Kotlin for Data Science
summary
Argues Kotlin closes the gap between data science and production code by using JVM libraries and concise syntax, shows an ojAlgo linear solver scheduling model that assigns three drivers and reports a $936 minimum-cost schedule, demonstrates a Kotlin-Statistics library that does k-means clustering and grouping/ binning (counts and average defect rates), notes interop with Apache Spark, Krangl and ND4J, and builds a small TornadoFX desktop UI to visualize clusters and tables.
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chapters (19)
- 0:00Introduction
- 1:55What is Data Science
- 7:00Challenges in Data Science
- 11:55Why Kotlin
- 13:00One Language One Code
- 13:37Kotlin vs Python
- 14:04Dynamic typing
- 14:47Static typing
- 15:12Data science libraries
- 16:15Scala
- 18:40Weaknesses
- 21:18Strengths
- 24:28Examples
- 24:42Linear Programming
- 24:58Example
- 26:02Math
- 31:13Applications
- 32:31More examples
- 39:18Summary
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