Choose the Right Programming Language for Data Science in 2020

25-Nov-2020

Data Science is one of the best inter-disciplinary fields that use scientific methods, processes, algorithms, and systems to extract knowledge. It also helps you to insights from many structural and unstructured data. It is being said that Data science course is related to data mining, machine learning, and big data that provides great facilities to choose the best subject and language to learn something deeply. It is a kind of way that accelerates the business value with a scalable Data Science platform where you can learn the trick to manage your business in a vital way. There are most of the fresher candidates who are required to complete their graduations and for this objective, they are looking into the world of Data Science, and choosing the right programming language can be a make or break such kind of decision soon.

How are Programming Languages used in Data Science?

There is a huge demand for Data Science Certification among those candidates who believe in their carrier in a programming language. This is a guide for all of them to build up the programming languages and their uses will help to enhance the student’s knowledge of data science. It typically comes related to the languages and it encourages them to make the right choice at every step of getting the course of Data Science.

Categories of Programing Languages

If you are looking for help to choose the right programming language and want to know that how it works for Programming Language for Data Science, you are required to read the proper advice and tutorials to blow your mind to take this course ahead forever.

Low-Level Programming language:

This programming language is considered the most understandable for the basic-level operations that are used by a computer. It is simply used for the assembly language and machine language where assembly language is used for hardware components and machine language comes for binaries and other software on the computer.

High-Level Programming Language:

Such programming Language is close to human languages that are used by developers who believe in creating codes that can be converted into the machine language in a useful manner. It is said that Data Scientists generally use the High-Level Programing Languages that are used for building analytical tools and technologies to enhance your business in many ways.

Also check our Post Graduate Program in Data Science

Different Programming Languages Used in Data Science is as Follows:

1. Python
2. Java Script
3. SAS
4. R

Python:

It is known as a high-level and general-purpose programming language. It is designed for philosophy emphasizes code readability with its notable use of significant whitespace. It is considered easy to use as it is interpreter-based and high levels of readability. This language is associated with small and big industries such as Netflix, Google, Mozilla, Facebook, and so on.

Pros of Python:

• Python language is widely used for open-source that comes to focus on code readability without realizing hard to read and understand.
• This language is pretty simple to use while making any code to prepare any tools and software on your device.
• In this language, you can see a library that makes you quite eligible to begin the process of machine learning, game development, and web development perfectly.

Cons of Python:

• It is said that when you use Python language you might face threading problems just because of the global interpreter lock. It allows a single thread to execute at the same time.
• Most of the developers observe Python for the mobile phone as it is not native for all mobile phones that are used by personal professional users.
• It is used for memory consumption for any kind of intensive tasks while creating codes and also it has limitations with database access.  

JavaScript -

JavaScript is a dynamic and fully-fledged programming language. It is very lightweight and does not consume many resources. It is commonly used to produce dynamic content and run scripts on web pages. It is an interpreted programming language with object oriented concepts and capabilities built into it.

It is extremely popular in the world of the internet. Almost every web browser in the world has a general purpose JavaScript core engine embedded in it.

Pros of the JavaScript Programming Language -

• There is a lesser amount of server interaction. JavaScript enables the programmer to validate user input on the client-side itself, without having to send it to the server. This reduces the need to send all the input to the server and reduces the sever traffic and load on the server.
• JavaScript enables the programmers to provide immediate feedback to the users. For example, if the user has filled a form and forgotten to fill in one of the required fields, if the web page has been written in JavaScript, the programmer can inform the user about the missing information without the user having to submit the form.
• JavaScript enables the programmers to provide very interactive and responsive interfaces. For example, the programmer can create a button over which a box of text with some information appears when the user hovers over it with a mouse.
• JavaScript enables the programmers to create rich interfaces with interactive and graphical components such as drag and drop input fields and sliders.

Cons of the JavaScript Programming Language -

• It is not possible to read and write files in client-side JavaScript. This is a design decision for security reasons.
• It is not possible to use JavaScript for networking applications because it is not supported by the language. This limitation can be overcome through the use of certain libraries.
• It is not possible to write multithreaded or multiprocessor-using applications in JavaScript because it is not supported by the language.

SAS Programming Language -

SAS is a computer programming language which was created in the 1960s by Anthony James Barr at the North Carolina State University. It is a program primarily intended to be used for statistical analysis. One of its most impressive features is its ability to accept common spreadsheets and databases as inputs, making it similar to SQL in this regard. It can perform many statistical functions and output the results of that statistical analysis in PDF, HTML, and RTF documents, as well as in tables and graphs. The language is fully cross-platform, with compilers available for Microsoft Windows, Linux, and other UNIX and mainframe computers.

Some statistical procedures that SAS can perform are arithmetic mean, mean by class, standard deviation, Analysis of Variance, Cluster analysis, Curve fitting, Gamma Distribution fitting, Grubbs’ Outlier test, Item responses analysis, meta analysis of correlated proportions, Bland-Altman plot and analysis, Deming regression etc.

R Programming Language -

R is a programming language which was created and is maintained by GNU project. It was created by John Chambers specifically at the Bell laboratories. R is primarily intended to be used for statistical analysis and graphical visualization. To this end, it provides several statistical techniques such as clustering, classification, time series analysis, classical statistical tests, linear and nonlinear modelling etc. It also provides the most latest and cutting edge graphical visualization techniques.

It is also extremely extensible with extensions in most of the major programming languages written for it and various popular libraries and modules available in the R communities. One of reasons why R is so popular in the scientific, mathematical, as well as the commercial and industrial world is that it can produce very high quality publication-standard plots on demand, including mathematical symbols and formulae.

It is a very well designed programming language which includes all the standard features of a modern programming language including input and output facilities, user-defined recursive functions, loops, and conditionals. It also allows users to write new functions and expand the already vast list of included functions in the language.

Pros of the R programming language -

• It is an open source programming language. This means that anyone with an internet connection can download, view, and modify the source code of the R programming language and the R programming suite. This makes it easy to contribute changes and spot programming errors or bugs.
• R provides great support for data wrangling. Packages like readr and dplyr can quickly transform unstructured and chaotic data into nicely formatted structured data.
• R has a huge array of packages and the number is constantly growing. There are over 10,000 packages in the CRAN repository.
• R is platform independent. It has high performance compilers available for all the major computing platforms such as Microsoft Windows, Linux, Unix, and mainframe systems.

Cons of the R programming language -

• R has its origins in a very old programming language S. This has led to it being very difficult to produce dynamic 3D graphics or interactive graphics in the language. But packages like Ggplot2 and Plotly exist to make up for this shortcoming.
• R doesn’t have basic security such as memory authentication. This shortcoming means that R cannot be embedded in any web application because of the security vulnerabilities it would introduce.
• R is a very complicated language. It is not as easy to learn as say, Python. This steep learning curve makes it very difficult for people without prior programming experience to enter into the R ecosystem.
• The R programming language and the R packages have much lesser speed compared to other data science languages such as Python and MATLAB.

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