- Boring data science with kixi.stats
- Data Science up and down the Ladder of Abstraction
- Do We Really Need Dataframe in Clojure?
- Think Stats in Clojure: Part I Parsing the Data
- Think Stats in Clojure Part II: Cleaning and Augmenting Data
- Think Stats in Clojure Part III: Exploring Data
- Doing data science with Clojure: the ugly, the sad, the joyful
- https://cbds.netlify.com/2017/10/12/data-analysis-with-clojure/
- https://www.slideshare.net/simonbelak/doing-data-science-with-clojure-64130324
- dev: https://github.com/clojupyter/lein-jupyter
- viz: https://github.com/metasoarous/oz
- higher-level viz: https://github.com/jsa-aerial/hanami
- https://github.com/MastodonC/kixi.stats
- https://github.com/incanter/incanter
- https://github.com/sbelak/huri
- https://github.com/gigasquid/clojure-mxnet
- https://github.com/techascent/tvm-clj
- Python/R are de-facto kings of data science world, and are great. I want to learn Clojure, and I'm a data engineer/scientist
- What can Clojure offer in the workflow that has the edge over Python/R?
- take R for Data Science book, and try implementing first couple of chapters in Clojure. Vega lite should map to it really well
- take inspiration from https://statcompute.wordpress.com/category/clojure/page/1/, things like how to transpose columns, update keys etc
- pick a section in https://chrisalbon.com/#python, then start translating the exercises in Clojure
- prep: need to create a simple Hugo site with a way to publish Clojure jupyter notebooks