Programming Languages of Data Science

Data Science is a study of data analysis in various aspects. In several cases of data analysis consideration, there is a general abstract framework that describes a basic structure for how data should be designed. For example, in the generation of musical notes, there is a certain criterion such as using only specific musical notes for the respective tunes. The description of data analysis is a difficult relationship. Developing a framework involves considering the elements ’data and implementing them using programming languages.

Why do we need programming languages ​​for data analysis?

As we know, data is used in many streams such as banks – to store customer information, hospitals – to store patient records and so on. For this, we require a place to store all data. To make it work according to the requirements, we use programming languages.

Let’s look at the different programming languages ​​we use for Data Science.

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  1. Python – the most widely used, popular language at the moment, used for a large number of applications and also in data science. The biggest reason to use python is because of its huge tools and ease of use. It is an interpreted language as it produces output simultaneously when we provide input to the interpreter. So it provides a base for all the data to be stored.

  2. R- It is also a programming language specifically designed to meet the needs of computer companies. The most basic IDE (integrated development environment) used is RStudio. It’s an easy-to-use programming that consists of built-in features that make it easier to handle.

  3. Java is the widely used and popular language used for various applications. It has many IDEs just like the other languages. Java can be easily connected to the databases, which is the main reason why we use it for many purposes.

There are many other languages ​​such as c / c ++, scala, perl, julia that are used for data analysis.

As there is a lot of room for a career in computer science, knowledge of these languages ​​plays an important role in building your career. Programming is a must in all fields these days. Especially when dealing with data. But having only knowledge in programming doesn’t give you much. To consider this, let’s look at the general question that may arise.

Who is going to come to the field of science?

The answer is obvious. If you have the skills that meet the requirements of a computer scientist, you are good to go! Let’s consider the skills required.

  1. Statistical Skills: The reason this is important is because data deals with quantitative data analysis.

  2. Programming: As mentioned earlier, programming is required to design the data retention framework.

  3. Ability to work with unstructured data – many of the business organizations retrieve data in unstructured form. The data scientist must be able to handle this type of data.