Having a good grounding of programming language is a basic necessity to have when embarking upon the data analytics journey. Rather, there is a whole world of programming languages that you can pick up and implement in this field. Programming languages lay the basic foundation of data science, let us take a look at some of the most popular ones.
Scala stands for scalable language which is designed to express common programming standards in a short, elegant and precise way. The code written in Scala can be executed much and is known for its stability, flexibility, scalability and high speed. You can use Scala to develop innovative products that are part of Big Data.
Julia is a refreshingly modern, high-performance and meaningful programing language that is open-source. It is commonly used for data manipulation and scientific calculations. You can make use of thesci-kit-learn library stored in Julia to accelerate the transition. The major advantage being that of Julia being 10x-30x faster than that of Python and R combined.
Swift is an open-source, simple, and flexible programing language. It builds on the best of C language and Objective-C, without the restrictions of C compatibility. It is popular for its concise yet understandable syntax and lightning speed to run web apps. There is a range of libraries for executing tasks including numerical computation, digital signal processing, high-performance functions for matrix math, building machine learning models, applying deep learning methods, and more.
Go is yet another simple, reliable, and efficient software with a singular focus. There are several open-source tools, offers, and resources for executing data science applications using this programing language. This includes data organization, data gathering, arithmetic and statistical computations, data parsing, EDA and building machine learning models, and many more.
Spark provides advanced-level application programming interfaces (APIs) in Java, Scala, Python and R, as well as an optimized engine to aid the execution of graphs. It has a fast cluster computing framework with an open-source, which is used for processing, analyzing and querying Big Data. Another benefit of Spark over other big data structures is that it is formed based on in-memory computation. This facilitates computations to run up to multiple times faster.
Spark can handle data engineering tasks along with various data science processes such as exploratory data analysis, supervised learning, feature extraction, model evaluation, as well as building and debugging its applications.
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