for Data Science (had.co.nz)
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TGHN has a collection of resources on R as well as ethics of data science in global health research: https://globalhealthdatascience.tghn.org/hub-resources/
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Coursera offers an introductory Python course for free, from the University of Michigan. It’s offered in several languages, although not all content is translated: https://www.coursera.org/specializations/python
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This is an introduction to the command line/Unix, which is often used for bioinformatics or genomics applications of data science methods: https://codethechange.stanford.edu/guides/guide_unix_commands.html
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This online textbook offers a free introduction to broad topics in statistics, and includes helpful exercises for the user to follow along: https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Introductory_Statistics_(Shafer_and_Zhang)/01%3A_Introduction_to_Statistics
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GitHub is a popular tool used by most data scientists - there is a great skill-development course on GitHub itself: https://skills.github.com/
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Google has a free crash course on machine learning, covering various applications – it has video lectures explaining each topic: https://developers.google.com/machine-learning/crash-course/ml-intro
These are just a few free resources you may wish to use to build your curriculum. You should cite and credit them as needed.
It is important to identify topics of interest prior to outreach and participant registration, as this may directly impact who registers. Once topics are decided, subject matter expects should be identified to help facilitate the sessions. We recommend 1-2 subject matter experts to facilitate the sessions, along with 2-3 subject matter experts to sit with the participants and help answer questions as the sessions go along, depending on the class size - see next section for details.
We also recommend creating a slide deck for each Data Club session. For the Data Clinic sessions, a slide deck isn’t necessarily needed. In-class demos are also helpful, for both Data Clubs and Data Clinics, alongside some take-home exercises for participants to spend more time with after the sessions. In addition, where possible, we encourage leveraging online courses (like the ones listed above) to support learning. That way, you can offer a blended learning methodology that combines synchronous lectures (either in-person or online) with the completion of asynchronous e-learning courses or toolkits offered by the TGHN training centre or partner e-learning platforms.
All course material should be uploaded to a shared folder that participants have access to. Participants should be encouraged to download all the materials on their own laptops and follow along as the sessions progress.
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