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Kotlin for Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, and systems to extract knowledge and insights from structured and unstructured data. As data science is becoming more and more important in today's world, the need for a powerful, flexible, and easy to use language is increasing day by day. Kotlin, being a statically typed, modern, and expressive language, can be a great choice for working with data.

In this article, we will explore how to use Kotlin for data science, from reading and manipulating data to performing basic statistical analyses.

Setting Up the Environment​

Before we start, you need to have Kotlin installed on your machine. Kotlin can run on Java Virtual Machine (JVM), which means you need to have Java installed as well. If you have not installed Kotlin, you can follow the official Kotlin installation guide.

Working with Data in Kotlin​

Kotlin provides a number of libraries that make it easy to load, manipulate, and analyze data. One such library is Kotlin Statistics, which is a Kotlin multiplatform library for exploratory and production statistics.

Loading Data​

Let's start by loading a CSV file. To do this, we can use the csvReader function from the kotlinx.serialization.csv library. Here is a basic example:

import kotlinx.serialization.csv.Csv

val data = Csv.decodeFromString<List<Map<String, String>>>(csvData)

Manipulating Data​

Once we've loaded the data, we can manipulate it using Kotlin's built-in collection functions. For example, we can filter rows, select specific columns, and perform operations on the data.

val filteredData = data.filter { it["age"]?.toInt() ?: 0 > 30 }
val ages = data.map { it["age"]?.toInt() ?: 0 }
val averageAge = ages.average()

Basic Statistical Analysis with Kotlin​

With the Kotlin Statistics library, we can perform basic statistical analysis on our data. Here are a few examples:

Descriptive Statistics​

Descriptive statistics provide simple summaries about the sample and the measures. These summaries may be either quantitative (i.e., mean, median, mode) or visual (i.e., charts, histograms).

import org.nield.kotlinstatistics.*

val ages = data.map { it["age"]?.toDouble() ?: 0.0 }
val meanAge = ages.mean()
val medianAge = ages.median()

Inferential Statistics​

Inferential statistics make inferences and predictions about a population based on a sample of data taken from the population. The sample ideally should be representative of the population.

import org.nield.kotlinstatistics.*

val ages = data.map { it["age"]?.toDouble() ?: 0.0 }
val agePopulationMean = ages.populationMean()
val agePopulationVariance = ages.populationVariance()

Conclusion​

In this article, we took a glance at how Kotlin can be utilized for data science. We learned how to load and manipulate data, and how to perform basic statistical analyses.

Although we only scratched the surface of what Kotlin can do in the field of data science, it should be clear that Kotlin, with its expressive syntax and powerful libraries, can be a great tool for data scientists.