Microsoft Excel is possibly one of the most popular programs<\/strong><\/a> that professionals of all stripes use to crunch numbers and analyze data. Others also implement tools<\/strong><\/a> like SAS, SPSS, and other statistical software packages that they came across during their studies. But, in the data science field<\/b><\/a>, these programs have some limitations that the sheer mass of data being created won\u2019t allow data scientists of the future capitalize on all the insights one can derive from it.<\/p>\n <\/p>\n Why Excel won\u2019t be your best friend in data science<\/i><\/p>\n When it comes to Excel, Quartz explains<\/b><\/a> \u201cExcel cannot handle datasets above a certain size, and does not easily allow for reproducing previously conducted analyses on new datasets.\u201d<\/p>\n <\/p>\n And when it comes to proprietary statistical software like SAS<\/i><\/p>\n Quartz tells us<\/b><\/a> \u201c\u2026that they were developed for very specific uses, and do not have a large community of contributors constantly adding new tools.\u201d<\/p>\n <\/p>\n To move beyond what you could get from the more widespread, albeit limited tools we prefaced with above, three programming languages are said to be the bread and butter<\/b><\/a> of the discipline: R, Python, and Hadoop<\/b>. While the IE Data Science Bootcamp does not teach Hadoop, the curriculum covers both R and Python. As part of the schedule, you’ll grasp core concepts in both programming languages.<\/p>\n <\/p>\n R<\/b><\/p>\n This programming language is said to be an absolute must<\/i><\/b><\/a> in data science, with some even calling it the \u201cgolden boy.\u201d What makes R so alluring? Around since 1992<\/b><\/a>, \u201cwas developed explicitly for data analysis by statisticians looking for an\u00a0open-source<\/a>\u00a0solution that could replace expensive legacy systems like SAS and MATLAB.\u201d<\/p>\n <\/p>\n Another essential thing to know about R is that it\u2019s a procedural programming language<\/b><\/a>. <\/b>For those not familiar with different classes of programming languages, the adjective gives us a clue, because:<\/b><\/a> \u201cit works by breaking down a programming task into a series of steps, procedures, and subroutines.\u201d<\/p>\n <\/p>\n Python<\/b><\/p>\n If you don\u2019t have any previous coding experience, Python may be more your speed. That\u2019s because, with Python<\/b><\/a>, you won\u2019t end up having to write as much code as with other programming languages. And Python also is a big part of something you do every day when you Google something: that\u2019s because Python powers<\/b><\/a> Google search engine.<\/p>\n <\/p>\n Python\u2019s raison d\u2019etre <\/i>since 1989 has revolved around making the code efficient and readable.<\/b><\/a> In contrast to R, Python<\/b><\/a> falls under the object-oriented programming category like Java, C++, or Scala, meaning that \u201c\u2026it groups data and code into objects that can interact with and even modify one another.\u201d<\/p>\n <\/p>\n Both R and Python are<\/span> free<\/b><\/a>, which is undoubtedly advantageous for potential data scientists of all stripes and income brackets. Their <\/span>dedicated communities<\/b><\/a> of users have come up with applications specific to each programming language, giving a tremendous amount of resources for your particular applications.<\/span><\/p>\n <\/p>\n With its origins as a programming language created to replace legacy statistical applications, <\/span>R <\/b>comfortably <\/span>shows<\/b><\/a> how one carries out complicated operations. <\/span>Plus<\/b><\/a>,<\/span><\/p>\n R\u2019s analysis-oriented community has developed open-source packages for specific complex models that a data scientist would otherwise have to build from scratch. R also emphasizes\u00a0<\/span>quality reporting<\/span><\/a>\u00a0with support for clean visualizations and the Shiny framework for creating interactive web applications.\u00a0<\/span><\/p><\/blockquote>\n It also works with Windows, MacOS, and UNIX, ensuring that you won\u2019t have any limitations where your computer\u2019s operating system is concerned. And when it comes to the type of work best suited, Quartz<\/span> says<\/b><\/a> \u201c<\/span>R is good for ad hoc analysis and exploring datasets.<\/span>\u201d But, R\u2019s most specific advantage is the fact that <\/span>R proficiency practically is a guarantee of you getting work. <\/b><\/a><\/p>\n When it comes to <\/span>Python<\/b><\/a>, it\u2019s often the programming language of choice when the time comes to create a fast-access application because of its high level of performance. It\u2019s also easier to learn because \u201c<\/span>it is a more general programming language: For those interested in doing more than statistics, this comes in handy for building a website or making sense of command-line tools. The way Python works reflects the way\u00a0<\/span>computer programmers think<\/span><\/a>.<\/span>\u201d Quartz also<\/span> says<\/b><\/a> \u201c<\/b>Python is better for data manipulation and repeated tasks.\u201d<\/span><\/p>\n <\/p>\n At the end of the day, while there has long been a debate over which one is better, the <\/span>language of choice for data analysis<\/b><\/a> doesn\u2019t matter because many tasks that were once associated exclusively with one can be done using both programming languages. <\/span><\/p>\n <\/p>\n What should be your rule of thumb?<\/i><\/b><\/p>\n The choice of programming language will depend on <\/span>what your colleagues are using<\/b><\/a>, so if everyone on your team gravitates towards Python, then follow suit. <\/span><\/p>\n <\/p>\n To learn more about the <\/span>IE Data Science Bootcamp<\/b><\/a>, download your copy of our informational booklet <\/span>here<\/b><\/a>. And, if you\u2019re ready to apply for our next intake, <\/span>get started on your application<\/b><\/a>. <\/span><\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Why Excel and other statistical tools aren\u2019t enough Microsoft Excel is possibly one of the most popular programs that professionals of all stripes use to crunch [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":7718,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[384],"tags":[390],"_links":{"self":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7715"}],"collection":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/comments?post=7715"}],"version-history":[{"count":3,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7715\/revisions"}],"predecessor-version":[{"id":7719,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/posts\/7715\/revisions\/7719"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/media\/7718"}],"wp:attachment":[{"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/media?parent=7715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/categories?post=7715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ie.edu\/lifelong-learning\/blog\/wp-json\/wp\/v2\/tags?post=7715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What do you learn to get to the next step?<\/h4>\n
What does each programming language give you?<\/h4>\n
Ready to gain the versatility of learning both standard programming languages?<\/h5>\n