{"id":804,"date":"2016-09-05T16:11:50","date_gmt":"2016-09-05T14:11:50","guid":{"rendered":"https:\/\/blog.zhaw.ch\/datascience\/?p=804"},"modified":"2016-11-09T15:22:35","modified_gmt":"2016-11-09T13:22:35","slug":"data-scientists-type-a-b-nonsense","status":"publish","type":"post","link":"https:\/\/blog.zhaw.ch\/datascience\/data-scientists-type-a-b-nonsense\/","title":{"rendered":"Data scientists type A &amp; B? Nonsense."},"content":{"rendered":"<p>By Thilo Stadelmann (ZHAW)<\/p>\n<p><em>Reposted from <a href=\"https:\/\/dublin.zhaw.ch\/~stdm\/?p=350#more-350\">https:\/\/dublin.zhaw.ch\/~stdm\/?p=350#more-350<\/a><\/em><\/p>\n<p>I <a href=\"http:\/\/www.kdnuggets.com\/2016\/08\/become-type-a-data-scientist.html\" target=\"_blank\">recently came about the notion<\/a> of \u201ctype A\u201d and \u201ctype B\u201d data scientists. While the \u201ctype A\u201d is basically a trained statistician that has broadened his field towards modern use cases (\u201cdata science for people\u201d), the same is true for \u201ctype B\u201d (B for \u201cbuild\u201d, \u201cdata science for software\u201d) that has his roots in programming and contributes stronger to code and systems in the backend.<\/p>\n<p>Frankly, I haven\u2019t come about a practically more useless distinction since the inception of the term \u201cdata science\u201d.<span id=\"more-350\"><\/span> Data science is the name for a new discipline that is in itself interdisciplinary [<a href=\"http:\/\/pd.zhaw.ch\/publikation\/upload\/206249.pdf\" target=\"_blank\">see e.g. here<\/a> &#8211; but beware of German text]. The whole point of interdisciplinarity, and by extension of data science, is for proponent to think outside the box of his or her original discipline (which might be be statistics, computer science, physics, economics or something completely different), and acquire skills in the neighboring disciplines in order to tackle problems outside of intellectual silos. Encouraging practitioners to stay in their silos, as this A\/B typology suggests, is counterproductive at best, fatal at worst.<!--more--><\/p>\n<p>If you want to counter the infamous \u201cunicorn\u201d description of a data scientist who is an expert in each and everything, take the distinction my\u00a0<a href=\"https:\/\/www.sheffield.ac.uk\/is\/pgt\/courses\/data_science\/dsqanda\" target=\"_blank\">colleagues from the University of Sheffield introduced to me<\/a>: While the \u201ctype I\u201d data scientist is himself a manager, bothered with hiring and leading data practitioners and having a more high level view of data sciences\u2019 potentials and workings, a \u201ctype II\u201d data scientist knows how to <a href=\"https:\/\/www.youtube.com\/watch?v=U7wLM77curg\" target=\"_blank\">\u201cdo the stuff\u201d<\/a> technically. This opens up the way to combined curricula for manager-type people and technically-oriented people.<\/p>\n<p>But to me, the attempt to distinguish \u2013 isolate \u2013 sub-types of \u201ctype II\u201d data scientists, is just re-labeling of good but a little bit old-fashioned names we once where used to (statistician, business analyst, BI specialist, data miner, database engineer, software engineer, etc.). There\u2019s nothing wrong with these titles if they fit the job. To re-label them only because \u201cdata scientist type A\u201d is more fashionable, might be good for the individual\u2019s self esteem; <strong>it is dangerous nonsense for the industry and (scientific) discipline as a whole.<\/strong><\/p>\n<p><em>[<b class=\"b2\">pers<\/b><b class=\"b3\">ona<\/b><b class=\"b4\">lly<\/b><b class=\"b5\"> signed contributions <\/b><b class=\"b4\">ref<\/b><b class=\"b3\">lec<\/b><b class=\"b2\">t th<\/b><b class=\"b1\">e <\/b>opinions of their author and not necessarily those of datalab]<\/em><\/p>\n<div class=\"pt-sm\">Schlagw\u00f6rter: <a href=\"https:\/\/blog.zhaw.ch\/datascience\/tag\/data-science\/\">Data Science<\/a>, <a href=\"https:\/\/blog.zhaw.ch\/datascience\/tag\/definition\/\">Definition<\/a>, <a href=\"https:\/\/blog.zhaw.ch\/datascience\/tag\/skill-set-map\/\">skill set map<\/a><br><\/div>","protected":false},"excerpt":{"rendered":"<p>By Thilo Stadelmann (ZHAW) Reposted from https:\/\/dublin.zhaw.ch\/~stdm\/?p=350#more-350 I recently came about the notion of \u201ctype A\u201d and \u201ctype B\u201d data scientists. While the \u201ctype A\u201d is basically a trained statistician that has broadened his field towards modern use cases (\u201cdata science for people\u201d), the same is true for \u201ctype B\u201d (B for \u201cbuild\u201d, \u201cdata science [&hellip;]<\/p>\n","protected":false},"author":265,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[1,7,21],"tags":[5,20,22],"features":[],"class_list":["post-804","post","type-post","status-publish","format-standard","hentry","category-allgemein","category-blog","category-philosophy","tag-data-science","tag-definition","tag-skill-set-map"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Data scientists type A &amp; B? Nonsense. - Data Science made in Switzerland<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.zhaw.ch\/datascience\/data-scientists-type-a-b-nonsense\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data scientists type A &amp; B? Nonsense.\" \/>\n<meta property=\"og:description\" content=\"By Thilo Stadelmann (ZHAW) Reposted from https:\/\/dublin.zhaw.ch\/~stdm\/?p=350#more-350 I recently came about the notion of \u201ctype A\u201d and \u201ctype B\u201d data scientists. 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