Data Science Eligibility Criteria

What is Data Scientist Eligibility Criteria

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Data Scientist Eligibility Criteria: “Data science is meant for engineers” is as untrue as “Data Science is for everyone”. In fact, there is a way to quickly check if you are cut out to be a data scientist.

Careers in data science are highly lucrative and have widely been considered to be among the most sought-after ones in this day and age. From engineering undergrads to maths, economics, and even humanities scholars, the era of data has witnessed individuals from various disciplines making a beeline for the domain. In fact, data science is often associated with multidisciplinarity since it relies on myriad branches of study.

From engineering to policy-making, sociology to biology, adeptness in data science is witnessing a continuous increase in demand for the last few years. Thus, if you are drawn to the discipline, do not worry about your educational background or job prospects since data science has truly become multidisciplinary in recent years. Although it is unfair to label it as “not everybody’s cup of tea”, there is a set of attributes which bode well for a career in the sphere.

What are Data Science Eligibility Criteria

Appetite for problems:

An aptitude for solving problems is considered to be a must for those considering a career in data science since there are distinctive stages in which a particular question is formulated and then addressed. This includes choosing suitable tools and using them for building a working model, validating and testing it on data, and iterating over the process with apt adjustments multiple times. This may end up getting too onerous if there is a dearth of enthusiasm.

At the same time, it is fallacious to believe that data analysts spend most of their time on visualization and deriving final insights in the quintessential corporate setup, for instance. Data cleansing is arguably the most essential part of the process, while the other stages follow later on. This one exercise drives the decision-making process largely and it is bound to get monotonous if the problem isn’t understood appropriately.

This is where business acumen needs to be mentioned as well. A thorough understanding of the industry is indispensable even if it may seem that all a data scientist does is crunching insights and implementing algorithms. Economics and its allied subject, econometrics, find wide application and are reputed to arm a prospective candidate with logical thinking and sound decision-making.

For example, a data professional in healthcare cannot slide into a similar role seamlessly into the shoe industry unless she has a deep understanding of the intricacies of the latter. Revealing the right trends is outright difficult for the unacquainted. This enables data science to supplement, rather than supplant, your previous knowledge from any given undergrad course.

While social scientists bank on public surveys to test and validate hypotheses and mold new governance policies, managers utilize previous sales data to forecast trends and adapt their next product or marketing strategy to the whims of their customers. And these are just a few use cases that demonstrate the domain-agnostic nature of data science!

Analytical Prowess

Having an analytical mindset augurs well for the discipline since statistics and probability are the basic building blocks of the concepts that one requires to understand and be thorough with, in order to build a career in data science. A common misinterpretation is to believe that one has to be a math whiz or belong to an engineering/technical background to do so. With the right motivation and instruction, you can train yourself to grasp these subjects better.

While it may seem like a steep learning curve, most of the ideas are based upon the matter one acquires in secondary and higher secondary schooling. Sheer fascination for making sense of numbers ends up being monumental for those looking to pursue a career in data science. Hence, do not let your hesitation hinder you from venturing into data science.

The same holds true for technical skills as well. Programming languages like Python, R, Scala, SAS, Julia, etc. can be learned at any stage of one’s academic journey. Being proficient in Python is looked upon highly owing to its lucidness, multifaceted usage, and ability to deal with a wide range of problems. In fact, it is being widely adopted as a “first” programming language these days.

Though, there is a popular misconception that urges beginners to know it all before applying their learnings. Working knowledge of Python goes a long way in the journey; it’s essential to “do in order to understand”, to quote Confucius. One does not need to be a programmer to pursue a career in data science.

Many learn these languages autodidactically as most of these still aren’t a part of the curriculum of most college courses in the country. The web is a repository of a vast amount of resources and comes in quite handy in a variety of situations. This serves to explain why many data science professionals swear by the adage, “Google Search is my best friend!”

Drive and soft skills

Curiosity is indispensable for learning any imaginable skill. This is especially important in the realm of data science, where principles and methodologies tend to get abstract and thus unappealing, for the ones without a craving for making data their own. Perseverance is an under-appreciated skill too, with projects often stretching long and having a tendency to digress from the stipulated problem.

Adaptability bodes well too, since new trends and tools keep emerging all the time. One has to keep learning in order to enhance their existing skills and to endow oneself with requisite tools which may become imminent and even replace the existing ones, with changing goals and new market requirements.

The art of storytelling and sound communication is quite essential since analysts often need to communicate their findings to those from departments like sales, management, etc., who may not be able to make sense of the process. Often, they do not seek to know how and why a particular procedure was followed, whilst being content only with the final findings. Therefore, effective presentation is a crucial requirement for those considering a career in data science.

It is perfectly normal to find these points intimidating initially. The number of applications of data science and allied fields may seem bewildering, with machine learning and artificial intelligence getting the most limelight from international media and experts. There are buzzwords like regression, classification, clustering, etc. and the list goes on and on.

At times, it would be seen that it isn’t a dearth of data science resources that is disconcerting, but their sheer amount, which poses an unending confusion. One’s choices make them believe that their chance at a career in data science stands jeopardized. This is where Pickl.AI can become a part of your data science journey! Learn it all in one place, in a fun and interactive way.

While you’d receive industry-relevant knowledge from seasoned data scientists, you will also be equipped with an appreciation of data’s power and its pertinence in making an argument, not just another opinion. So, what are you waiting for?


  • Ayush Pareek

    Written by:

    I am a programmer, who loves all things code. I have been writing about data science and other allied disciplines like machine learning and artificial intelligence ever since June 2021. You can check out my articles at I have been doing my undergrad in engineering at Jadavpur University since 2019. When not debugging issues, I can be found reading articles online that concern history, languages, and economics, among other topics. I can be reached on LinkedIn and via my email.