Programming in Data Analytics

What Programming language best applies in Data Analytics?


Well, can there be any computer related technology or science without it involving ‘Programming’ languages or codes?

Save yourself the troubles, the answer is No, and it is basically due to the key in fact that the computer is a stupid machine and a foreigner who doesn’t understand English or any other human language.

So for its own good, programming languages are created or written to comprise a set of instructions or commands the computer can understand so it can produce the needed output.

These programming languages are created to implement algorithms or applications we often times come to refer to as ‘Programmes’ or ‘Softwares’. When it comes to data science or data analytics, data scientists cannot be the merrier as these programmes have made their jobs easier on the surface level.

Programming in Data Analytics

What they only need to do is to learn and master at least one of these programming languages as it is very much essential to their field of work in data science or analytics.

Of course, by now we all know that these languages are already grouped into 2; low level programming languages and high level programming languages.

Low level languages like it is with broken English speakers, is a less advanced form or programming language which like the broken English is quite understandable for people new to programming. Here, we have the assembly language and the machine language.

In Low level languages, the assembly language deals with direct hardware manipulation and performance issues whereas the machine language is basically the binary jargons the computer reads and very well understands. The thing is, an assembler gets to convert the assembly language into these machine codes.

For the high level programming language, it provides stronger details and concepts and are portable and closer to human language that is quite useful in handing instructions.

When it comes to Data Science or Analytics, the programming languages applicable or mostly used as earlier established are Python, Java Script, SQL, R, Scala and Julia.

Python, for one is the most used by data analysts or data scientists or roughly just about everybody in the coding business. It is the most reliable when it comes to its speed and the fact data manipulation is eased while at it.

JavaScript is another object oriented language like Python, with hundreds of libraries available on it to cover all sorts of problems the programmer may face. Like the Python, it is quite versatile and capable of multi-tasking.

SQL, pronounced by a few as ‘Sequel’ which of course isn’t its exact meaning as it stands for the acronym of Structured Query Language is known for managing data, but ironically isn’t exclusively used for data science operations.

Programming in Data Analytics

This domain-specific language is extremely convenient for storing, manipulating, and retrieving data in relational databases.

R, this one is a high level language that was built by statisticians, and like you may have guessed, is used for statistical computing and graphics. It comes in handy when seeking to explore data sets and when conducting ad hoc analysis. When compared to Python, it is more complex to understand.

Scala, is more a recent programming language that was created in 2003 mainly to address the issues with Java. Its operation ranges from web programming to machine learning, and it does quite well handling big data. It supports object oriented and functional programming as well as allowing for synchronized processing.

Julia, is a purpose driven language made for speedy numerical analysis which is quick enough to implement mathematical concepts such as linear algebra. It also deals with matrices and can be used to serve front end and back end programming, with its API possessing the capacity of being embedded in programmes.


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