A quick guide for new members

[htcai]

Since there often are new members who join us, it seems a good idea to provide a quick guide for the machine learning work for Cognoma. The README.md file has been working as a guidance for setting up the Python environment. In addition, I think it is helpful to cover the following issues in the quick guide by either providing the code, or the link to the appropriate webpages:

• Install and configure Git, clone the machine-learning repository, etc.
• Which jupyter notebooks people can/should read and play around.
• The ongoing problems which we are tackling and the contributions people can make.
• ...

These things are for Cognoma members instead of outsiders. Therefore, they probably should be incorporated into a separate file than README.md. I would like to compose the file, as I have given a tour to many new members who are interested in the machine learning work for Cognoma.

[dhimmel]

I would like to compose the file, as I have given a tour to many new members who are interested in the machine learning work for Cognoma.

Fantastic -- documentation is an incredibly important contribution that is often forgotten. We'd love whatever help you'd like to provide.

[htcai]

@dhimmel Thanks for your prompt comment! I will start to compose the documentation after I submit my pull request for the sparsity of the mutation MAP2K1.

[KT12]

@htcai let me know if you need help with beginner documentation.

[htcai]

@KT12 Thanks in advance for your help! I plan to write a sketchy (and incomplete) draft, so that you and other people can also contribute to it.

[jruhym]

This tutorial is useful for conda. It takes ~30 min.
https://conda.io/docs/test-drive.html

[MichaelKheifetz]

Sorry if this is slightly off topic, is there any location where all the feature names are listed in something like a data dictionary or something of the like so its easy to look at?

[dhimmel]

Sorry if this is slightly off topic, is there any location where all the feature names are listed in something like a data dictionary or something of the like so its easy to look at?

@MichaelKheifetz thanks for your question. Would you like to open a new issue, so we can keep discussion organized?

[MichaelKheifetz]

Sure, I guess I wasn't sure how to word it properly in a new issue. I just wanted to have a better biological grasp of both what the features and target variables actually represent from a biology point of view....