What Programming language do one need to learn for becoming a Data Scientist

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In today’s competitive market, Data scientists need to upskill and upgrade themselves as per the changing demands in the industry. They must possess the knowledge and application of programming languages that better amplify the Data Science industry.
These are the top programming languages:
  • Python - Python is an interpreter based high-level programming language that is mostly used for Data Science and Software Development(Data Analysis, data mining, wrangling, visualisations and developing predictive models, Natural Language Processing, and Computer Vision). It is one of the most popular languages because of its versatility, scalability, and code-readability(even YouTube has migrated to Python due to its scalability). Also, it is an object-oriented, open-source and easy to learn programming language.
  • R - R is considered as the most popular analytical tool in the world nowadays. It is used in data analysis, statistical modelling, time-series forecasting, clustering, etc. and in other statistical operations. The functions in R can be added to a single vector without putting it in a loop. It provides an intensive environment to analyse, process, transform and visualise information.
                                 
                 

  • SQL - SQL or Structured Query Language is a domain-specific language used for extracting, managing and manipulating the data held in a relational database management system. It is mainly used for storing and retrieving data for decades.SQL is also a highly readable language, owing to its declarative syntax. One example of this can be SELECT name FROM users WHERE salary > 20000 is very intuitive.
  • Scala - Scala which is also known as Scalable language is an extension of Java language and runs on Java Virtual Machine (JVM). It is an open-source multi-paradigm programming language known for its stability, flexibility, high speed, and scalability. It is generally used to develop useful products that work with Big Data.
                        

  • SAS - Like R, SAS(Statistical Analysis System) can also be used for Statistical Analysis. The only difference is that it is not open-source like R. It is one of the oldest languages which is designed for statistics. Apart from this, SAS is highly reliable and companies looking for a stable and secure platform use it for their analytical requirements.
  • Julia - Julia is a recently developed programming language that is best suited for scientific computing and is capable of solving complex mathematical operations at a very high speed. It is widely being recognised as a language for artificial language and uses Flux, which is a machine learning architecture for advanced AI processes. It is also used in banks and consultancy services for Risk analytics.
Other than these, Hadoop, Weka, Tableau, and Java are also the top programming languages.

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