# Kotlin Program to Calculate Standard Deviation

#### In this program, you’ll learn to calculate the standard deviation using a function in Kotlin.

This program calculates the standard deviation of a individual series using arrays. Visit this page to learn about Standard Deviation.

To calculate the standard deviation, `calculateSD()` function is created. The array containing 10 elements is passed to the function and this function calculates the standard deviation and returns it to the `main()` function.

## Example: Program to Calculate Standard Deviation

``````
fun main(args: Array<String>) {
val numArray = doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0)
val SD = calculateSD(numArray)

System.out.format("Standard Deviation = %.6f", SD)
}

fun calculateSD(numArray: DoubleArray): Double {
var sum = 0.0
var standardDeviation = 0.0

for (num in numArray) {
sum += num
}

val mean = sum / 10

for (num in numArray) {
standardDeviation += Math.pow(num - mean, 2.0)
}

return Math.sqrt(standardDeviation / 10)
}``````

When you run the program, the output will be:

`Standard Deviation = 2.872281`

In the above program, we’ve used the help of Math.pow() and Math.sqrt() to calculate the power and square root respectively.

# Special 95% discount

## Two Machine Learning Fields

There are two sides to machine learning:

• Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
• Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

`Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  `