Choosing an appropriate programming language is important in the field of Data Science. As the field is huge and involves numerous libraries, it is imperative to use different languages which have different purposes.
R: R is a programming language which focuses on the analysis of data. It is a preferred tool while working with any kind of data which requires extensive analysis. Data Scientists should have a comprehensive knowledge of an analytical tool such as R Programming. The programming language makes it easier to handle large amounts of data. R offers statistical techniques such as classical statistical tests, linear modelling, non-linear modelling, classification, clustering etc. to make data handling, data storage, calculation and data analysis easier. R offers high quality open-source packages, loads of statistical functions and great visualization tools.
PYTHON: Python is one of the most famous as well as the most commonly used programming language. It is a crucial skill to have in the field of Data Science. It is a general purpose, high-level programming language. The language was developed to emphasize on the readability of codes and to make the syntax simpler to read and write. As Python offers versatility and simplicity, processing of data becomes simpler and easier. Various formats of data are accepted by Python which makes the integration between these types of data easier and multiple operations can be performed by professionals to achieve the required results. Along with this, datasets can be created, and codes can be written to store and do calculations.
SQL: SQL which stands for Structured Query Language, is a programming language which helps in communicating with a database. It is a domain-specific language and helps in accessing, communicating and working on data easier. It is designed to manage and process large amounts of data. SQL statements can also be used to update and retrieve from any database. By using this programming language, a Data Scientist can gain insights into the formation as well as the structure of a database.
JAVA: Even though, Java has a smaller number of libraries when compared to other programming languages used in Data Science it has several advantages. Java is compatible with most systems as a majority of them are coded in Java. This makes it easier to integrate into the system. Java is a general purpose, compiled and high performing programming language.
SCALA: Scala is a preferred language among Data Scientists as it runs on JVM. Even though this gives it a complex structure, it’s high performing cluster computing covers up for the complexity. An added advantage of Scala is that it can run on Java as well.