{"id":951,"date":"2018-10-29T05:23:21","date_gmt":"2018-10-29T02:23:21","guid":{"rendered":"http:\/\/kusuaks7\/?p=556"},"modified":"2021-12-20T11:07:05","modified_gmt":"2021-12-20T11:07:05","slug":"how-to-flaunt-your-passion-for-analytics-in-data-science-job-interviews","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/how-to-flaunt-your-passion-for-analytics-in-data-science-job-interviews\/","title":{"rendered":"How to flaunt your Passion for analytics in Data science job interviews?"},"content":{"rendered":"

Ready to learn Data Analytics? Browse Data Analyst Training and Certification courses<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

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\u201cData scientist is the sexiest job of the century.\u201d<\/p>\n<\/blockquote>\n

And everyone wants a pie of the action. Millions of aspirants are after those limited, meaty positions and are willing to give a limb for it. So, how does one really show that they are\u00a0the special one\u00a0<\/strong>cut out for that data science role?<\/p>\n

From years of interviewing for analytics roles at\u00a0Gramener<\/a>, I\u2019ve often come across candidates who try to call themselves apart by claiming to have a deep passion for analytics. Some even go on to claim that data runs in their blood.<\/p>\n

But in reality, nothing really sets most candidate resumes apart from the thousands that apply for the position, everyday. Their projects or career interests don\u2019t display a focus on analytics, leave alone a passion for it. And, their answers don\u2019t give subtlest of hints for a\u00a0fire burning deep inside<\/em>, for data.<\/p>\n

Isn\u2019t it ironical to make a case for a data job on purely emotional grounds, unbacked by factual evidences?<\/p>\n

\u201cIf we have data, let\u2019s look at data. If all we have are opinions, let\u2019s go with mine.\u201d\u200a\u2014\u200a<\/em>Jim Barksdale<\/strong><\/p><\/blockquote>\n

Every time a \u2018data science\u2019 buzzword is uttered, the next batch of analytics aspirants graduate from a finishing school. While I\u2019m not sure whether data scientist open positions run into the millions as per analyst reports, what\u2019s beyond doubt is that analytics training courses are doing roaring business.<\/p>\n

Given the skewed demand-supply situation, one has to go beyond ticking all the boxes in a JD checklist. Either one innately has a passion for data, or\u00a0tries to conjure it up<\/em>, given the obviously favourable economics; even when their educational background or work experience is not too favourable.<\/p>\n

So, the big challenge often comes down to demonstrating and defending this passion for analytics in a data science interview. How can one go about doing this to stand out from the crowd? Drawing from my experience and from what many practitioners look for in candidates, here are 5 steps to achieving this.<\/p>\n

1. Read, write and get conversant with\u00a0data<\/h4>\n
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Photo by\u00a0Annie Spratt<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

Candidates often get tongue-tied when quizzed beyond the terminologies listed in one\u2019s CV. While one might explain sentiment analysis and lay out an approach to extract behavioural insights from social media data, they must also know how such harvesting has been misused for\u00a0social manipulation<\/a>.<\/p>\n

There are candidates, who inspite years of work experience in, say logistic regression haven\u2019t learnt simple neural networks, or bothered to get curious on why the world is going crazy over\u00a0deep neural networks. These patterns don\u2019t do justice to the claims of deep passion to be a part of this industry.<\/p>\n

One must go beyond the syllabus of analytics courses. It\u2019s critical to gain breadth in analytics, in addition to depth in a few core techniques. Explore avenues like\u00a0blogs<\/a>,\u00a0books<\/a>,\u00a0podcasts<\/a>\u00a0or\u00a0videos<\/a>, and get hooked to data content. Information is in over-abundance today, it is time and interest that\u2019s in short supply.<\/p>\n

2. Execute your own data\u00a0projects<\/h4>\n
<\/canvas><\/figure>\n

Photo by\u00a0Laith Abuabdu<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

One can almost identify the finishing school by looking at templatized\u00a0\u2018live projects\u2019<\/em>\u00a0on candidate CVs. Expert interviewers tend to automatically gloss over insincere content and undifferentiated projects. Laterals often commit the crime of not applying mind to projects beyond the direct line of work.<\/p>\n

It\u2019s insincere to not back passion for data with a personal project, even if rudimentary. Open data movements are democratising data around the world, and every world event is leaving a huge trail of data, whether one likes it or not. A few google searches can fetch data in any field of interest.<\/p>\n

One just needs to pick a problem and apply the analytics learnings as a small side project. An imperfect solution to a personal data project is far more compelling than a perfect answer to a paid\u00a0live\u00a0<\/em>project. It is the initiative and effort that\u2019s more important and this is sure to get one noticed.<\/p>\n

3. Display your wares in\u00a0public<\/h4>\n
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Photo by\u00a0Annie Spratt<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

Aspirants who bottle their competence to the confines of a CV or a staid LinkedIn page are just chaining themselves in broad daylight, in the job marketplace. One of the first things I look for in a CV are links to external websites, portals or any public repository which can back the candidature.<\/p>\n

The web is a wonderful place brimming with possibilities. Put up your code on\u00a0Github<\/a>, show your technical competence by busting bugs on\u00a0Stackoverflow<\/a>. Flaunt your data visualisation skills on the public\u00a0visualisation<\/a>\u00a0portals<\/a>. Show your thought leadership in data science by penning blogs on\u00a0Medium<\/a>.<\/p>\n

Today, there are a gazillion ways to hawk your wares in the public. It\u2019s no more a question of balancing modesty with showmanship. This has become a bare minimum necessity to establish one\u2019s credibility, while also helping unshackle the job pitch from the confines of a PDF document.<\/p>\n

4. Get competitive with data scientists<\/h4>\n
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Photo by\u00a0Timothy Eberly<\/a>\u00a0on\u00a0Unsplash<\/a><\/p>\n

Today when corporate hiring is switching over to public hackathons and coding contests, a candidate who has never competed on platforms like\u00a0Kaggle<\/a>\u00a0looks like a time traveller from the dotcom era.<\/p>\n

A no-brainer to demonstrate passion is by huddling into the competitive arena. One needn\u2019t figure in the contest leaderboards to score. As with any sport, it\u2019s all about taking the initiative, competing at the right skill level and persisting with a few quality entries, more than targeting definitive outcomes.<\/p>\n

It\u2019s raining data contests across skills, like\u00a0Kaggle<\/a>,\u00a0InformationIsBeautiful<\/a>\u00a0and\u00a0open data<\/a>\u00a0competitions. From a skill-building perspective, there is no better way to learn than by applying. Connecting with people, competing in teams and hustling with fellow data scientists serves to accelerate this process.<\/p>\n

5. Start to see (eat, sleep and dream) data in everything you\u00a0do<\/h4>\n
<\/canvas><\/figure>\n

Photo by Radu Florin on Unsplash<\/a><\/p>\n

Google is powered by your data. Facebook harvests it. Companies you never knew existed have built entire business models with your data trails. When one takes up fancy predictive analytics with other\u2019s data, shouldn\u2019t they care enough to do the simplest of things with data that they own and produce.<\/p>\n

A person who is really into data will start seeing numbers living and breathing all around them. Whether it\u2019s by extracting data from your phones and fitness trackers, or downloading the data we\u2019ve liberally donated to\u00a0Google<\/a>,\u00a0Facebook<\/a>\u00a0or\u00a0Linkedin<\/a>, take these for a spin using your newfound data skills.<\/p>\n

Think up use cases and put data to some fun, or personal use. Use data to decide when to buy the cheapest tickets? Why not build a personalised model to recommend yourself the next movie? Eat your own dogwood and have fun with data, since these tidbits also make for spectacular stories in an interview.<\/p>\n

Summary<\/h4>\n

The shortcut to demonstrating a strong passion in analytics is to cultivate some genuine interest in\u00a0data.<\/p><\/blockquote>\n

If you\u2019re serious about a job with data, go the whole hog and evolve into a numbers person. Perhaps like wine, data has an acquired taste. One just needs to buckle up and get started on this earnest journey, to enjoy the highs it invariably bestows.<\/p>\n","protected":false},"excerpt":{"rendered":"

A person who is really into data will start seeing numbers living and breathing all around them. The shortcut to demonstrating a strong passion in analytics is to cultivate some genuine interest in data. The big challenge often comes down to demonstrating and defending the passion for analytics in a data science interview. How can one go about doing this to stand out from the crowd? Drawing from my experience and from what many practitioners look for in candidates, here are 5 steps to achieving this. <\/p>\n","protected":false},"author":315,"featured_media":3317,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[1994],"class_list":["post-951","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":1994,"user_id":315,"is_guest":0,"slug":"ganes-kesari","display_name":"Ganes Kesari","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2021\/05\/Ganes_Kesari-150x150.jpeg","user_url":"http:\/\/gramener.com","last_name":"Kesari","first_name":"Ganes","job_title":"","description":"Ganes Kesari is the Co-founder and Chief Decision Scientist at Gramener<\/a>, a data science company that helps organizations present data insights as stories. He advises executives on data-driven leadership and helps organizations adopt a culture of data for decision-making. He is a TEDx speaker and Contributor to Forbes and Entrepreneur. Find his latest work here<\/a> and reach out to him on LinkedIn<\/a>, where he shares insights regularly."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/951","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/315"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=951"}],"version-history":[{"count":6,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/951\/revisions"}],"predecessor-version":[{"id":28439,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/951\/revisions\/28439"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3317"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=951"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=951"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=951"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}