A formal language which consists of instructions set that produce different types of output is called a programming language. These kinds of languages are used in the programs of the computer for implementing the algorithms and comprises of several apps. You can find different programming languages for data science. Many of the data scientist need to master and learn at least a single programming language. Because it is crucial device to realize the functions of data science. There are two kinds of programming languages one is high level and low level. The language of low level is less developed and computers most understandable language for performing various function. It involves machine and assembly language. While the language of assembly deals with manipulation of direct hardware and issues of performance, a machine language is binaries read and computer executed. The software of assembler will convert this assembly language into machine code.
The programming languages of low level are faster and high memory efficient when compared with the counter parts of high level. Another programming language offers powerful details and programming concepts abstraction. The high-level languages can produce code which is independent of the type of computer. They are as well closer to human language, and utilized for instructions of problem solving, and are portable. Such that most of the data scientists utilize languages of high-level programming. The one who want to enter into the data science specialization need to learn some programming languages to become a data scientist.
Different types of data science programming languages
Let’s discuss about various kinds of data science programming languages.
It is the programming language of high level constructed by statisticians. The language of open source and software are utilized for graphics and statistical computing. However, it consists of various applications in data science and also it consists of different data science libraries. The language R can be used for data sets exploring and performing analysis of hoc. But the loops consist of more than thousand iterations and it is complicated to learn that python programming language.
It is a programming language of data science which is a developed one whose purpose is for performing a high-performance computational science and speedy numerical analysis. It also can implement the concepts of mathematics such as linear algebra. Julia is an amazing language which deal with the matrices and other kind of algorithms of mathematics. It can be utilized for two programming one is back end and front end. It consists of API that can be incorporated in the programming language.
SQL in other words a structured query language has turned into a prominent programming language for maintaining the information over the years. Even though it is not utilized for performing the operations of data science, SQL tables and queries can really help data scientists in dealing with the systems of data management. This is one of the domain specific language which is extremely comfortable for manipulating, retrieving, and storing the information in databases that are relational.
Scale is the elegant and modern programming language produced in the year of 2003. It was actually designed to identify the problems associated with Java language. Its range of application from the programming of web to machine learning. It is also one of the effective and scalable languages for big data handling. In todays companies, this programming language is supporting functional and object oriented and also synchronized and concurrent processing.
It is the most popular data science programming language in the recent days. Python is easy to utilize and an open source programming language which is in the field of programming languages from the year 1991. Python is a dynamic as well as object-oriented programming which supports various paradigms from functional to structured and programming of procedure. Such that it is considered as the prominent programming language for data scientists also. It consists of less than 1000 iteration so it is a best and fast choice for the manipulations of data. The processing of natural data and learning of data is easier with the python having packages in it. Also, it is easier for programmers for reading the information in a spreadsheet for making an output of CSV.
Thus, these are some of the best data science programming languages.