clojure-style-guide VS PythonDataScienceHandbook

Fixed link to that style guide entry: https://guide.clojure.style/#opt-commas-in-map-literals

Per that style guide, the above map would be formatted like this (on HN, just indent by two spaces):

  {:a 1

As for the broader question of Clojure style, there are style guides like https://github.com/bbatsov/clojure-style-guide and tools like clj-kondo to help learn and reinforce important practices.

Basically, you learn the expected places to put whitespace, make sure to edit your code accordingly and all of the parens will be automatically closed and adjusted. Using parinfer—which you can also combine with the more traditional paredit—makes writing Clojure code a lot like writing Python.

Also, there are guidelines, the styleguide, clj-kondo, kibit etc. And if you don't review your interns/juniors code to teach them good practices - you're doing it wrong (well, this one is true for any practical PL out there).

Another good resource is https://guide.clojure.style/ -- the (unofficial) community style guide for Clojure.

It's not an answer to your question but i can refer you to https://github.com/bbatsov/clojure-style-guide

The same could be said about the "Clojure Style Guide" from the Cider guy. As a matter of fact, there was an issue about it that was quickly declined https://github.com/bbatsov/clojure-style-guide/issues/232

Formatting is not that great, see https://github.com/bbatsov/clojure-style-guide btw

Python Data Science Handbook

Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data

I don't know what tools you will be using but if you will be using Python you can start with Python Data Science Handbook by Jake VanderPlas and Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataData Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data which gives a very good outlook on the data science and big data frame work. PS: Jake's book is also available as jupyter notebooks so you can read and run the code at the same time.

Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)

Otherwise you can work through courses on Datacamp, Coursera, Udemy, etc, or check out this book for a more general non-spatial perspective.

7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.

I can recommend https://jakevdp.github.io/PythonDataScienceHandbook/