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 favor 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 thrust 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 the years. Data scientist is a loosely used term, a title that’s heavily abused in the industry. Quite like Big Data or, 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’!
So, what does it take to enter and succeed in Data science?
Fuelled by such confusion, 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 of 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 customized meal. And yes, we’ll also unwrap the secret sauce to becoming a unicorn in this industry.
The spectrum of Skills in Data science
There are 5 skills that are central to data science. To emphasize 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, let's talk about the complete listing of competencies needed in a project, in order to deliver business value.
The spectrum of the key data science skills
1. Data Bootstrap skills (or) Data science literacy
Passion for numbers is a pre-condition for success in data science, and a great asset. One must pick up 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 the analysis and results of data techniques are like an unpolished diamond. They are valuable to a trained eye, but worthless in a marketplace.
It's invaluable to pick up 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 pick up 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.
2. Information Design and Presentation
Information design is the presentation of data in a way that fosters effective and efficient understanding.
The emphasis is on visual design that enables the consumption of data, rather than just beautification. Visualization is the last mile of communication that enables users to get value from analytics.
To become an expert in this area, one must master the design skills spanning the realms of interaction design, user experience, and data visualization.
This calls for expertise in user mapping, information architecture, data representation, wireframing, high fidelity design and visual aesthetics.
The greatest value of a picture is when it forces us to notice what we never expected to see — John Tukey
3. Statistics and Machine learning
In most data science courses, this area is devoted a lion’s share of attention. The focus here is on statistics and modeling, while scripting or programming is a secondary skill.
While this is a key area for extraction of value from data, an over-emphasis here may take the focus off other 4 key skills in data science.
Building upon the basic data wrangling skills, one must dive in deeper into statistics, probability and then branch out into the techniques and algorithms in machine learning.
Deep learning and other trending AI techniques fall into this bucket as well, but these call for more extensive coding skills.
4. Deep programming
People with a core programming background have great use in data science, and it’s not mandatory for them to pick up machine learning skills.
Data applications call for backend coding to connect and handle data, need heavy lifting with data processing and building out the internals of data science apps.
There are also strong needs for front-end coding skills to showcase the data insights to users, which is where the rubber really meets the road.
5. Domain expertise
Deep domain skills help bring in meaning, interpretability, and actionability to analytics. The importance of blending domain expertise with data skills cannot be overemphasized.
Lack of sufficient attention in this area throughout a project is the most common cause of failure for initiatives.
Picking up the depth in a chosen domain and mastering the business flows is the first step here. One must then focus on data-literacy and a conceptual understanding of analytical techniques.
This brings in the know-how to weave a tight fabric by combining domain skills with data chops, for superior value.
“If you do not know how to ask the right question, you discover nothing. — W. Edward Deming”
“What’s the secret sauce to transforming into a Unicorn in Data Science?”– Ganes Kesari Tweet