And with Windows 11, Android apps will be on the OS that runs on most desktop and laptop devices. It’s the OS that runs on most mobile devices. Kotlin not only has the speed advantage, but it also has a platform advantage: Android. Python and R might have a head start, but that doesn’t mean you can’t use Kotlin for data science. It will run circles around both Python and R. As a JVM language, it’s compatible with the galaxy of Java libraries developed for and used by its base of millions of developers. Like Python, it has a concise and readable syntax, and it’s far easier to learn and less verbose than Java. There’s no reason Kotlin, too, can’t be a good language for data science. Python’s easy-to-learn and readable syntax as well as its vast ecosystem of data processing and analysis libraries (especially pandas and NumPy) and R’s standing among the statistics-minded and its Tidyverse data science packages have made them data science mainstays. Right now, when you bring up the topic of data science, the Python and R programming languages come up most often. You’ll also learn about a data structure you can use to do data science with Kotlin. In this article, you’ll learn the basics of Jupyter Notebook and how to turn it into an interactive interpreter you can use to learn Kotlin, as a scratchpad for your Kotlin ideas and experiments or as your annotated library of code snippets. In this article, you’ll set up a Kotlin playground that runs locally on your computer! If you’ve ever wished for a Kotlin version of a REPL (read-evaluate-play loop) like the ones in command-line Python or Node.js or for a lightweight local version of the Kotlin Playground, there’s a way to do it: by using Jupyter Notebook with a Kotlin kernel.
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