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Pursue A Career in Data Science: Why Is Now the Perfect Time?



When thinking about data science and its definition, everything seems so blurry and undefined, especially if you never met and conversed with anyone who does data science for a living to simplify it to you.  

But suppose you’re thinking about pursuing a career in data science. In that case, you can do so with regular studying, researching, and with a data science certification that will prove your knowledge in data science.

But first, let’s clarify this blurry thing called data science.

What is Data Science?

In the past two decades, data science became more and more popular among all the different types of businesses. With the amount of available data today, companies can dig into any information they need so they can find a way to be better than their competitors.

Let’s make it simple, think about gold mining:

You rent a bulldozer or two, go to a vast muddy field or river, go thoroughly around it with a metal detector, and when the detector starts beeping, you start digging. When you dig up a hole, and you find a piece of gold, you make something that’s golden and shiny.

To be frank, it doesn’t go as easy as we said it, but you will get the point.

With data science, you actually do that mining, with certain data, only to find small but significant details that can change or improve the things that the company is working on.

Now let’s talk about the types of data scientists.

Types of data science specialists

There are many data science fields where you can pursue your career, but we’ll say something about a few that are most common in today’s world.

Actuarial data scientist- If you have a Ph.D. in Actuarial Science or work in an insurance company, you might start thinking about learning about data science too, and expand your knowledge for further job opportunities.

 As actuaries’ domain covers insurance risks and data, and data science covers big data and data mining, combining those two and narrowing your specialties might be a perfect fit.

Machine learning data scientist- As the technology of computer and machine systems is growing and developing rapidly, merging machine learning and data science can help to improve the outcome of machine decision-making capabilities and artificial intelligence.  

Statistics data scientist- This is the type of data science in its specific context. Calculating, number crunching, dividing, data analyzing, and visualizing data are only a few skills you need to work in statistics as a data scientist.

Combining these two is perfect for problem-solving as it gives a definite conclusion about the data you are working on.  

Healthcare data scientist- Having an interest in healthcare, epidemiology, math, and data science can significantly impact healthcare. When all of these qualities and interests are combined, it can bring a significant gap in understanding genetic issues and drug studies and research.

Mathematics data scientist- mathematics is getting more and more appreciated in the world of data science, making it one of the essential skills, research, and studies for data science.

It is practiced in any field of data science for it uses pricing algorithms, inventory management, and defect control as a part of its specialty.  

We’ve named and tried to describe only a few fields in data science, but as we said at the top, data science is becoming more and more popular in any area you can imagine. So why not try it?  

Is now the perfect time?

Data science is rapidly growing in any field possible that you can imagine. By that, we mean that there might be a possibility that data science will be used to understand better and dig deeper to grow businesses that didn’t require data science before.   

So yes, now is the perfect time to start growing your knowledge and pursue a career in data science.

How to educate yourself for a career in data science?

There are many skills that a data scientist needs to know apart from the basic principles of data science. For a start, we recommend you learn and understand the basics of data science first, like data visualization, or learn more about how to do the scripting in Python so that it can help you better understand its concept.

You can do that on your own, with research and YouTube, but the best thing to do is start a course and learn from educated professors.


Working and increasing your education is always a good thing to have and do, so don’t hesitate to explore more, as it will bring you a feeling of satisfaction and a bigger salary. It might be a struggling thing to climb that hill at the beginning, but don’t give up as data science is growing, and it will pay off at the end.

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