Data science is an exciting field to work, combining advanced statistical and quantitative skills with real-world programming capabilities. There are many programming languages in which an aspiring data scientist may consider specializing.
Let's take a look at some of the most popular languages used in data science:
1. R Programming language:
Launched in 1995 as a direct descendant of the old programming language S, R has been strengthening. R is a powerful language that excels in a wide variety of data visualization and statistical applications, and being open source allows for a very active community of taxpayers.
Python is a very good language option for data science, and not just at the input level. According to Data science training institute in Bangalore, the data science process revolves around the ETL process which makes the generality of Python fit perfectly.
Much of the information science process depends on ETL, and the longevity and efficiency of SQL are proof that it is a very useful language for the modern data scientist.
Java is an extremely popular language that runs on the Java Virtual Machine (JVM). It is an abstract computer system that allows perfect portability between platforms. Many companies will appreciate the ability to integrate the data science production code directly into the basis of an existing code, and we also find that Java performance and type security are very advantageous.
Developed by Martin Odersky and released in 2004, Scala is a language that runs on the Java Virtual Machine (JVM). It is a multi-paradigmatic language, which allows both object-oriented and functional approaches. The Apache Spark cluster computing framework is written in Scala. However, if your application does not handle data volumes that justify the added complexity of Scala, your productivity is likely to be much higher when using other languages, such as R or Python.
Launched in 2011, Julia impressed the world of numerical computing. His profile was raised by early adoption by several important organizations, including many in the financial industry. As a recent language, it is not as mature as its main alternatives: Python and R.
MATLAB is a numerical computing language that is used in academia and industry. Developed and licensed by Math Works, a company established in 1984 to market the software. The widespread use of MATLAB in a variety of quantitative and numerical fields both in the industry and in the academic world makes it a serious choice for data science.
Well, we have seen a quick guide on what languages to consider for data science. The key here is to understand the usage requirements in terms of generality versus specificity, as well as your preferred development style of performance versus productivity.
For a GIS technician who wants to start performing data science, the ideal is to use R, Python or SQL. Since the most common functions will be to develop existing data processes and ETL processes. These languages provide an adequate balance between generality and productivity, with the option of using more advanced R statistical packages when necessary.