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Why Should You Learn Data Science Now?

Why-Should-You-Learn-Data-Science-Now
If you want a one-line answer, then the answer would be you are going to be obsolete in the job market in the next few years, if not get upgraded with data science skills.’

Yes, learning Data science courses are essentially a component of progressing with technology. Data science has been a key aspect of the field of Computer-Aided learning, and data science courses appear to be a wonderful fit for someone who is an enthusiast looking for a career boost/ security. Let’s peep into the details reasons. 

Career Security And Continuous Growth Assurance

Sample Question:

  • I am not happy with my career growth. If I move into data science, is this assured that growth will not become stagnant again within the next 2 or three years?
  • How Secure Is Getting Into A Data Science Career?

The present job market has become an ever-changing entity—irrespective of the industries, whether IT or non-IT, sometimes there remains very high demand (that results in even 200% hikes). Within the next few months, the same experience, the resource overflow (that results in employee terminations).

But data science is such a field without which every single domain becomes lame. Starting from marketing to the supply chain, from health care to the media and entertainment, and from e-commerce to energy: every industry needs eligible data science and analytics professionals.

And above all, it’s a growing field. There will be countless job opportunities in the field of data science. As per the research data of AIM, by June 2021, the Indian job market has shown 47.1% job growth compared to the last quarter of 2022. And this growth was entirely backed up by the data science job openings. Even Michael’s page report India- a recruitment firm, has revealed that in the next 4 years, there will be around 11.6 million new job openings in data science, AI and ML. So, from the perspective of job security and career growth, no doubt the data science field is the most promising one.

Moreover, as I said, it’s in a growing phase; hence you have lots of scopes to learn that also ensure your career growth. Adapting the continuous learning strategy for data science is the key to a secure career.

Salary Hike:

Sample Question:

  • How much salary hike can I expect?
  • Is the salary hike reliable? Or is it just a short-term achievement?
  • I have been in a non-technical domain for the last x years, but my salary package is quite low. Can I expect a lucrative package while moving into the data science field?

It’s very common that when variable job options are there, then you can negotiate better. In fact, when you already have some working experience or are even an expert in a particular field (e.g., 7+ years of work experience)- you can expect an unbelievable salary hike while switching to the data science field. Until the first quarter of 2022, Our placement reports revealed that with an average of 3 years of experience, professionals are getting an average of 97% salary hike. For more experienced people the highest mark reached up to 450%.

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Here, you can say that the higher the salary hike will be, the greater the risk of losing a job during the depression of workforce demand. But here, I can assure you that this is not going to happen in the field of data science until you stop upgrading your skill. Also, there is a lack of experienced data scientists who own demanding business acumen for a particular industry. Above all, all industries are getting highly dependent on data. Hence for the next 10 years, data science is going to be the only booming career.

Data Is Everywhere, And So Is Data Science.

Sample Question:

  • I am from a non-technical domain, will switching to data science be worth it for me?

In the current scenario and according to future insights, data science is not an option; rather, it’s an inseparable measure of every business.

For insane, you are into marketing. You need to do target analysis, customer satisfaction direction predictions, legalization of demands, customer relationship management, etc. for each of such, you need to analyze gigantic amounts of data. And it’s not only the historic data. You need to analyze real-time data too. So, it’s quite obvious that traditional advanced excel technology is not going to work anymore. In fact, the volume of data is becoming so big day by day- the traditional computer system is also reaching its limitations. So dedicated systems, as well as a dedicated and expert analytical team, are required. But to perform as per the expectation of business, every single marketing professional needs at least a basic analytical knowledge.

So you can now understand the significance of data science in marketing now. And it’s quite clear that if you specialize in the same, how demanding you will be in the present competitive job market.

The scenario is the same for all industries. In fact, the demand for data scientists with expertise in particular domains like BFSI, Marketing, Energy, sales, etc., is more on-demand than the non-data science IT pros.

The hard truth is that even for IT professionals now, data science, mainly machine learning and AI, is becoming a mandatory skill for a growing career. So, it doesn’t matter from which domain you come; data science skills are required everywhere.

Why-Should-You-Learn-Data-Science

Switch Towards A Newer Field But No Need To Start Afresh Domain-Specialized Analytical Knowledge:

Sample Question:

  • Why should I learn domain-specialized data science?
  • Or should  I have to start as a fresher while switching to a data science career?
  • While doing a career transition, the biggest concern of every candidate is ‘do I have to start fresh?’ or ‘will my designation go down?’
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Data science is such a field where you need not start fresh. In fact, the more your experience, the greater will be your opportunity to approach the senior role in the data science field.

As already mentioned, without analytical knowledge, you have no worth in the job field.- This is going to be the scenario for every single field, as already mentioned. But learning data science skills without knowing the proper application of the same in a particular domain is not worth it in the job market. In fact, in the next 5 years, data science skills without domain knowledge will completely become obsolete.

For example, you have worked for a fintech company for the last 5 years. Now switching to the data science field. While doing analytics or planning a predictive analytics project, without having the ultimate knowledge of fintech business/ market/ best practices, is it possible to offer the most profitable and full-proof solution to a business problem?

The same thing is the concern of every business. Hence, the random recruitment of data science professionals is slowing down.

Sample Question:

  • What if I don’t have any domain experience?

Now, what if you don’t have any domain experience. Well, this is the most common scenario for freshers. It’s true that for working professionals, the scopes are much greater. But entry-level data scientists are still in demand; only you have to earn a specific domain knowledge via promising projects and internships.

The Computing Process Is Shifting Towards A High-Performance End

Sample Question:

  • I am in IT/ CS. I am aware of technological advancement. Still not satisfied with my career growth. But why should I opt for a data science career switch (or how a data science career switch can save my future?)

Being in IT/CS, you might present yourself as quite upgraded. But do you know that we have already moved a lot towards high-performance computing? Also, gradually approaching the world of quantum computing? Your traditional computing knowledge is going to be obsolete very soon. The new approaches to both high-performance and quantum computing will be solely dependent on live data. And when it comes to the matter of live data, any advancement is not at all possible without data science. So, here also, data science is going to boss the CS/ IT field.

Continuously Increasing Gap Between Demands And Supply (In Data)

Sample Question:

  • What is the assurance that after a few years, my demand will not drop? (why should I choose a data science career transition, not the other technology like cloud computing or anything else? )

In 2020, there were 2.5 quintillion data bytes generated per day. It’s 2022 now. You can expect at least a 250% hike of the same. So, the rate of daily basis data generation is increasing exponentially. But the number of adept professionals to convert this data into meaningful insight is lagging a lot. We can alternatively say that supply of data converters is not able to compete with the spreading demand.

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It’s true that 8 out of 10 people are pursuing data science courses, but there is a huge lack of properly trained and job-ready data scientists. And targeting such lag is the best way to be a demanding profession, for which MNCs and even SMEs are ready to pay any figure.

No Limitations On Job Roles:

As mentioned several times, every industry is dependent solely on data, and so are the job roles.

There are variable job roles in the data science field (although the majority of aspirants focus more on ‘data scientists’). A few examples are,

  • Data analyst
  • Data scientist
  • Data architect
  • Marketing data scientist
  • Financial analyst
  • BI developer
  • Machine learning engineer
  • Data engineer

So, depending on your interest and working experience, you can target your first data science role. The scope of further upgrading is also endless.

Hence, in one line: you should learn data science because:

  • It connects the past
  • Perform in the present
  • Make way for the future

In the next five years, every single designation is going to be filled by data science professionals only. And yes, without data science knowledge, there will be no scopes of lucrative or even the standard salary.

Now Or Never:

The indication of the data science field’s ever-growing development might make you a bit lazy to learn data science now. But as you approach the upcoming years, the scientific data needed is going to be more complex.

Conclusion:

Data Science is the next big thing in technology; pursuing a profession in data science will most certainly lead you to a world controlled by technology, where you will be able to fully realize your potential and get the greatest results. To get started with Data Science, You’ll need to enrol in Data Science courses with outstanding supervision and mentorship. The best way is to opt for such a course that offers live and interactive learning and, yes provides the guarantee of placement with an ample salary hike.

As a result, we can simply conclude that understanding data science will help you find your dream job and will undoubtedly improve your career prospects and job satisfaction.

Phurba SherpaAbout the Author:

I’m Phurba Sherpa, a passionate blogger who loves writing about the latest technologies. I also write educational and technical content regarding data science courses, Artificial Intelligence (AI) and ML.  I’ve always believed in smart learning processes that help readers to understand concepts, and writing is one of the ways. I always prefer articles that will encourage tech enthusiasts in growing their careers.

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