Bootcamps needed

[hlapp]

There's (intentionally!) a diversity of level of expertise among us with the various technologies we will need to apply during the workshop to be productive. A common mechanism we use to get everyone up to speed on the technologies they need to be is that some of those proficient in a technology give a short bootcamp (~30 minutes) to those who need to know more about it given what they are planning to do.

Hence, by definition we can't know exactly what bootcamps we'll need to organize until some time into the workshop, but I'm opening this thread here to collect input on (a) bootcamps need you already anticipate, and (b) who could offer a bootcamp in a technology that they anticipate to be useful.

(I'm using the word technology broadly here, in a sense that includes approaches, techniques, protocols, methods, etc.)

Some things that come to mind to start the discussion:

• Formulating learning objectives (@ErinBecker is already planning, see Lesson on writing learning objectives #5)
• Using Github and/or Git?
• How best to install / run Jupyter? (see Installing Jupyter and Openning Jupyter Notebooks #6)
• How to make slides?

[bridgethass]

As a a newbie to Python, Jupyter, and Markdown, I think these topics might be useful for beginners:

• Jupyter Notebooks Basics: Possible topics to cover:

1. Tour Jupyter: Help > User Interface Tour
2. Getting Started: Notebook Basics
3. Explain the Different Cell Types: Code, Markdown, nbconvert
4. How does Jupyter differ from other Python Platforms?
5. Useful Shortcuts: eg. jupyter-notebook-tips-tricks-shortcuts/

• Markdown Syntax
  Some links I found useful for learning Markdown include:

1. Working With Markdown Cells
2. Markdown CheatSheet

[elliewix]

For the github/git, I strongly suggest checking out GitHub desktop. It is super light weight, but gets all the essential version control stuff done in a pretty easy GUI. For those who need a more git centered github bootcamp I suggest it as a platform, and a bootcamp to cover the essentials can be done easily in 30 min.

[mpacer]

I'd suggest at least one additional jupyter module separate from the module on the using ui of the notebook (the above "Notebook Basics"). I'm not sure how to organise it though, it's sort of miscellaneous Jupyter topics some that are specifically related to being a scientist interested in reproducible research some that are more general.

This would include:

• installing a non-(i)python kernels (e.g., R seems especially important)
• programmatically archiving project and analysis history (e.g., checkpoints & git)
• clearing a notebook’s output cells from both the UI and the command line (which eliminates data URIs and so will ease the use of git, see git vs Jupyter #4)
  • advanced: there might be a place here for something like git (clean & smudge) filters so it's automated
• customising the notebook
• manually converting notebooks & using content (e.g., for easier use in publications)
  • advanced: shifting code out of notebooks into reusable modules
• cross linking within notebooks (and possibly between notebooks using bookbook)
  • advanced: cross linking code between notebooks using ipynb
• citations in a notebook (as of today only via zotero)
• advanced: automation for including results in publications using LaTeX (e.g., w/ nbflow)
• advanced: having python2 and python3 available (e.g., using environments to use software compatible with only one or the other)
• techniques for sharing notebooks
• working with collaborators who don't want to change their toolsets

I'd already started writing here but these kinds of issues would seem related to #15 as well.