What is Data Scientist Eligibility Criteria?

Summary: To become a data scientist, you need problem-solving skills, analytical prowess, and programming knowledge. A background in math or engineering helps, but various disciplines can lead to success in this multidimensional field.

Introduction

“Data science is meant for engineers” is as untrue as “Data Science is for everyone”. 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 are widely considered to be among the most sought-after ones today. From engineering undergrads to math, economics, and even humanities scholars, the era of data has witnessed individuals from various disciplines making a beeline for the domain. 

Data science often associated with multidisciplinarity since it relies on myriad branches of study.

From engineering to policy-making, sociology to biology, adeptness in data science has witnessed 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 recently become multidisciplinary. 

Although it is unfair to label it as “not everybody’s cup of tea,” there are a set of attributes that bode well for a career in the field.

Appetite for problems

An aptitude for solving problems considered a must for those considering a career in data science since there are distinctive stages in which a particular question formulated and then addressed. 

This includes choosing suitable tools and using them to build a working model, validating and testing it on data, and iterating over the process with apt adjustments multiple times. This may get 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 visualisation and deriving final insights in the quintessential corporate setup. Data cleansing is arguably the most essential part of the process, while the other stages follow later on. 

This one exercise largely drives the decision-making process and bound to get monotonous if the problem isn’t understand 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 a data scientist only crunches insights and implements 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 seamlessly transition into a similar role in the shoe industry unless she has a deep understanding of the latter’s intricacies. 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 mould new governance policies, managers utilise previous sales data to forecast trends and adapt their next product or marketing strategy to their customers’ whims. These are just a few use cases that demonstrate the domain-agnostic nature of data science!

Analytical Prowess

An analytical mindset augurs well for the discipline since statistics and probability are the basic building blocks of the concepts that one must understand and be thorough with to build a career in data science. 

A common misinterpretation is to believe that one has to be a math whiz or have 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 ideas are based upon the matter one acquires in secondary and higher secondary schooling. The sheer fascination for making sense of numbers is monumental for those pursuing a career in data science. Hence, do not let your hesitation hinder you from venturing into data science.

The same holds for technical skills. 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 highly regarded owing to its lucidity, multifaceted usage, and ability to deal with a wide range of problems. It is being widely adopt as a “first” programming language.

However, a popular misconception urges beginners to know everything before applying what they have learned. Working knowledge of Python goes a long way in the journey; it’s essential to “do 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 they are still not part of most college courses’ curriculum. The web is a repository of a vast amount of resources and comes in handy in various situations. This explains 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 data science, where principles and methodologies tend to become abstract and thus unappealing to those who do not crave making data their own.

Perseverance is also an underappreciated skill, as projects often stretch long and tend to deviate from the stipulated problem.

Adaptability bodes well, too, since new trends and tools keep emerging constantly. One has to keep learning to enhance one’s existing skills and to endow oneself with requisite tools that may become imminent and even replace the existing ones, given those without cravings for changing goals and new market requirements.

The art of storytelling and sound communication is essential since analysts often need to communicate their findings to those from departments like sales, management, etc., who may be unable to make sense of the process. 

Usually, 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 data science applications 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 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 jeopardised. 

This is where Pickl.AI can join 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  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?

Frequently Asked Questions

What qualifications need to become a data scientist?

Becoming a data scientist typically requires a strong statistics, programming, and data analysis background. While degrees in engineering, mathematics, or related fields are common. Individuals from various disciplines with the right skills and passion can also succeed.

Do I need advanced math skills to be a data scientist?

While advanced math skills are beneficial, they are not mandatory. Basic knowledge of statistics and probability is crucial, and many data scientists develop their math skills over time through practical experience and learning resources.

Is programming essential for a data science career?

Yes, programming is essential in data science. Proficiency in languages like Python or R highly recommended. As they are widely use for data manipulation, analysis, and building models. However, one can start with basic programming knowledge and improve it progressively.

Conclusion

Embarking on a career in data science requires curiosity, analytical skills, and perseverance. While a strong background in math or programming is beneficial, the field is accessible to individuals from diverse educational backgrounds. To thrive in this evolving domain, embrace continuous learning and adaptability.