The most interesting findings are on the backgroun...
# datascience
The most interesting findings are on the backgrounds of data science professionals imo. I wish the technology and methodologies got more coverage. I've been thinking a lot about priorities and what to propose at KotlinConf this year. I feel that for Kotlin to be successful on the whole "data science" domain, the emphasis should be on "production readiness" more than anything else. We really cannot win mindshare of people who don't value that, and they likely will stick with Python, R, and Julia. But there are many people that value creating products and not just models. Those are the folks we want, and they understand Kotlin can't be like R or Julia for pragmatic reasons. Kotlin/Native may be instrumental as well as it opens up an obvious platform to hack data analytics. For me personally, my year is going to focus heavily in using Kotlin for Operations Research and mathematical modeling. I was pretty enlightened by this thread discussing my article today, and I think it demonstrates a lot of perceptions on Kotlin being used for "data science" purposes
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