{"id":8484,"date":"2019-05-22T14:00:07","date_gmt":"2019-05-22T08:30:07","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=8484"},"modified":"2021-06-11T17:54:29","modified_gmt":"2021-06-11T12:24:29","slug":"julia-vs-python","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/julia-vs-python\/","title":{"rendered":"Julia Vs Python \u2013 Which programming language should you learn to enter the data science industry today?"},"content":{"rendered":"<p><a href=\"https:\/\/ivyproschool.com\/blog\/could-julia-take-on-python\/\"><br \/>\n<div id=\"attachment_8557\" style=\"width: 610px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-8557\" class=\"wp-image-8557\" src=\"https:\/\/ivyproschool.com\/blog\/wp-content\/uploads\/2015\/08\/IMG-20190515-WA0008-e1558018137676.jpg\" alt=\"\" width=\"600\" height=\"343\" \/><p id=\"caption-attachment-8557\" class=\"wp-caption-text\"><em>Image Reference : https:\/\/www.techlabe.com\/2018\/11\/julia-vs-python-whichprogramming.html<\/em><\/p><\/div><\/a><\/p>\n<p>&nbsp;<\/p>\n<p>A new entrant on the block, Julia has managed to garner a lot of buzz around itself in the past year. But Python is not falling back in the race<\/p>\n<p>The language still in its nascent stages (unveiled only in 2012, developed and incubated at MIT, yes MIT! ) made an appearance in TIOBE&#8217;s &#8220;interesting moves&#8221; picks at 39th position in September 2018.<\/p>\n<p>But if you ask me, Julia has a long way to go and is yet to achieve what Python has already earned. Python was ranked the programming language of the year 2018 by <a href=\"https:\/\/www.tiobe.com\/tiobe-index\/\">TIOBE<\/a>. Python is a favoured language among data professionals and widely used by them on a day to day basis with three out of four recommending aspiring data scientists to learn Python first.<\/p>\n<p>But then again, we are Analysts, we need data to corroborate our sayings.<\/p>\n<p>Let me try and breakdown some of the features of both the languages to get a more comprehensive view and perhaps you can declare the verdict yourself.<\/p>\n<h3><strong>JULIA &#8211; Features<\/strong><\/h3>\n<ul>\n<li><strong>Compiled and not interpreted<\/strong>: Julia is compiled by the LLVM and throws up issues like recompiling the code every time it is started up.<\/li>\n<li><strong>Not properly Developed<\/strong>: Considering its recent entry, there is still room for improvements. Many R users, when shifted to Julia, realized that it doesn\u2019t work as smooth as R. Julia\u2019s tools didn\u2019t seem to be as fluid and reliable as they are expected.<\/li>\n<li><strong>Unable to identify issues<\/strong>: Julia is far behind from Python and R in terms of identifying issues and debugging tools. But soon more tools were expected to be developed for users.<\/li>\n<li><strong>Perplexing arrays<\/strong>: Julia arrays are 1-indexed. (Translation?) The first element in an array is 1 (one) instead of one unlike in other languages(Python, Javascript, Java) which can tip off some programmers.<\/li>\n<li><strong>Column major or row major?<\/strong> Julia matrices are accessed column-major, whereas Python Numpy matrices are accessed row-order. Accessing a column-major array stored in the cache in the\u00a0column-row\u00a0order is usually more efficient than a row-column order due to the storage of the array in a sequential manner. Yes Julia has adopted that well.<\/li>\n<li><strong>Visualizations<\/strong> \u2013 Julia does offer a few libraries for visualization but they mostly seem inspired by R. E.g. &#8211; Gadfly.jl inspired by ggplot2 for R.<\/li>\n<li><strong>Safety issues regarding Interface<\/strong>: Julia poses the risk of unsafe interface to native APIs by default.<\/li>\n<li><strong>Lack of powerful packages:<\/strong> \u201cR\u201d &amp; Python has some terrific third party packages for data analysis, which Julia still lacks.<\/li>\n<\/ul>\n<h3><strong>PYTHON &#8211; Features<\/strong><\/h3>\n<ul>\n<li><strong>No compilation<\/strong>: Python is an interpreted language, there is no need to compile it.<\/li>\n<li><strong>Versatility<\/strong>: The versatility of Python (including its easy readability and code friendly syntax) allows developers to perform multiple activities at one time combined with abundance of libraries and frameworks that facilitate coding and save significant development time. Python excels in all its roles right from being the most favoured scripting language, to automating stuff, to web development and so much more.<\/li>\n<li><strong>Ease of learning<\/strong>: Even if you are a new Python user, chances are you can pretty much find the codes quite easily by just googling it. That simple!<\/li>\n<li><strong>Strong Developer community<\/strong> : This <a href=\"https:\/\/www.techrepublic.com\/article\/the-six-best-programming-languages-to-learn-right-now\/\">article<\/a> will give you a good idea about the trending programming languages and why Python is a feature in almost all articles in the same context, creditable to its high versatility and developer community unlike Julia which is still in its nascent stages.<\/li>\n<li><strong>Slow execution? Not any more<\/strong>: Although some people argue that interpreted language can result in slow execution, Intel Distribution for Python, has come up with a set of tools that lets anyone speed Python application performance right out of the box, usually with no code changes required. It helps accelerate Python execution performance. However, you can boost the speed of Python by using third-party compilers such asPyPy\u00a0and other external libraries as well.<\/li>\n<li><strong>Third-party packages<\/strong>: One of the biggest attractions of Python is the huge no. of packages it can support and make up an essential part of every data scientist\u2019s toolkit.<\/li>\n<li><strong>Python for Machine Learning<\/strong>: Python is the most popular language for Machine Learning today. There are over 145,000 custom-built software packages, many of which use \u2018machine learning\u2019 to crunch patterns in big data. Enroll in a Machine Learning <a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/machine-learning-with-python-certification\/\">course<\/a> to learn the best combination of theory and practical aspects of Machine Learning with Python.<\/li>\n<li><strong>Employer likeability<\/strong>: Python and \u201cR\u201d are also among the\u00a0<a href=\"https:\/\/www.techrepublic.com\/article\/here-are-the-10-skills-you-need-to-become-a-data-scientist-the-no-1-job-in-america\/\">most frequently mentioned skills<\/a> in job postings for data science positions.( A cool 28% of all Silicon Valley job postings).<\/li>\n<\/ul>\n<h4><strong>Industry Use Cases<\/strong><\/h4>\n<p>Nearly three decades since its inception, companies (Google, Amazon for its Product recommendations, Netflix, LinkedIn, Facebook) heavily rely on Python for their programming, web development, IOT or data analysis needs.<\/p>\n<p>According to TIOBE, Python is now becoming &#8220;increasingly ubiquitous&#8221; and a top choice at universities for all subjects that require programming, as well as in industry.<\/p>\n<p>The key to its popularity is its easy learning curve, easy installation and deployment.<\/p>\n<p>Julia is used at more than 700 universities and research bodies and also at companies such as Aviva, BlackRock, Capital One, Netflix but is yet to amass wide scale popularity of the likes of Python.<\/p>\n<p><strong>Are you still wondering? Take a read:<\/strong> <a href=\"https:\/\/ivyproschool.com\/blog\/2019\/02\/20\/top-9-reasons-to-learn-python-to-become-data-scientist-ai-expert\/\">Top 9 Reasons to learn Python to become Data Scientist &amp; AI Expert<\/a><\/p>\n<p>Python has been hailed as the top career skill of 2019.<\/p>\n<blockquote><p>\u00a0\u201c<em>Through machine learning (ML) skills, which include data science skills like R\/Python, data modelling and validation techniques, expertise in statistics and probability, and implementation of machine learning libraries and algorithms<\/em> \u201c by Economic Times.<\/p><\/blockquote>\n<p>#Pro Tip : To sum it up, Julia was\u00a0conceptualized as\u00a0a top-tier language but it still has a long way to go before being adopted for mass consumption. If you are entering the data science field, learning the nuances of Python programming language will land you more lucrative \u00a0jobs in the current job landscape.<\/p>\n<p>Check out Ivy Professional School\u2019s new course,\u00a0<a href=\"https:\/\/ivyproschool.com\/our-courses\/big-data-and-analytics\/machine-learning-with-python-certification\/\">Machine Learning With Python<\/a><u>. <\/u>Ivy\u2019s expert faculty have been named among the\u00a0<a href=\"https:\/\/analyticsindiamag.com\/20-most-prominent-analytics-data-science-academicians-in-india-2018\/\">Top 20 Analytics Academicians<\/a>\u00a0in the country in 2018.<\/p>\n<p>Shromona Kahali \u2013 Content Strategist, Ivy Pro School<\/p>\n<p>REFERENCES:<\/p>\n<ul>\n<li>https:\/\/www.technotification.com\/2018\/11\/julia-vs-python-programming.html<\/li>\n<li>The six best programming languages to learn right now<\/li>\n<li>Machine Learning Course In Bangalore with Python Certification<\/li>\n<li>The top and relevant career skills you must learn in 2019<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; A new entrant on the block, Julia has managed to garner a lot of buzz around itself in the past year. But Python is not falling back in the race The language still in its nascent stages (unveiled only in 2012, developed and incubated at MIT, yes MIT! ) made an appearance in TIOBE&#8217;s &#8220;interesting moves&#8221; picks at 39th position in September 2018. But if you ask me, Julia has a long way to go and is yet to achieve what Python has already earned. Python was ranked the programming language of the year 2018 by TIOBE. Python is a favoured language among data professionals and widely used by them on a day to day basis with three out of four recommending aspiring data scientists to learn Python first. But then again, we are Analysts, we need data to corroborate our sayings. Let me try and breakdown some of the features of both the languages to get a more comprehensive view and perhaps you can declare the verdict yourself. JULIA &#8211; Features Compiled and not interpreted: Julia is compiled by the LLVM and throws up issues like recompiling the code every time it is started up. Not properly Developed: [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":8557,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,715,551],"tags":[22,467,110,721,246,504,716],"class_list":["post-8484","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics","category-machine-learning-ai","category-tools-softwares","tag-analytics","tag-data-science","tag-ivyproschool","tag-julia","tag-latest-buzz","tag-machine-learning","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Why Python vs Julia is the new big thing? | Official Blog | Ivy Pro School<\/title>\n<meta name=\"description\" content=\"When Julia came into the programming world, there has been a lot of comparisons drawn with Python. 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