What’s the secret sauce to transforming into a Unicorn in Data Science?

Every aspirant in data science has the question “What skills do I need to enter the industry?”, closely followed by “How do I become highly sought after in this job market?” While the industry is hot with a skewed demand-supply that’s in favour of trained professionals, getting the mix of skills right is not easy.

Now it’s common knowledge that ‘data scientist is the sexiest job of the century’. But what role does this exactly refer to? The very mention of this title conjures up images of math wizards sweating it out in multivariate calculus and linear algebra, or of geeks coding to create the next general artificial intelligence.

And then, one is also thrusted upon with busy venn diagrams that call for mastery of a laundry list of skills. These add up to areas that a team of people may have mastered amongst them, over years. Data scientist is a loosely used term, a title that’s heavily abused in the industry. Quite like Big Dataor, say AI.

In practice, the title is often used as an umbrella term for related roles and is variously interpreted by companies in the industry. I’ve come across many people who’ve confessed to me in private, “Give me any job and role, but please coin me a job title with some play of these 2 words — ‘data’ and ‘scientist’!

Fuelled by such confusions, people wonder whether they must learn programming to have a go at a career in data science. For others, statistics or machine learning may not be their cup of tea. These then appear to be stumbling blocks for making any advances into the analytics field.

This particularly perplexes laterals who have developed an interest in data, but, say have 10 years in an unrelated role, in a different industry. The assumption of having to learn coding or design afresh to restart their career stumps them. These misconceptions must be forcefully put to rest, lest they continue crushing dreams of a career in data science.

So, what’s a realistic expectation on the skills needed to make a career in data science? And, can aspirants pick and choose skills of interest to carve out a preferred role, one that builds on strengths, while also being in demand?

We’ll first present the spectrum of skills that are needed in data science, like a buffet menu. Then we’ll construct the key industry roles that deliver analytics value, by picking and choosing from amongst these skills, like a customised meal. And yes, we’ll also unwrap the secret sauce to becoming a unicorn in this industry.

There are 5 skills that are central to data science. To emphasise this again, no, one doesn’t need to learn them all. We’ll cover the roles and the mix of skills that each role entails, in the next section. First, lets talk about the complete listing of competencies needed in a project, in order to deliver business value.

Passion for numbers is a pre-condition for success in data science, and a great asset. One must pickup data wrangling skills to get a feel for data — compute averages, fit cross tabs and extract basic insights through exploratory analysis. It’s the approach that matters and any tool, say Excel, R or SQL will do.

Insights from analysis and results of data techniques are like an unpolished diamond. They are valuable to a trained eye, but worthless in a marketplace. Its invaluable to pickup the presentation and basic design skills to polish those nuggets of insights. This makes one’s efforts effective and worthwhile.

Data handling and basic design must be topped up with a good orientation of a chosen domain. Techniques with data are only as good as their adaptation to a business problem. This basic knowledge can’t be outsourced to a business analyst, so anyone serious about data science should pickup domain basics.

In short, this skill requires one to befriend data and train their eyes to spot patterns in numbers. This is a fundamental skill that is non-negotiable in analytics.