Top 10 Myths about Data Science Career

Top 10 Data Science Career Myth

Data Science, Machine Learning and Artificial Intelligence are gaining importance for their respective use. But many have their doubts before taking up these roles as their career. Data Science Career is one of the most successful careers to work in now. The myth-busters are as follows.

Myth 1: Data Science, Machine Learning or Artificial Intelligence

The name itself surpasses the meaning that the Job Designations carry. Data Science, Machine Learning, Artificial Intelligence or Deep Learning all these fields are in relation to one another. When all of these are together they merge to have power, thus adding too many responsibilities. Let’s find out how:

  1. Data Science:

    • Applies Machine Learning to generate actual products.
    • Attends to Real-Life Complexities.
  2. Artificial Intelligence:

    • Sounds Catchy.
    • Fetches more money.
    • It seems like the future.
  3. Machine Learning:

    • More of an academic discipline.
    • No relation to real-world tools/models.
  4. Deep Learning:

    • A subset of Machine Learning.
    • Deals with Artificial Neural Networks.

Data science is a vast paradigm and often overlaps amongst Deep Learning, Machine Learning, and Artificial Intelligence. But, one must not confuse themselves and treat it as a separate entity.

Data Science facilitates processes and tools to extract and interpret huge amounts of data faster and efficiently. ML is often the link between AI and data science since it is analyzing and deriving meaningful information from data over a period of time. In a precise way, we can say that ML is a subset of AI that results best when used with data science.

Myth 2: Learning the Fanciest First!

Adding to the above points. As a beginner, new terms seem very aspiring and thought-provoking which in turn results in learning all the stuff in one go.

But, that’s where it all goes wrong!

Like any other core, discipline, even Data Science, AI, and ML must be taken seriously and dealt with the utmost attention.

If you really want to dive in the future, you must introspect which way to go.

Lots of resources are available on the internet to help you get started and form a structured plan.

Myth 3: Artificial Intelligence will Obsolete Data Scientists?

Well, the very myth strikes a thought in mind, whether it is a question or a statement.

Once, you understand the basic concepts on which these technologies thrive upon, you will get a brief idea that even with the most advanced algorithms, there will still demand skilled professionals to steer them forward.

The focus is on creating more accurate and self-generating algorithms and systems, but even the most sophisticated ones will need human interference and judgment.

Myth 4: Artificial Intelligence jobs will consume the market

Even if we consider AI to sweep off all the current technologies, the fact that a project team is a collaboration of various skilled professionals can’t be ignored.

Roughly, any AI project constitutes these members and to just understand AI will not help. Here are some skilled experts (in no particular hierarchy) that make a successful AI team:

data science career

As is evident from the above diagram, to generate an efficient process or product, you need a team and just an AI expert won’t deliver the desired results.

Read More: How is Ivy Professional School Motivated to Add Value to Your Data Journey

Myth 5: General AI (once developed) will be autogenic and no longer require the human expertise

Most of it is what’s explained in the above point except for the fact that a general AI will have both the power and means to self-learn and implement processes. But, firstly, we are way too far from reaching that point yet.

Secondly, even if that happens, a general AI would still require some human to counter bias or fraud.

artificial intelligence

We must never forget, the origin of these algorithms is subjected to human bias and thus needs a constant review. Thus, we can never negate the role of human expertise.

Myth 6: Data Scientist/ Engineer/Analyst are no different from each other

This is one of the most asked queries. People keep confusing these skills.

But each role has its own uniqueness, as a professional when they are working on a project/product development.

A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for a better understanding of how the business performs, and to build AI tools that automate certain processes within the company.

data engineer data scientist data analyst

Myth 7: Scientist and Data Can’t be a match

A scientist is someone who gathers data, studies it and generates meaningful information from those data.

Now, this study could be scientific, technical or simply to help your business. Therefore, a data scientist mustn’t be judged by their domains but by the nature of their work.

Myth 8: You need to be a Mathematic Wizard

It is one of the pre-requisites but not the only one. When companies lookout for data scientists they definitely want sound analytical and logical reasoning.

A data scientist deals in a lot of skills varying from domain knowledge, programming to statistical formulae, thus, an upper hand in mathematics could come handy but will not make you a lesser contender for a job if you don’t have a mathematics degree.

Read More: In conversation with Yash Chaudhary- Founder of Zorba Consulting India

Myth 9: You need a doctorate or specialization

Data science has been around for quite some time now but still lacks skilled professionals. One reason is that everybody thinks only a Ph.D. deserves this job.


While a doctorate might give you some extra points but you must also know the trade. Just some fancy degree will not help you do your job.

What one needs is a thorough grasp of concepts and tool expertise to make it count.

If you are looking to get certified in having a Data Science Career, then Ivy Professional School provides many kinds of courses on Data Science, Data Analytics, Machine Learning, and Artificial Intelligence.

Myth 10: You need a regular data science degree to have a data science career

This is where a lot of experts disagree.

Some think that a data science degree will give you a cutting edge knowledge regarding the domain.

This again is quite dependent on demand. Some companies prefer professionals with data science degrees while others take a holistic approach and look for versatility.

While a data science degree is a quicker way to get under the subject but a majority of people transitioning to data science still rely on self-learning.

Thus among all the other myths that go around the mind of people before taking up Data Science as a Career. These myth-busters are a necessity.

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