Most famous among them are Scala, Python and R. Since I am working with Scala at work, I would like to share some of the most important concepts that I come across and worthy for the beginners in Data Science Engineering. In Scala 2 we used to heavily rely on implicit conversions to add methods and on implicit parameters to propagate instances, which was a bit cryptic to beginners who were maybe already struggling with new concepts in functional programming. It has become mainstream and the most in-demand … The Scala ecosystem has matured considerably and rather quickly over the past years. Spark is an extension for Hadoop which does batch processing as well as real-time processing. This is Recipe 13.9, “Simple concurrency with Scala Futures.”. The picture below shows a snapshot of the most popular tools and frameworks written in Scala. Sure you can learn some basic Scala and jump right onto using these tools. In this tutorial, we will highlight the main features that you will benefit from when adopting the Scala language. If you are coming from a pure Object Oriented background, hearing a lot of buzzwords around functional programming may at first be a bit scary. But in here methods are automatically coerced to functions if the context requires this. I'm pretty sure you must be familiar with Albert Einstein infamous E = MC² equation. In Scala 3, this pattern becomes simpler thanks to the new syntax. A Data Science Engineer should have a strong programming knowledge as well as knowledge in Database. Since it involves a good research, normally a PhD or M.Sc in Data Science is required to get the job done. I hope you are as excited as I am :), So what is a function? The Scala List class is very different from the Java List classes -- including the part where it’s immutable -- and although the Scala Array is an improvement on the Java array in most ways, it’s not even recommended as the “go to” sequential collection class. They’re described in depth in Recipe 9.1, but here’s an example of the long form for an anonymous function: As shown in Recipe 9.1, that long form can be reduced to the following shorter form: That doesn’t look like much by itself, but when it’s combined with the filter method on a collection, it makes for a lot of power in just a little bit of code: This is a nice lead-in into the third topic: implied loops. Being an Object Oriented programming language, you have the usual type hierarchies as you'd expect from an Object Oriented language. Note:- In Scala methods and functions are different concepts. As we will see in upcoming tutorials from Chapter 2, Scala highly encourages immutability as a first class citizen in the language. This book is on our 2020 roadmap in collaboration with a leading data scientist. Side effect is a common buzzword in functional programming and it simply means that when defining a function it is important that the function does only what it is meant to do . Then, regardless of where you are in your thousands of lines of code, you know that the value of your cupOfCoffeeEnergy will be exactly the same! Data Science is an interesting field to work in, a combination of statistics and real world programming. Apache Spark is considered as a powerful complement to Hadoop, big data’s original technology.Spark is a more accessible, powerful and capable big data tool for tackling various big data challenges. They are an abstraction, encapsulation, inheritance, and polymorphism. So we can use lazy val instead which denotes a field that will be calculated once it accessed for first time and then stored for future references. As we've already mentioned from Steps 1 and 2, we should strive to encapsulate functions in a strict mathematical sense. Three important concepts. In doing so we end up with a bunch of functions that can be freely mixed in with other functions to compose even more functions.


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