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“Data scientist is the sexiest job of the century.”
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 the special one cut out for that data science role?
From years of interviewing for analytics roles at Gramener, I’ve 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.
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’t display a focus on analytics, leave alone a passion for it. And, their answers don’t give subtlest of hints for a fire burning deep inside, for data.
Isn’t it ironical to make a case for a data job on purely emotional grounds, unbacked by factual evidences?
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” — Jim Barksdale
Every time a ‘data science’ buzzword is uttered, the next batch of analytics aspirants graduate from a finishing school. While I’m not sure whether data scientist open positions run into the millions as per analyst reports, what’s beyond doubt is that analytics training courses are doing roaring business.
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 tries to conjure it up, given the obviously favourable economics; even when their educational background or work experience is not too favourable.
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.
1. Read, write and get conversant with data
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Candidates often get tongue-tied when quizzed beyond the terminologies listed in one’s 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 social manipulation.
There are candidates, who inspite years of work experience in, say logistic regression haven’t learnt simple neural networks, or bothered to get curious on why the world is going crazy over deep neural networks. These patterns don’t do justice to the claims of deep passion to be a part of this industry.
One must go beyond the syllabus of analytics courses. It’s critical to gain breadth in analytics, in addition to depth in a few core techniques. Explore avenues like blogs, books, podcasts or videos, and get hooked to data content. Information is in over-abundance today, it is time and interest that’s in short supply.
2. Execute your own data projects
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One can almost identify the finishing school by looking at templatized ‘live projects’ on 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.
It’s 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.
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 live project. It is the initiative and effort that’s more important and this is sure to get one noticed.
3. Display your wares in public
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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.
The web is a wonderful place brimming with possibilities. Put up your code on Github, show your technical competence by busting bugs on Stackoverflow. Flaunt your data visualisation skills on the public visualisation portals. Show your thought leadership in data science by penning blogs on Medium.
Today, there are a gazillion ways to hawk your wares in the public. It’s no more a question of balancing modesty with showmanship. This has become a bare minimum necessity to establish one’s credibility, while also helping unshackle the job pitch from the confines of a PDF document.
4. Get competitive with data scientists
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Today when corporate hiring is switching over to public hackathons and coding contests, a candidate who has never competed on platforms like Kaggle looks like a time traveller from the dotcom era.
A no-brainer to demonstrate passion is by huddling into the competitive arena. One needn’t figure in the contest leaderboards to score. As with any sport, it’s all about taking the initiative, competing at the right skill level and persisting with a few quality entries, more than targeting definitive outcomes.
It’s raining data contests across skills, like Kaggle, InformationIsBeautiful and open data competitions. 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.
5. Start to see (eat, sleep and dream) data in everything you do
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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’s data, shouldn’t they care enough to do the simplest of things with data that they own and produce.
A person who is really into data will start seeing numbers living and breathing all around them. Whether it’s by extracting data from your phones and fitness trackers, or downloading the data we’ve liberally donated to Google, Facebook or Linkedin, take these for a spin using your newfound data skills.
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.
The shortcut to demonstrating a strong passion in analytics is to cultivate some genuine interest in data.
If you’re 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.