5 Myths about a Career in Data Science: Debunked

. 4 min read

Data Science may not be a new field in the truest sense, but it has been continuously evolving. More recently, it has gained traction as one of the trendiest career paths for job aspirants. And with good reason.

Due to its potential for superior computing, huge data creation and storage abilities, and scalable machine learning algorithms, Data Science has presented many opportunities to individuals to explore. Additionally, there is tremendous scope in the field to develop new paradigms and solve problems in new ways.

However, just like any phenomenon with fast-paced growth, Data Science has also given rise to many myths about its roles and responsibilities. In this post, we’ll examine some of those myths closely, and put some of them to rest.

PhD is mandatory to succeed in the field


There is no doubt that having a PhD in Statistics or Mathematics is an advantage. It takes dedication, a lot of hard work and intense research - all of which are essential qualities for a Data Scientist. But is the qualification itself necessary?


Short answer: No. It’s not mandatory.

Data Science is a vast field, which includes roles like data engineering and data analysis. Neither of which require expertise in mathematics or statistics. If an individual has a good understanding of data and business, they can pick up Excel and coding in R, Python or Spark. Additionally, a dedicated learner can pick up machine learning concepts and stats to get a better mix of theoretical concepts and practical experience of data science projects.

The bottom line is that it is the combination of the methodology of doctoral studies and the subjects that gives rise to success in the field of Data Science, not the actual qualification.

All previous work experience should be in Data Science


You are a working professional with a few years of experience in a particular sector. However, you come across Data Science and the opportunities the field brings with it. But you can’t bring your previous experience into this new domain, can you?


There are two ways to get started on this new career path:

  • You change your entire domain to get into Data Science.
  • You look for a role in Data Science while sticking to your current domain.

Let’s understand how these scenarios are different from each other:

According to the first situation, if you are changing your domain entirely, your previous work experience will count for very little. Not only are you looking for a new job role, but in a completely new line of work. In this scenario, you will be starting from scratch - basically, at entry level. This may sound like a daunting prospect, but there are upsides to starting with a clean slate. The important consideration here is to understand the situation and set your expectations accordingly.

In the second situation, you may be new to the job role, but you have domain expertise. You are aware of all the nuances surrounding the domain. This will be counted as an added advantage, even if you are switching to a completely new career. Staying in the same field and understanding how Data Science can be applied there is a safer, more calculated approach overall.

Need to have a coding, computer science or mathematics background


Most people you meet in the field of Data Science are from a computer science or engineering background. Does that mean that an individual from a non-engineering or non-programming background cannot excel in Data Science?


Data Science is not a rigid field. It is quite widespread with several aspects. Discipline, hard work and dedication are the three cornerstones of the field. While working on different Data Science projects, keeping your mind open to learning new concepts and programming languages will propel you forward.

Data Scientists are basically Business Analysts


The Data Scientist role has gained widespread popularity among job aspirants only in the last 10 years or so. Because it is not rigidly defined, it is often felt to mean that same role as the old school Business Analyst. Is that the case?


A Data Scientist is partly a Business Analyst, but a Business Analyst is not a Data Scientist. A Business Analyst’s role included tasks like cleaning out data, sorting data and preparing it for analysis. However, when we talk about a Data Scientist, this job role also includes searching for and creating new data.

In a nutshell, a Business Analyst tells you what happened and when, but a Data Scientist can explain what, how and when it happened, in addition to data-backed predictions about whether it will happen again.

Data Scientists will be replaced by AI


In 2017, a report by Gartner estimated that more than 40% of data science tasks would be automated by 2020. As technology advances and the year approaches, people wonder: can AI replace data science professionals?


We ended with this one because it is just outlandish enough to be true - but it isn’t. A data science professional is much more than automated data tasks. Not only do they clean and sort data, but develop prediction models and derive usable business insights.

For instance, if we talk about Google’s Cloud Machine Learning Engine, it can create designs and evaluate architecture models. But it’s not capable of making nuanced judgements or decisions involving risk.

AI cannot replace creativity. Whether it’s about detecting financial fraudulence or designing proficient facial recognition, AI requires expert supervision to run effectively. Creativity in problem-solving is the core of Data Science and AI is still struggling to copy true creativity.


There are myths surrounding all domains and industries, and it is but natural that a little-understood one such as Data Science will provoke more myths than most. As always, do your research and look to trusted professionals in the field for answers.

Have thoughts to share, or questions to ask? Drop us a line; we'd love to hear from you!

Get Started - Future proof your career

Join 150,000 aspirants. Learn Today - Apply Today. Try Free Programs

Learn Data Science Free with GLabs