The R Language was developed at AT & T Bell Laboratories by Rick Becker, John Chambers, and Allan Wilks and released in 1995. As an interpreted language, R’s users frequently access it through a command-line interpreter , Data scientists and statisticians around the world use this programming language to solve some of their most challenging problems in fields that range from computational biology to quantitative marketing. Since complex data is represented through charts and graphs, the language has become an essential part of the data analysis process.



Supports matrix arithmetic

Freely available under the GNU

Contains pre-compiled binary versions that are available for every operating system

Command line interface is used in the language

Implements a wide variety of statistical and graphical techniques including linear and non-linear modeling, time-series analysis, classical statistical tests, classification, clustering, and others

Because of functions and extensions it is easily extensible Most of R’s standard functions are written in the language itself which makes it simple for the users to follow the algorithm changes that are made

Unlike most statistical computing languages, R has stronger object-oriented programming facilities

The extending of R is simplified by its lexical scoping rules

download guide for install R

Read more about R for Data Science



Python was established in the late 1980s, and it went on to being implemented in December 1989 by Guido van Rossum at CWI in the Netherlands. It was released in 1991.



The language equips constructs with the intention of enabling clear programs on both large and small scale

. The language supports various programming paradigms, including imperative, object-oriented, and functional programming or procedural styles.

It contains a dynamic type of system, a large comprehensive standard library and an automatic memory management system.

The major advantage it holds is its breadth. An example to explain this: On a preprocessed dataset, R can run Machine Learning Algorithms.

Python, however, is much better at processing data. Python uses Panda, which is an immensely useful library that can do everything that SQL or R does, plus more.


How is useful in data scientists ?

They often involved in wiring together network applications, scripting and automating data processing jobs, programming for the web and other processes including data munging. They find it desirable to do all of this, in addition to the actual analysis and modeling in one single language.




MATLAB, a fourth gen programming language, is a multi-paradigm numerical computing environment. Developed by MathWorks, it was initially released in 1984



The programming language allows platting of functions and data, matrix manipulations, implementation of algorithms, interfacing with programs written in other languages, including C, C++, Forton, Java, and Python, and creation of user interfaces.

Though the language was initially intended for numerical computing, the language now oers an optional toolbox that uses the MuPAD symbolic engine that allows access to symbolic computing capabilities.

Simulink, an additional package that is oered, adds multi-domain graphical simulation and model-based design for dynamic and embedded systems.

The language has an interactive environment for design, iterative exploration and problem solving.

It oers mathematical functions for statistics, linear algebra, filtering, Fourier analysis, optimizations, solving ordinary deferential equations, and numerical integrations.

It has built-in graphics for visualizing tools and data to create custom plots.


How is useful in data scientists ?


data analysis and scientific community use this language to solve problems that are represented as matrix problems. With this programming language, data scientists can perform analysis to gain insight into the data quicker than those like C, C++, or Visual Basic. Data Analysts use MATLAB to access data from spreadsheets, files, databases, data acquisition hardware, and other software, explore data for the identification of trends





The language is designed to scale up from single servers to millions of machines, each of which oers local storage and computation. Hadoop is written in Java; all the modules are devised with the central assumption that hardware failures are ordinary and common and should be handled automatically in a software.




Hadoop Common – this module consists of utilities and libraries that are essential to other Hadoop modules.

Hadoop Distributed File System (HDFS) – The HDFS module is a distributed file-system that is involved in storing data in commodity machines that provide high aggregate bandwidth across a cluster.

Hadoop YARN – YARN is a resource management platform that handles the management of computation of resources in clusters and uses them for the scheduling of users’ applications.

Hadoop MapReduce – is a programming model that is used for large-scale data processing.


How is useful in data scientists ?

data scientists to store and process data. Instead of depending on proprietary hardware and other systems to process and store data, Hadoop allows parallel distributed processing of massive amounts of data across industry standard servers that will process and store data

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