Since the advancement of data science is more popular capture. Employment opportunities in this field are more. Therefore, in order to gain knowledge and become a professional worker, you must have a brief idea of ​​at least one of these languages ​​required in data science.


Python is a general purpose, multi-phrasing one of the most popular languages. It is simple, easy to learn and widely used by data scientists. Python has a large number of libraries which are its biggest power and can help us in multitasking like image processing, web development, data mining, database, GUI, etc. The rise, the demand for Python experts increased. Since Python combines optimization with the ability to interact with high performance algorithms written in C or Fortran, it has become the most used language among data scientists. The data science process revolves around the ETL process (converting extract – download) making Python perfectly suited.


For statistical computing purposes, research in data science is considered the best programming language. It is a programming language and software environment for graphics and statistical computing. It is a specific field and has an excellent quality range. R consists of open source packets for statistical and quantitative application. This includes advanced plotting, non-linear regression, neural networks, genetics, and others. For data analysis, data scientists and miners use data widely.


SQL, also known as Structured Query Language, is one of the most common languages ​​in the field of data science. It is a field-specific programming language and is designed to manage a relational database. It is systematic in handling and updating relational databases and is used for a wide range of applications. SQL is also used to recover and store data for years. SQL declarative syntax makes it a readable language. SQL proficiency is evidence that data scientists consider it to be a useful language.


Julia is a high-level localized language (JIT (“Just in Time”)). Provides dynamic writing, scripting capabilities and language simplicity like Python. Due to its rapid implementation, it has become a good option for dealing with complex projects that contain large amounts of data sets. Reading is the primary feature of this language, and Julia is a general purpose programming language.


Scala is a multaradigm, open source, general purpose programming language. Scala software complies with Java Bytecode running on JVM. This provides interoperability with Java, making it a core language suitable for data science. Scala + Spark is the best computing solution for working with big data.


Java is also a general purpose programming language. Java applets are categorized byte-independent platform code and run on any system with JVM. Help in Java is executed by a Java runtime system called Java Virtual Machine (JVM). This language is used to create web applications, backend systems as well as desktop and mobile applications. Java is said to be a good option for data science. Java security and performance are said to be really useful for data science because companies prefer to integrate production code directly into the existing database.


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