Why Data Science is still one of the best career choices you can make

With so many options out there, why should you consider a career in Data Science?

It's not new that data science and artificial intelligence have been the hottest careers over the world for a while. Mostly because of the powerful impact that this field brings to companies which consequently leads to high demand and well-paid salaries.

According to the World Economic Forum report for future jobs, by 2025, around 97 million new roles may emerge into the new division of labor: Humans, machines, and algorithms. The top three jobs in demand include data analysts and scientists, AI and machine learning specialists, and big data specialists.


Future of Jobs Report 2020, World Economic Forum.


Below we can see another reason why data science professionals won’t have trouble finding a job any time soon. The investments in big data and data analysis projects will nearly double by 2026 to a global spending budget of $92,2 billion per year.


Prediction of money spent on data science projects by Statista.


Thanks to the rapid development of digitalization, the amount of data created, captured, copied, and consumed in the world has been increasing extremely fast. Here, we have some information about how much data we produce and consume regularly:

  • 1.7MB of data is created every second by every person during 2020.
  • In the last two years alone, 90% of the world’s data has been created.
  • 463 exabytes of data will be generated each day by humans as of 2025.
  • By the end of 2020, 44 zettabytes will make up the entire digital universe.

*Data Source.

Based on how much data is already generated every day, the number will surely increase. Once again, it's evident the necessity for qualified people to interpret all these data and make good data-driven decisions.

While we keep creating data we will have data science jobs.


The data science field is also known for accepting people of all sorts, regardless of their background and domain. People from different industries such as finance, health, business, engineering, and physics can all couple their domain knowledge with expertise in data science and make a breakthrough work.

The reason for this diversity is that data science roles can differ substantially from company to company. Basically, data science is taking insights from data and using it to make strategic business decisions. However, that can be a wide variety of things in terms of day-to-day duties, requiring different skills and industry knowledge.


Top 10 industries in 2020


Careers in The Netherlands and EU

Doing a quick search we can find an average of 60 new data science-related positions each day in big cities around Europe. Besides, facing a pandemic, lockdowns, and social distancing has highlighted the efficiency of remote work. This can make the possibilities even better for someone in the tech field.

Salary Expectations

Although data science salaries will widely depend on the level of expertise and years of experience, in general, it's considered a well-paid field. The gap in demand and supply of data science professionals is benefiting even the less experienced workers that are just starting out.

In Europe, the average amount for a Data Scientist is about $65,000 according to Glassdoor information. In the graph below we can compare a few countries.


Mean salary + bonus (in euros) — From Data Science Salary Report 2020 Europe by Big Cloud


Expected Salary for Data Science positions in The Netherlands

As mentioned before, there is a great diversity in data science roles. Job titles can diverge from company to company and the best way to know exactly what to expect is by reading the job description.

Here, we can check the salary average for a few positions around the Netherlands.

> Data Scientist: € 65,501
> Machine Learning Engineer: € 69,000
> Data Engineer: € 51,111
> Data Analyst: € 48,174

*Data gathered from Glassdoor (October/2020).



What else to expect

As stated above, the actual meaning of a data scientist role may vary a lot. So if you intend to follow this career be prepared to get overwhelmed with many concepts and skills required to perform a good job. In general, this includes coding, statistics, machine learning, data visualization, and other techniques involved in data collection, preparation, analysis, and modeling.

Each company interprets what data science is in its own way. By that said, you may find roles involving complex machine learning and deep learning models, or others where you will spend most of the time working with SQL and data preparation.

Of course, you don't have to be an expert in everything, especially when starting in the field. That's why is important to understand the whole data process and the specific duties for each part of it. Make sure to understand the role you're looking for.

Should we worry about automation?

Many aspiring data scientists may be wondering if automation will interfere with their future employment plans.

Well, automation is a huge thing nowadays. It is clear that many predictable and repetitive tasks are being replaced by AI-powered machines. However, saying that data scientists will be replaced is not very realistic. It might be one day but not in the near future.

Automated machine learning systems can’t handle complex, creative problem-solving nor theoretical tasks that involve critical thinking and interpretation of results. These smart tools act as a complement to the data science professionals, allowing them to do more with less time. Besides, more data scientists will be needed to support automated tasks and to fill in higher-level positions.



If you are looking for an innovative career with growing demand and high paid salaries, then data science can be the one. But of course, with all the goodies, come the responsibilities and challenges.

Data science and AI are definitely getting more and more noticed for different businesses. In the coming years, we will have a huge increase in job openings for distinct expertise in the field. So, if you have an interest in working with data and using cutting-edge technologies and tools, it's worth going for it.


This article was written by Camila Bernardi (read more about Data Science topics on @cmbernardi).