Which language is better for Data Science?
Table of Content
Choosing the best programming language to use as a data scientist can be challenging. You must have a solid command of a language in order to have the knowledge. Any language you choose to learn requires efficiency and precise competence since every language has benefits and drawbacks. Some of these have to be learned eventually. Let's attempt to comprehend the number of programming languages used in data science and the best language for the field.
Data Science is the most popular and fast-growing field right now. It has the highest job demand currently, whereases the market demand is always going to increase. Increasing market requirement makes it an excellent option for a career choice.
To become a data scientist, one has to master the analytical tools and not just one, but more than one. Many popular data science tools are available right now. The debate on which language is best has been going on forever, so let us see which has more leverage.
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What is a Programming Language?
Programming language refers to a formal language that contains a collection of instructions and generates the desired result. Algorithms are implemented in computer programmes using a variety of languages.
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For data science, a variety of programming languages are utilised. To fully realise the goal and functions of data science, a data scientist needs grasp the analytical tools and at least one language.
Low-Level And High-Level Programming Languages
Although these languages are less sophisticated, they are simple to master. Assembly and machine languages are examples of low-level programming languages. Compared to their high-level equivalents, low-level programming languages are faster and more memory-efficient.
Low-level programming languages
These languages are easy to learn, but they are less advance. Low-level programming languages include assembly and machine languages. Low-level programming languages are swifter and more memory efficient as compared to their high-level counterparts.
High-level programming languages offer a strong conception of detailed programming ideas and concepts. They can create codes that make a computer independent. Besides, high-level programming languages are utter advance, portable, and closer to human language.
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Which Language is Better for Data Science?
Data scientists today work in a rapidly expanding area, thus in order to further their careers, they must acquire at least one or two high-level programming languages.
Let's examine some of these languages' characteristics and benefits.
The most widely used and popular data science programming language that has frequently been taught in beginner courses at universities is Python. It frequently makes an appearance when ranking top programming languages.
Python, which is mostly used for web and research development, is comparable to Java in that they both have extremely simple syntax. Python isn't technically object-oriented, but you can start producing acceptable code without having to group everything into classes as you would in Java.
Python works like a Swiss knife. It can be used anywhere and at any level. You can interact with hardware through Arduino, use its scientific libraries, do web scrapping, connect with the web, use its CMS like Django, build your search engine, and run a web server. It's highly scalable, functional, supports multithreading and parallelism.
Java has consistently been ranked one of the best programming languages for years. This is due to its ease of use, "Write once, run anywhere" capabilities, and is the de-facto language of Android.
It’s also frequently taught in beginner courses at universities and used by many large-scale corporations.
Another high-level programming language that was developed in 2003 is called Scala. Big data management and object-oriented programming are both offered by Scala.
R programming language is used to find the solutions of statistical computing and graphics. R language offers to exploring data sets and conducting ad hoc analysis.
The relational DBMS uses the database query language Structure Query Language (SQL) to store and manage data. The first business language for E.F. Codd's relational model was SQL Database. Today, SQL is the de facto standard computer query language and is used by practically all RDBMS (MySql, Oracle, Infomix, Sybase, and MS Access).
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But why did I Choose these Languages?
A solid understanding of these languages will help you have a solid decision making of where to develop a certain component. Some things are better done on the backend, like heavy processing, because you will limit front-end processing with your computer processing power and not utilize server capacity.
These languages have enough breadth and depth to be deployed anywhere. They can cater to 90% of common demands.
In a Nutshell
More than 250 programming languages are employed in various industries in our fast evolving technological world.
With more than 70,000 libraries and 8.2 million users globally, Python emerges as the most prominent and respected programming language in this expanding business.
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It is advised that you study SAS as your first language if you are a newcomer entering the analytics business, particularly in India. It has the biggest job market share and is simple to learn.
If you have experience and have a long history in the field, you should aim to broaden your knowledge by picking up a new skill.
At least two of these languages should be known by professionals and specialists in the data science field. Learning two languages would greatly increase flexibility and provide new options.
R or Python is more helpful if you want to start a business or do freelance work.
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