{"id":250255,"date":"2023-03-22T13:45:47","date_gmt":"2023-03-22T13:45:47","guid":{"rendered":"https:\/\/imarticus.org\/?p=250255"},"modified":"2024-04-05T09:46:04","modified_gmt":"2024-04-05T09:46:04","slug":"python-vs-r-why-is-python-preferred-for-data-science","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/python-vs-r-why-is-python-preferred-for-data-science\/","title":{"rendered":"Python vs R: Why is Python Preferred for Data Science?"},"content":{"rendered":"

Python<\/span> is a high-level and fast-growing programming language which is ideal for scripting both applications as well as websites. Even though there are several programming languages such as C++, SQL or R that are widely used by aspiring data scientists, <\/span>Python<\/span> stands out from the rest.\u00a0<\/span><\/p>\n

\"become<\/p>\n

A <\/span>career in Data Analytics<\/a><\/strong> ensures a promising future for those who can master the fundamental programming concepts and apply them to solve real-world problems in any business.\u00a0\u00a0<\/span><\/p>\n

It is imperative to know how to employ data analytics tools to evaluate the performance of a business. Knowing a programming language like <\/span>Python<\/span> can be extremely effective as it helps you build these tools.\u00a0<\/span><\/p>\n

Some data scientists, on the other hand, use R to analyse data through interactive graphics. In fact, R is a frequently chosen programming language for data visualisation. It is, however, important to understand on what grounds <\/span>Python<\/span> and R are different and why Python is the most preferred programming language in this profession.\u00a0<\/span><\/p>\n

What Is Python?<\/strong><\/h2>\n

Python<\/span> is extremely versatile and it is among the most dynamic and adaptable programming languages used in data analysis. It is used to develop complex numeric as well as scientific applications. You can use this programming language to perform scientific calculations.\u00a0\u00a0<\/span><\/p>\n

It is an open-source and object-oriented programming language with a rich community base, libraries and an enormous arrangement of tools. Compared to other programming languages, <\/span>Python<\/span>, with its straightforward code, is much simpler to learn because of its broad documentation.\u00a0<\/span><\/p>\n

What Are the Features of Python?<\/strong><\/h2>\n

The significant features of this programming language are<\/span><\/p>\n

Readable:<\/b> In comparison to other programming languages, it is much easier to read. It uses less code to perform a task.\u00a0<\/span><\/p>\n

Typed language:<\/b> The variables are automatically created as it is a typed language.\u00a0<\/span><\/p>\n

Flexible:<\/b> It is quite easy to run this programming language on multiple platforms as it is flexible and adaptable.\u00a0<\/span><\/p>\n

Open-source:<\/b> It is a free programming language. It uses an easily accessible and community-based model.\u00a0<\/span><\/p>\n

Why Is Python Important in Data Science?<\/strong><\/h2>\n

Whether you are already a professional data analyst or someone who aspires to explore a lucrative <\/span>career in Data Analytics<\/a><\/strong>, it is imperative that you know how to use <\/span>Python<\/span>. Some of the most prominent reasons why this programming language is preferred for data science are:<\/span><\/p>\n

Easy to learn and use:<\/b> With better comprehensibility and simple syntax it has become extremely popular over the years. It is also quite easy to handle the data through its data mining tools<\/a><\/strong> such as Rapid Miner, Weka, et cetera.<\/span><\/p>\n

Builds superior analytics tools:<\/b> It is a dynamic programming language that provides better knowledge and correlates data from large datasets. It also plays a crucial role in self-service analytics.\u00a0<\/span><\/p>\n

Important for deep learning:<\/b> It assists data scientists to develop deep learning algorithms which were majorly inspired by the architecture of the human brain.\u00a0<\/span><\/p>\n

Creates data analysis scripts:<\/b> Data analysis scripts can be created using this program within <\/span>Power BI<\/span>.\u00a0<\/span><\/p>\n

Has a rich community base:<\/b> Python<\/span> developers are able to address their issues within a huge community of data scientists as well as engineers. <\/span>Python<\/span> Package Index, for example, is a great place for developers to explore this programming language.\u00a0<\/span><\/p>\n

What Is R?<\/strong><\/h2>\n

R is a versatile, statistical and advanced programming language which is primarily used for interpreting data. It works perfectly for data visualisation, web applications and data wrangling. It is also used to perform statistical calculations and that too without vectors. R makes collecting and analysing large datasets easy.\u00a0<\/span><\/p>\n

What Are the Features of R?<\/strong><\/h2>\n

The important features of R include<\/span><\/p>\n

Open-source:<\/b> R too, is free, adaptable and accessible to all. It can be easily integrated with multiple applications.\u00a0<\/span><\/p>\n

Static graphics:<\/b> R has powerful and interactive static graphics which produce high-quality data visualisations.\u00a0<\/span><\/p>\n

Statistical calculations:<\/b> This programming language can perform both simple as well as complex statistical calculations.\u00a0<\/span><\/p>\n

Compatibility:<\/b> This programming language is compatible with other programs such as C, C++, Java, et cetera.\u00a0<\/span><\/p>\n

Python vs R: Which Programming Language Is Preferred in Data Science?<\/strong><\/h2>\n

The primary reasons why Python is often preferred over R are:<\/strong><\/p>\n

Purpose:<\/b> Both these programming languages serve different purposes. However, even though both are used by data analysts, it is <\/span>Python<\/span> which is considered more versatile in comparison to R.\u00a0<\/span><\/p>\n

Users:<\/b> The software developers prefer <\/span>Python<\/span> over R as it builds complex applications. Statisticians and researchers in academia, on the other hand, prefer using R.\u00a0<\/span><\/p>\n

Ease of use:<\/b> Beginner programmers prefer <\/span>Python<\/span> because of its English-like syntax. R, on the other hand, can be difficult once a programmer starts exploring its advanced functionalities.\u00a0<\/span><\/p>\n

Popularity:<\/b> Python<\/span> outranks R mainly because it can be used in several software domains.\u00a0<\/span><\/p>\n

However, despite these differences, both these programming languages have robust ecosystems of libraries and are extremely crucial for an aspirant who wishes to start a prospective <\/span>career in Data Analytics<\/span>.<\/span><\/p>\n

Conclusion<\/strong><\/p>\n

A career in data science<\/strong><\/a> is considered one of the most successful professions in recent years. If you want to <\/span>learn data analytics<\/span> techniques, it is imperative that you learn <\/span>Python<\/span>.\u00a0<\/span><\/p>\n

In order to pursue a <\/span>career in Data Science<\/span>, you should choose a proper <\/span>data analytics certification course<\/span> that introduces you to this programming language.\u00a0<\/span><\/p>\n

Imarticus’ Postgraduate Program in Data Science and Analytics<\/span><\/a> is a job-assurance program that helps you navigate all aspects of this profession. The curriculum of this course covers all the fundamental data analytics concepts including data analysis, introduction to important programming languages such as SQL and <\/span>Python<\/span>, data visualisation with <\/span>Power BI<\/span> or Tableau and applications of machine learning.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Python is a high-level and fast-growing programming language which is ideal for scripting both applications as well as websites. Even though there are several programming languages such as C++, SQL or R that are widely used by aspiring data scientists, Python stands out from the rest.\u00a0 A career in Data Analytics ensures a promising future […]<\/p>\n","protected":false},"author":1,"featured_media":243301,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_mo_disable_npp":"","_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[23],"tags":[3229],"class_list":["post-250255","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-best-data-analytics-course"],"acf":[],"aioseo_notices":[],"modified_by":"Imarticus Learning","_links":{"self":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/250255","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/comments?post=250255"}],"version-history":[{"count":2,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/250255\/revisions"}],"predecessor-version":[{"id":262918,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/posts\/250255\/revisions\/262918"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media\/243301"}],"wp:attachment":[{"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/media?parent=250255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/categories?post=250255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imarticus.org\/blog\/wp-json\/wp\/v2\/tags?post=250255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}