E78 Data Science with Daniel Slutsky

Daniel on Twitter - https://twitter.com/daslu_
Daniel on GitHub - https://github.com/daslu
SciCloj - https://scicloj.github.io

Study Group - https://scicloj.github.io/docs/community/groups/

processing tables:
https://github.com/techascent/tech.ml.dataset
github.com/scicloj/tablecloth
processing arrays:
https://github.com/cnuernber/dtype-next (should have mentioned)
https://neanderthal.uncomplicate.org/
processing nested, unstructured data:
https://github.com/clojure/core.match
https://github.com/noprompt/meander
https://github.com/redplanetlabs/specter
math and stats:
https://github.com/generateme/fastmath
https://github.com/MastodonC/kixi.stats
data viualization libraries:
https://vega.github.io/
https://github.com/generateme/cljplot
https://github.com/jsa-aerial/hanami
classical machine learning:
https://haifengl.github.io/ (wrapped in scicloj.ml)
deep learning
https://github.com/scicloj/clj-djl (wrapped in scicloj.ml)
https://github.com/uncomplicate/deep-diamond
machine learning workflows:
https://github.com/scicloj/metamorph.ml
the underlying pipeline notion:
https://github.com/scicloj/metamorph
wrapping all most of those machine learning libraries and workflow together:
https://github.com/scicloj/scicloj.ml
processing tables with spark:
https://github.com/zero-one-group/geni
should have mentioned:
https://github.com/clj-python/libpython-clj
https://github.com/scicloj/clojisr

Video Courses:
https://clojure.stream
https://www.learnpedestal.com/
https://www.learndatomic.com/
https://www.learnreitit.com/
https://www.learnreagent.com/
https://www.learnreframe.com/
https://www.jacekschae.com/

Episode source