hssay

Thanks for the amazing work! This is a project that many would look forward too. Shell is a place where Clojure's program to abstractions and data oriented programming philosophy can do wonders.

Ability to browse documentation of Clojure functions at the shell might help. Currently, man pages can be accessed for utilities. But quickly browsing doc of, say, Clojure re-find will add value, although features from stage 2 and 3 of roadmap are more important/show-stoppers.

Yup. This is the holy grail!

ipython shell with its magic commands is pretty powerful, although not a bash replacement. For example, you can assign output of ls command to a variable (e.g. a = !ls) and then use that variable as a python list.

Good overview of its power here: https://jakevdp.github.io/PythonDataScienceHandbook/01.00-ip...

Thanks for the excellent article.

One more interesting thing I have observed in data projects failing: organizations culture around data and the gap between data science team and engineers. Say, you have 2 top notch data scientist who know enough (stats, markov chains, algorithms and so on..). But let us say an average engineer in the organization doesn't know even a bit about A/B testing or difference between building a machine learning model Vs. obtaining predictions from already built model. Then no matter how good your so called data scientist are, the end result in terms of product or solution delivery is always sub-optimal. If the engineers and data science teams can't speak a common language, the result is always disastrous. Note that the gap is specifically about understanding data analysis as a domain.

The efforts to narrow down this gap must be driven by the lead data science member or CTO. Something like 'data bootcamp' mandatory for every new joinee can help. I had read about Facebook having such a bootcamp mandatory.