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.
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.
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.
Developers should consider utilizing JavaScript for data science projects. It currently doesn’t 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.
The number of developers using JavaScript will probably grow as these benefits are discovered. Data scientists can use resources like Redux for React training to get the most of this client-side language.
What are some of the reasons JavaScript can be useful for data scientists?
Peter Gleeson, a founder of Revolut, discussed the pros and cons 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’t 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.
There are a lot of benefits that overshadow the drawbacks. Please include the following:
- A number of great data visualization features. There are a number of libraries that can be used for this purpose, which include D3.js and Chart.js.
- There are a number of product integration technologies, which enable developers to work closely with each other. JavaScript is clearly one of the most collaborative programming languages currently available, which is an important function of any data science development platform.
- Tensorflow is a great feature for machine learning. This feature was introduced in 2018. JavaScript wasn’t as versatile of a data science language before this new project was announced, but it offers some powerful benefits.
- JavaScript also provides some APIs that have made it a lot easier to work with large amounts of data.
This is a succinct list of the benefits of JavaScript for data development projects. There will probably be other benefits as well as new libraries are released.
What JavaScript tools can be useful for data scientists?
There are a number of libraries in tool kits that are useful for Data science projects. Some of the best are listed below.
Tensorflow.js
Tensorflow is a critical library for machine learning projects in JavaScript. It has some very sophisticated linear algebra algorithms and can easily be used for deep learning. Although it is far more recent than the counterpart with the same name in Python, it has already progressed to the same level of functionality.
Integrated Development Environment
Data science projects require complex code and extensive testing. Every data scientist needs a sophisticated environment to test their code to work out issues with functionality. The Integrated Development Environment (also known as the IDE) is the best environment for testing data science code in JavaScript.
Node-Spark
This project is ideal for processing large data sets. It is similar to Apache spark. Since Apache Spark is not available for JavaScript, this is a good alternative when you need to process big data.
JavaScript is Evolving as a Mainstream Data Science Development Language
Many data scientists are still skeptical about the merits of JavaScript. However, the benefits are becoming clearer. More data scientists will likely turn to JavaScript in the near future as the number of supported libraries and resources is scaled.
