{"id":8823,"date":"2020-07-07T06:40:28","date_gmt":"2020-07-07T06:40:28","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=8823"},"modified":"2023-11-30T09:06:44","modified_gmt":"2023-11-30T09:06:44","slug":"is-javascript-emerging-as-a-mainstream-big-data-language-in-2020","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/is-javascript-emerging-as-a-mainstream-big-data-language-in-2020\/","title":{"rendered":"Is JavaScript Emerging as a Mainstream Big Data Language in 2020?"},"content":{"rendered":"\t\t
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Talented programmers deserve a lot of credit for recent advances in big data technology. They take a very specialized approach to this fascinating science. In order to create cutting edge big data projects, developers need to use the most appropriate programming languages.<\/p>\n\n\n\n

Python has long been one of the most popular programming languages for creating big data projects. However, some less popular programming languages can also be highly effective for data science applications. One programming language that is often overlooked among data scientists is JavaScript.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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JavaScript is not the most robust programming language. As a client-side scripting language, it clearly has some limitations that might render it imperfect for data science applications. However, there are some important benefits it provides. It is even useful for many machine learning applications<\/a>.<\/p>\n\n\n\n

Developers should consider utilizing JavaScript for data science projects. It currently doesn\u2019t have quite the same level of functionality, due to fewer libraries and support forums. However, this will change as more data scientists discover the benefits and expand the base of knowledge and resources in the JavaScript environment. Data scientists should understand the benefits and the steps that they can take to integrate it into their framework.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The number of developers using JavaScript will probably grow as these benefits are discovered. Data scientists can use resources like Redux for React training<\/a> to get the most of this client-side language.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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What are some of the reasons JavaScript can be useful for data scientists?<\/h2>\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Peter Gleeson, a founder of Revolut, discussed the pros and cons<\/a> of using JavaScript for data science projects. When you look at them side-by-side in contacts, it can easily seem like the benefits outweigh the drawbacks. His point about the drawbacks center primarily around the opportunity costs of taking the time to learn a new language, as well as the lack of toolkits that have been made by other developers. These issues can be worked around though, because there is no reason that JavaScript developers can\u2019t create their own tool kits from scratch and share them with others through open source code. Gleeson did make a good point about the lack of multithreading capabilities, but this is not necessarily a dealbreaker.<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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There are a lot of benefits that overshadow the drawbacks. Please include the following:<\/p>\n\n\n\n