Data Science is a study of analyzing data in different aspects. In several cases of consideration of data analysis, there is a general abstract framework that describes a basic structure on how data has to be designed. For example, in the generation of music notes, there’s a certain criterion like using only particular music notes for the respective tunes. Describing data analysis is a difficult conundrum. Developing a framework involves considering the elements of the data and implementing it using programming language.
Why should we use programming languages for data analysis?
As we know, data is used in many streams such as banks-to store customer details, hospitals-to store patient records and so on. For this, we require a place to store all the data. To make it function according to the requirements, we make use of programming language.
Let’s take a look at the different programming languages that we use for Data Science.
- Python-the most widely used, popular language at present, used for a vast number of applications and also in data science. The major reason of using python is because of its enormous tools and user-friendliness. It is an interpreted language as it produces the output simultaneously as we provide input to the interpreter. So it provides a base for all the data to be stored.
- R– it is also a programming language that is specifically designed to meet the needs of data miners. The most basic IDE (integrated development environment) used is RStudio. It is a user-friendly programming that consists of built-in functions to make it easier to handle.
- Java-is the widely used and popular language used for various applications. It has many IDEs just like the other languages. Java can be linked with the databases very easily and that is the main reason 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 scope for a career in data science, the knowledge of these languages play a major role in building your career. Programming is a must in all the fields these days. Especially when you are dealing with data. But having knowledge only in programming don’t yield you much. To consider this, let’s take a look at the general question that might arise.
Who should get to the field of data science?
The answer is obvious. If you have the skills that meet the requirements of a data scientist, you are good to go! Let’s consider the skills that are required.
- Statistical skills: the reason this is important is because data deals with quantitative analysis of data.
- Programming: as mentioned earlier, programming is required to design the framework for holding data.
- Ability to work with unstructured data– many of the business organizations retrieve data in unstructured form. The data scientist must be capable of dealing with such kind of data.