Data Science has been an up and coming field that deals with a lot of importance in the new age of information. We can use if to elucidate all kinds of data, no matter how orderly or chaotic it is. But despite this, there’s still a lot that we don’t understand about the field as a whole.
As data is getting more and more complicated, data scientists are looking for newer resources to improve their methods of finding sorting data. This also means that newer methods are starting to be developed to help data scientists understand and categorize the data they’re given.
These days there are a lot of good opportunities for growth of a data scientist that knows their way around Java. That’s why we’re going to look at some reasons why you should consider learning Java if you’re a data scientist.
Versatile Working Tools
Java is often considered a great backbone to build a platform or part of a platform out of. There are multiple workloads and tasks out there that require specific resources. Most of the time, you’re between having to learn how to use these resources or hiring someone that does.
Java sidesteps this problem by incorporating useful resources and tools all in one package. Everything just becomes an extension of a larger part of your Java skills. As a data scientist, you’ll be easily able to use resources and libraries like Deeplearning4j, MLlib, Weka, and Java-ML for things like data science and machine learning.
Often when programming or developing something, you have to pay special attention to the platform you’re working for. It doesn’t matter what kind of resources or libraries you tend to use, if they can’t be utilized on your intended platform, then it can be a bit of a waste.
Thankfully, Java does not have major platform restrictions or differences. It is completely platform-independent, which makes it ideal for something like data science, where the need for platform modification is necessary. Doing things like making custom tools or implementing across multiple different platforms is much easier with Java. You do a lot to increase your productivity with data science and get your workflow going.
Ease of Learning
Learning complicated coding structures, modules, and libraries can make it very hard for you to excel at your programming needs. For things like data science, it can be very frustrating to sift through having to learn and implement new tools.
Java, on the other hand, was built with simplicity in mind. It was meant to be easy to learn, easy to compile, easy to code, and easy to debug. You don’t have to do much to learn the language, and there are helpful online sources that help you learn it in a matter of seconds. In fact, there are so many resources that can make it hard to differentiate. That’s why you can often hear questions like, “Is Pluralsight or Lynda better value for money?” being asked.
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