Practicalli Clojure Data Science should be a series of practical step by step tutorials show how to apply and adopt the more specific Clojure data science tools like tablecloth, etc. and how they compare to more general Clojure tools and libraries.
Aim:
provide a smooth transition for existing Clojure developers to move into data science projects.
Identify Clojure core functions and libraries used for general data transformation and data scraping
clojure.core functions
data inspectors for navigating and visualising data during development
general clojure libraries
scraping (enlive, etc.)
visualisation, ascii plots, etc
Discuss why data science has specific libraries and tools for data transformation
Identify the data science specific libraries
tablecloth, dtype-next, etc
visualisation - oz, hanami, etc
python integraition
R integration
Initial sections
installing Clojure and data inspection tooling (Clojure CLI, practicalli/clojure-deps-edn for Rebel Readline, Portal, Reveal and general tooling for Clojure projects)
data wrangling with the clojure.data tools and the basics of tablecloth - including some idea when to use each of these
REPL driven development in Clojure data science
using the REPL, data inspectors (Portal, Reveal, Cider inspect) to help with data wrangling
using notespace and NextJournal to create a visual notebook for a data science project
visualising the results with asciigraph, Oz and perhaps Hanami too (and if so, some suggestions as to when to use Oz or Hanami)
testing, CI, collaborative coding, packaging and deployment of Clojure Data Science projects
Ideally the guide would consist of 2-3 practical step by step tutorials. Each tutorial would either build on a previous one or cover a different aspect of data science (although probably not anything too deep into the science). This would complement much of the work that has been done in the Clojure data science hand book and perhaps enable more people to contribute to that handbook in the long run.
Practicalli Clojure Data Science should be a series of practical step by step tutorials show how to apply and adopt the more specific Clojure data science tools like tablecloth, etc. and how they compare to more general Clojure tools and libraries.
Aim:
Identify Clojure core functions and libraries used for general data transformation and data scraping
Discuss why data science has specific libraries and tools for data transformation
Identify the data science specific libraries
Initial sections