Since the advancement of data science is gaining more popularity. The job opportunities in this field are more. Therefore, to gain knowledge and become a professional worker, you must have a brief idea about at least one of these languages ​​that are required in Data Science.

PITON

Python is a general-purpose, multi-paradigm language, and one of the most popular. It is simple, easy to learn, and widely used by data scientists. Python has a large number of libraries, which is its greatest strength and it can help us to multitask such as image processing, web development, data extraction, database, graphical user interface, etc. As technologies such as Artificial Intelligence and Machine Learning have come a long way, the demand for Python experts has increased. Since Python combines enhancement with the ability to interact with high-performance algorithms written in C or Fortran, it has become the most widely used language among data scientists. The Data Science process revolves around the ETL (extract-transform-load) process which makes Python very suitable.

R

For statistical computing purposes, R in data science is considered the best programming language. It is a programming language and software environment for graphics and statistical computing. It is domain specific and has an excellent high quality range. R consists of open source packages for statistical and quantitative applications. This includes advanced graphing, nonlinear regression, neural networks, phylogenetics, and much more. To analyze data, data scientists and data miners use R extensively.

sql

SQL, also known as a structured query language, is also one of the most popular languages ​​in the field of data science. It is a domain-specific programming language and is designed to manage relational databases. It is systematic in the manipulation and updating of relational databases and is used for a wide range of applications. SQL is also used to retrieve and store data for years. The declarative syntax of SQL makes it a human-readable language. The efficiency of SQL is proof that data scientists find it a useful language.

JULIA

Julia is a high-level JIT (“just-in-time”) compiled language. It offers dynamic writing, scripting capabilities, and the simplicity of a language like Python. Due to its faster execution, it has become an excellent choice for dealing with complex projects containing large volumes of data sets. Readability is the key advantage of this language and Julia is also a general-purpose programming language.

SCALE

Scala is a general-purpose, open-source, multi-paradigm programming language. Scala programs comply with Java Bytecode that runs on the JVM. This enables interoperability with the Java language, making it a substantial language appropriate for data science. Scala + Spark is the best solution when it comes to computing to operate with Big Data.

JAVA

Java is also an extremely popular and general-purpose object-oriented programming language. Java programs are compiled into byte code that is platform independent and runs on any system that has a JVM. Instructions in Java are executed by a Java runtime system called the Java Virtual Machine (JVM). This language is used to create web applications, backend systems, and also desktop and mobile applications. Java is said to be a good choice for data science. The security and performance of Java is said to be really advantageous for data science, as companies prefer to integrate production code into the existing code base, directly.