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# System prepended metadata

title: Berlin Meeting 2/27/20 - Workplan Workgroups
tags: [temporal]

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tags: temporal
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_This document is temporal: it is not meant to be updated after 2020-02-27_

# Berlin Meeting 2/27/20 - Workplan Workgroups


## Team Nils

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## Team Nick

 - Clojure will not replace existing solution
 - Be able to use Clojure features for data science: importance of the REPL, pragmatism and symbiotic approach
 - Glue between existing things
 - A kind of effect to what clojurescript did to frontend development: bring sanity, stability and fun experimentation

## Team Ben

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## Team Santiago

- default version of a data frame, something that is good enough for beginners
- tutorials with basic steps of data science loading data, doing basic ETL and viz and finally serve models
- examples of data sets from small to large with good practices about how to work with them in clojure and in a functional way

## Teodor analysis (2020-02-27)

I'm getting the impression that there's a strong consensus for the value of tutorials / examples. We seem to be hearing this at every turn of the road. Specific down-into-the-mud guides on how to do things.

Judgement: I think we'll have to put some thought into how we'd like to organize and structure tutorials. Some aspects I'd find to be important:

- Do we differentiate on quality? Do we separate what we consider to be "really good" from what we consider to be "work in progress"?
- We might have conflicting goals for the tutorials/experimentation:
    - Goal #1: library exerimentation. Does this even work? Is the API good? Is the library good? Is this something that we can actually recommend?
    - Goal #2: education. If we want to host workshops, or enable workshops in any sense, I believe that the quality of the tutorials will be of utmost importance. This is _not_ the place for half worked-through guides, in my opinion.
- How do we index and organize the tutorials?
    - Possible categories: visualization, data munging/cleaning, machine learning, writing performant code