Since the advancement of Data Science is capturing more popularity. Job opportunities in this field are more. Therefore, in order to gain knowledge and become a professional worker, you need to have a brief idea about at least one of these languages that are required in Data Science.
Python is a general-purpose, multiparadigm and one of the most popular languages. It is simple, easy-to-learn and widely used by the data scientists. Python has a huge number of libraries which is its biggest strength and can help us perform multiple tasks like image processing, web development, data mining, database, graphical user interface, etc. Since technologies such as Artificial Intelligence and Machine Learning have advanced to a great height, the demand for Python experts has risen. Since Python combines improvement with the ability to interface with algorithms of high performance written in C or Fortran, it has become the most popularly used language among data scientists. The process of Data Science revolves around the ETL (extraction-transformation-loading) process which makes Python well suited.
For statistical computing purposes, R in data science is considered as 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 plotting, non-linear regression, neural networks, phylogenetics and many more. For analyzing data, Data Scientists and Data Miners use R widely.
SQL, also known as 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 at manipulating and updating relational databases and is used for a wide range of applications. SQL is also used for retrieving and storing data for years. Declarative syntax of SQL makes it a readable language. SQL's efficiency is proof that data scientists consider it a useful language.
Julia is a high level, JIT ("just-in-time") compiled language. It offers dynamic typing, scripting capabilities and simplicity of a language like Python. Because of faster execution, it has become a fine choice to deal with complex projects that contain high volumes of data sets. Readability is the key advantage of this language and Julia is also a general-purpose programming language.
Scala is a multiparadigm, open-source, general-purpose programming language. Scala programs comply with Java Bytecode which runs on JVM. This permits interoperability with Java language making it a substantial language that is appropriate for Data Science. Scala + Spark is the best solution when computing to operate with Big Data.
Java is also a general-purpose, extremely popular object-oriented programming language. Java programs are compiled to byte code which is platform-independent and runs on any system that has JVM. Instructions in Java are executed by a Java run-time system called 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. Java's safety and performance are said to be really advantageous for Data Science since companies prefer to integrate the production code into the codebase that exists, directly.