"Clojure for Data Science in the Real World"
summary
Argues that Clojure already meets the practical needs of data science: libpython-clj and libjulia provide interop with mature Python/Julia ecosystems, the JVM delivers performance, and recent libraries (many released in the last 2–3 years) give near feature parity with Python/R. Highlights d-type.next for fast column operations and Calva notebooks for portable, reproducible namespace-driven workflows. Notes these tools work in production but need polish, documentation, and community adoption to scale.
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chapters (10)
- 0:00Start
- 2:25Clojure's data science story
- 7:26Interop
- 21:38What's missing?
- 22:07What's next?
- 25:15What is the tidyverse?
- 27:11First steps toward this end
- 29:04What can you do?
- 32:12New study group
- 34:05Financial support
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