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The basics of data science you should know

2019-04-30 Education No comment

What exactly is data science?

In today's information technology world, it has become a buzzword. This happens in the many technologies people start talking about, not in understanding the meaning of technology, what the scope is, and so on. We will have some detailed discussions. This confusion begins when you use data science as part of today's technology solutions. It is equipped with various components. Whenever you talk about the ingredients of data science, you basically talk about big data. At this time, you also talked about the various tasks that make up part of the data science – what is the role of the data scientist, what the role of the data curator is, the role of the data librarian, and so on. In today's case, when you treat it as a field itself, it basically handles chunks of data.

Hadoop's role in data science

It mainly refers to big data and a large number of frameworks for solving these big data. There are quite a few frameworks that happen to have their own strengths and weaknesses. Hadoop is the most extensive and popular framework. Whenever you talk about data science, you're talking about different analyses, and you've already manipulated this chunk of data – you really can't escape Hadoop. Whenever you do a statistical check, you don't need to care about Hadoop or any such big data framework. However, data science happens to be a different kind of animal. In addition, Hadoop is developed in Java, so it will help if you understand Java.

What is data science?

R is actually a statistical programming language. You really can't avoid R, because when you talk about different algorithms, you need to apply this large amount of data so that you can get insights into the data or actually enable it on top of some machine learning algorithms, you need to use R service.

What is Apache Mahout?

Apache Mahout happens to be a library for machine learning. It was developed by Apache. Now, what is the reason it is so popular? What is the reason? The real soy sauce is that it integrates directly into mathematics. This is really not just about the sheer volume of data. It actually gets useful insights from a given set of data. Mahout happens to have a direct integration equation with Hadoop that allows it to use Hadoop's processing power when implementing its algorithms on large amounts of data. If you look at big companies like Facebook and LinkedIn, you will run into Mahout.

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