Although blocks of data may be referred to by name instead of by their machine addresses, assembly language does not provide more sophisticated means of organizing complex information. Like machine language, assembly language requires detailed knowledge of internal computer architecture.
Algorithmic languages are designed to express mathematical or symbolic computations. They can express algebraic operations in notation similar to mathematics and allow the use of subprograms that package commonly used operations for reuse. They were the first high-level languages. It was intended for scientific computations with real number s and collections of them organized as one- or multidimensional arrays. Its control structures included conditional IF statements, repetitive loops so-called DO loops , and a GOTO statement that allowed nonsequential execution of program code.
It was immediately successful and continues to evolve. ALGOL algo rithmic l anguage was designed by a committee of American and European computer scientists during —60 for publishing algorithms , as well as for doing computations.
Like LISP described in the next section , ALGOL had recursive subprograms—procedures that could invoke themselves to solve a problem by reducing it to a smaller problem of the same kind. ALGOL introduced block structure, in which a program is composed of blocks that might contain both data and instructions and have the same structure as an entire program. Block structure became a powerful tool for building large programs out of small components.
ALGOL contributed a notation for describing the structure of a programming language, Backus—Naur Form, which in some variation became the standard tool for stating the syntax grammar of programming languages. ALGOL was widely used in Europe, and for many years it remained the language in which computer algorithms were published. Many important languages, such as Pascal and Ada both described later , are its descendants.
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LISP lis t p rocessing was developed about by John McCarthy at the Massachusetts Institute of Technology MIT and was founded on the mathematical theory of recursive function s in which a function appears in its own definition. LISP uses a very simple notation in which operations and their operands are given in a parenthesized list. Although this appears awkward, the notation works well for computers. LISP also uses the list structure to represent data, and, because programs and data use the same structure, it is easy for a LISP program to operate on other programs as data.
Its capacity to structure data and programs through the composition of smaller units is comparable to that of ALGOL.
It uses a compact notation and provides the programmer with the ability to operate with the addresses of data as well as with their values. COBOL co mmon b usiness o riented l anguage has been heavily used by businesses since its inception in A committee of computer manufacturers and users and U. Learn R free here. This high level dynamic programming language designed to address the needs of high performance numerical analysis and computational science is rapidly gaining momentum amongst the data scientists. The base library written in Julia itself integrated with best of breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.
A collaboration between Jupyter and Julia communities, it provides a powerful browser based graphical notebook interface to Julia. Learn Julia here. A market leader in the commercial analytics space, it is one of the most popular languages in the data science community.
Julia: come for the syntax, stay for the speed
It has a wide range of statistical functions with a user friendly GUI that helps data scientists learn quickly. It is an easy to learn programming language and preferred as a must have language for beginners entering analytics industry. It surely makes it for the top 10 programming languages to learn this year. Learn SAS free here. One of the favourites amongst the data science dwellers, SQL has been at a heart of storing and retrieving data for decades and continues to do so. It is used in dealing especially large databases, reducing the turnaround time for online requests by its fast processing time.
Learning SQL can be a good addition into skills required for data science and ML experts, as this is looked after by most recruiters as a preferred skill set. Learn SQL free here. Developed by Mathworks, this is a fast, stable and ensures solid algorithms for complex math. Considered to be a hard-core language for mathematicians and scientists dealing with complex systems, it finds a way into a lot of applications.
It is one of the best known languages with one of the largest user bases. Since it was engineered to run on the JVM, anything that is written on Scala can run anywhere that Java runs. It is highly flexible and functional enough to play well with others. It is becoming a popular tool for anyone doing machine learning at large scales or building high-level algorithms.
Learn Scala free here. One of the oldest languages in the list, C has been a source to most of the modern day languages and still continues to be the popular programming languages for developers across the globe. C can be a good starting point for equipping yourself with other languages.
It is largely used in embedded systems of electronic devices such as car dashboards, television firmware etc. Learn F free here.