Data Science

Programming Languages Best Suited For Data Science

The insights extracted from Big Data, by Data Science, are to a large extent dependant on the Programming Languages used. Some of the best programming languages to learn for data science are now reviewed to guide our readers.

Some of the best programming languages to learn for data science are as follows:

  • R: Along with Python, this is the major language for Data Science now. Scheme or S was created by John Chambers in 1976, at Bell Labs. R is an open source implementation of S, by Language Designers Robert Gentleman and Ross Ihaka. S has been combined with lexical scoping semantics to create R. It is an interpreted language, for procedural programming. It provides software for statistical computing and graphics, supported by the R Foundation for Statistical Computing. Business support is provided by Microsoft, RStudio and so on.
  • Python: This language has been dubbed the easiest language to learn and read. It can interface with high-performance algorithms written in Fortran or C, so it is the leading programming language for open Data Science. It was created by Dutchman Programmer Guido van Rossum in the ‘80’s at CWI (Centrum Wiskunde & Informatica) as a successor to his own team’s ABC language (which followed SETL). It was conceived in order to interface with the Amoeba Operating System. It was released in December 1989. The software includes CPython, Psyco, Nuitka, SageMath, Ubuntu, Gentoo Linux, Sugar and XO.
  • SQL: Originally known as SEQUEL, and now standing for “Structured Query Language”, this language was developed during the ‘70’s by Researchers Donald Chamberlain and Richard Boyce of IBM. It was spurred by the published paper by Edgar Frank Todd in 1970, entitled “A Relational Model of Data For large Shared Data Banks”. It is popularly used for querying and editing the information stored in a relational database. Particularly large Databases can be managed by its fast processing time. This is an essential Programming Language skill demanded by employers from candidates.
  • Java: This is the most popularly used programming language for Android Smartphone applications. It is also the favorite language for the development of IoT (Internet of Things) and Edge devices. The Java compiler is written in C and C++ to create a “simple to use” language, and since it is English-based, numeric code knowledge is not required. Java is therefore a High Level language. It is an exceptional computing system supported by Oracle that creates portability between different platforms. It runs on the JVM (Java Virtual Machine). MNC companies use it frequently, to take advantage of its portability between platforms. This is another essential skill for software architects and engineers.
  • Scala: The Scalable Language was designed by Martin Odersky as a generic purpose, high level, and multi-paradigm programming language in order to provide support for the functional programming approach. Scala Programs can do the JVM (Java Virtual Machine) run, and can convert to ‘bytecodes’. Scala is inter-operable with Java, and is therefore a superb general purpose language, as well as being perfect for Data Sciences. As an example, the Cluster Computing Framework, ‘Apache Spark’, is written in Scala.
  • Julia: Julia has similar Syntax as Python, and is also a dynamic, high-level High-performance programming language for Technical Computing, Distributed Parallel Execution, Numerical Accuracy and Extensive Mathematical Function Library. Its performance is as good as ‘C’, which is a statically compiled language. It has been designed for Numerical Computing, but can also be used as a general purpose language. Julia is fast developing as a viable alternative for Python, and is rapidly acquiring a following of top class developers. Experts feel Julia could soon overtake ‘C++’. Julia was designed and developed by Jeff Bezanson, Allen Edelman, Stefan Karpinski, Viral B. Shah and others. Julia is much more adapted for Data Science than the presently popular Python. It is specifically aimed at Data Mining, Distributed and Parallel Computing, Large Scale Linear Algebra, and Machine Learning. All of these make it far better than Python for use in Data Science.
  • TensorFlow: TensorFlow Software was built by Google with an underlying C++ Programming Language. This is an AI Engine, and Coders can use C++ or Python. It uses Data Flow graphs to build models. Large scale neural networks with many layers can be built with TensorFlow, and can be used for Perception, Understanding, Discovering, Prediction and Creation. TensorFlow was Open-sourced by Google in late 2015, and is now the most popular ‘Deep Learning’ framework.
  • MATLAB: This is a multi-pardigm Numerical Computing environment, and Proprietary Programming Language. It is a perfectly suited language for mathematicians and scientists dealing with complex mathematical requirements, such as, Matrix Algebra, Fourier Transforms, and Image and Signal Processing. Latest release was in September 11th 2019. It was designed by Cleve Moler and developed by MathWorks.