[Q] Learning ML for a primarily classical statistician

I am in the 2nd year of a biostats MS and I notice as I am applying to jobs that my lack of python and the “hot/in” ML/data science knowledge is lacking. I don’t know how to “put models into production” or use Unix/Linux and the cloud. Nor do I know web scraping, SQL, etc.

Do I absolutely need to get some baseline software engineering skills and how do I do that? How do I get some ML knowledge from the stats perspective?

For example I know nothing about parallel computing, unstructured data, computer vision, bioinformatics etc. These are the sorts of things biotech companies want these days and my CS knowledge is weak. Beyond arrays/if stmts/for loops/functions I don’t know anything.

How did you gain this knowledge coming from the stats side?

I know cross validation, using LDA/QDA, logistic regression, and have a basic idea of using kNN. Essentially a basic knowledge of classification and training/test stuff. Also tried CART and hierarchical clustering recently. The latter I couldn’t even interpret. But beyond this I don’t know anything.

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Nevin Manimala

Nevin Manimala is interested in blogging and finding new blogs https://nevinmanimala.com

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