{"id":214,"date":"2020-02-20T16:55:12","date_gmt":"2020-02-20T16:55:12","guid":{"rendered":"http:\/\/51.75.254.207\/Club-Freelance\/?p=214"},"modified":"2022-10-10T16:16:36","modified_gmt":"2022-10-10T15:16:36","slug":"the-holy-trinity-of-data-science","status":"publish","type":"post","link":"https:\/\/wp-uk.mindquest.io\/?p=214","title":{"rendered":"The \u2018Holy Trinity\u2019 of Data Science"},"content":{"rendered":"

There are probably dozens
\nof variants<\/a> of the Venn diagram that Drew Conway proposed a few years ago
\nto capture the core skills of a data scientist. Needless to say, the role has experienced
\nmany changes since then, while rapid technological developments and the boom of
\nAI have further propelled the profession to the top of LinkedIn\u2019s emerging
\njobs ranking<\/a>. <\/p>\n

<\/div>\n
<\/div>\n

Well \u2014 we couldn\u2019t resist putting forward
\nour own version of the infamous Venn diagram. Like Conway\u2019s, ours is built on three
\naxes. However, our model focuses on broader categories rather than on specific expertise.
\nIn today\u2019s ever-changing business world, soft and cross-cutting skills are the
\ntruly decisive factors that, in the long run, can ensure adaptability and
\nsuccess. \u00a0<\/p>\n

Thus, our \u201choly trinity,\u201d if you will, of
\ndata science is made up of:<\/p>\n

    \n
  • Curiosity<\/li>\n
  • Technical know-how<\/li>\n
  • Collaboration<\/li>\n<\/ul>\n

    Thinking of a career in the field, or
    \nwondering if you\u2019re doing this right? Let\u2019s dive into each component. <\/p>\n

    <\/div>\n

    The importance of a curious mind<\/strong><\/p>\n

    Probably obvious, but it\u2019s impossible to
    \ntalk about science and not mention the innate curiosity that powers it. Whether
    \nyou plan to explore the possibility of life in other planets or the mysteries
    \nof quantum entanglement, it is the thirst for answers to questions and riddles
    \nthat will make you advance. <\/p>\n

    This, of course, applies to the problem-solving
    \ncapabilities required in data science projects. Nevertheless, well-directed technical
    \ninquiries tend to fall on shaky ground whenever there are not accompanied by a
    \ngood contextual understanding. Just because you\u2019re good at playing with data and
    \ncreating models that produce intricate insights and machine learning
    \nexperiences, none of it is worth anything if your work isn\u2019t helpful to the
    \noverarching goal. <\/p>\n

    For this reason, the need for curiosity
    \nexpands to the domain of expertise in which you operate (i.e. finance, political
    \nstudies, marketing). The more you know about the field of work of your company
    \nor department, the better questions you will ask yourself, the useful insights
    \nand models you will produce. <\/p>\n

    Note that we\u2019re highlighting \u201ccuriosity\u201d rather than \u201cknowledge.\u201d You\u2019re going to spend many hours working with this data. Make sure it\u2019s something that you are passionate about or at least find interesting. \u00a0<\/p>\n

    <\/div>\n

    Knowing the technical ins and outs<\/strong><\/p>\n

    Some describe a data scientist as someone
    \nwho knows more about math and statistics than your average programmer while
    \nhaving greater coding capabilities than your average mathematician. Although this
    \ndefinition errs on side of oversimplification, it is not totally misguided. <\/p>\n

    To be successful in data science, you need
    \nto be proficient in certain data engineering and coding-related methodologies
    \nand practices. It is important not only to know how to build effective code, but
    \nalso how to efficiently extract and clean data. <\/p>\n

    Additionally, there is the crucial
    \ntechnical knowledge that has less to do with computer engineering and more with,
    \nfor instance, data privacy compliance. You must know what data sets you can
    \nmanipulate and which ones you can\u2019t, which processes can be computed on the
    \ncloud and which ones are better reserved for on-premises infrastructure. At the
    \nsame time, if you work in finance or in any other field where sector-specific concepts
    \nare a basic requirement, you will have to dominate those on top of your
    \nknowledge of data science. <\/p>\n

    <\/div>\n

    Playing as a team<\/strong><\/p>\n

    This is where soft skills play the biggest
    \nrole. Interpersonal communication and teamwork have always been one of the key
    \nfactors of success Their relevance in this hyperconnected world of ours is only
    \nincreasing. <\/p>\n

    There must be good cooperation between all
    \nteams and stakeholders involved in the process, and, for that, you should be
    \nable to communicate efficiently and in a compelling way. It\u2019s not enough with working
    \nclosely with developers or analysts. Knowing how to present a project in
    \nlayman\u2019s terms becomes essential if you want to be granted the staff or
    \ncomputational power that you\u2019ll need to complete it.<\/p>\n

    Apart from this, you need to be well-versed
    \nin concepts like Agile development, which help teams streamline the production
    \npipeline. Version control, a unified repository, and a good understanding
    \nbetween development and production are a teamwork-must in today\u2019s IT world. \u00a0\u00a0<\/p>\n

    <\/div>\n","protected":false},"excerpt":{"rendered":"

    There are probably dozens of variants of the Venn diagram that Drew Conway proposed a few years ago to capture the core skills of a data scientist. Needless to say, the role has experienced many changes since then, while rapid technological developments and the boom of AI have further propelled the profession to the top […]<\/p>\n","protected":false},"author":6,"featured_media":217,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[763,752],"tags":[51,54,106,58],"class_list":["post-214","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-growing-career-permanent-freelance","category-it-consultants","tag-ai","tag-data-science","tag-data-scientist","tag-it-careers"],"acf":[],"_links":{"self":[{"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/posts\/214","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=214"}],"version-history":[{"count":7,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/posts\/214\/revisions"}],"predecessor-version":[{"id":6441,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/posts\/214\/revisions\/6441"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=\/wp\/v2\/media\/217"}],"wp:attachment":[{"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=214"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=214"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp-uk.mindquest.io\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=214"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}