As Artificial Intelligence and Data Science continue to grow and take an even more central role in our lives, many people are considering getting into the Big Data industry.
Unfortunately, some of these professionals, e.g. those in electrical engineering, have little experience in statistics and data analytics, which makes an outright switch impossible.
Below, we’ll compare the two careers and see how easy it is to switch between them.
Can An Electrical Engineer Be A Data Scientist?
Electrical engineers can easily get into data science within a few months if they so wish.
First off, most data science jobs don’t require a specialized degree and you only need to have the relevant analytical skills. Secondly, the field involves a lot of programming, which many electrical engineers are adept at, so switching isn’t so hard.
To make a seamless transition to data science, electrical engineers need to catch up on some concepts they missed in their undergraduate studies.
Fortunately, every resource you need to master data science is available online. You can buy detailed courses on individual subjects on Coursera, Udacity, and Stanford or even use the numerous free tutorials on YouTube.
Simply put, whether you can become a data scientist has more to do with your commitment and willingness to learn than any actual barriers of entry.
Which Is Better: Data Science Or Engineering?
Data science and engineering are both rewarding fields of study. However, some people prefer one over the other.
Supporters of engineering point at all the inventions made by engineers as a testament to how crucial the field of engineering is to human civilization.
On the other hand, those who like data science, point at how data is becoming increasingly crucial to decision-making processes in the political, commercial, and social spheres.
It’s therefore difficult to objectively tell which is better.
Below is a side-by-side comparison of the two disciplines.
|Types of Tasks Involved in
|Designing buildings and physical structures
Drafting regulatory documents on safety standards
Supervising large construction and manufacturing processes
|Data processing and analysis
Extracting actionable insights from complex data
Developing machine learning algorithms for data collection and analysis
|Ease of Completing Major
|Business Intelligence Analyst
Machine Learning Engineer
|Ease of Finding Employment
As you can see, the two careers match up very well in most regards. We can therefore conclude that none is better than the other.
The fact that you can easily switch between engineering and data science jobs further shows that the two disciplines complement each other.
Do Data Scientists Earn More Than Engineers?
Data scientists are generally paid more than engineers. According to the Bureau of Labor Services (BLS), the average wage for data scientists as of May 2020 was $103,930 while that of engineers was $83,160.
However, the picture isn’t as black and white as it seems.
For one, there are many more engineering jobs available, which means engineers have better chances of landing jobs. The future won’t be much different either, as the BLS projects 140,000 new engineering jobs will be created in the decade ending 2026.
Only about 50,400 data science jobs are set to be created in the same timeframe. So essentially, many data scientists are struggling to find well-paying jobs despite the much-vaunted lucrativeness of the career.
Secondly, many of the well-paying data science jobs are in senior positions – mostly in major companies – while engineers in intermediate job levels earn very good wages.
Most importantly, it’s easier for an engineer to transition to data science, and thus increase their earnings, than it is the other way around.
Why Is Data Engineering Better Than Data Science?
Although data science is the more appreciable discipline, it can’t exist without data engineering, which essentially makes the latter more important.
Below are reasons why we recommend data engineering over data science:
1. Data Engineering is the Mother of Data Science
If you have a passion for Big Data, data engineering is the first thing you should master. This is because data engineers build all the models and methods of data analysis used by data scientists.
For instance, data scientists mostly deal with data visualizations and statistical methods for analysis. Data engineers do everything else in the data pipeline, from retrieval to storage to multi-platform integration.
2. Data Scientists Can’t Do Without Data Engineers
Most data science courses don’t go deep into the fundamentals of data processing and management.
As a consequence, many data scientists can’t manage the entire data pipeline on their own. They have to constantly call data engineers to clean or retrieve data, fix ETL pipelines, and so on.
3. Data Engineers are Much Happier
Data science pays more and is by far the more glamorous job but data engineers have better job satisfaction levels.
This is because they work fewer hours (mostly as support staff) and spend their time solving technical problems which are not as mentally taxing as the analytical problems data scientists face.
Moreover, data engineers have a wider array of jobs to choose from compared to data scientists.
Is Data Engineering Boring?
Data engineering is too robust and challenging to be boring even for engineers who have been working for decades.
As previously mentioned, they’re involved in building and maintaining data pipelines, and cleaning and organizing data and databases.
Since different companies deal with different databases, engineers have to keep learning new ways to solve problems.
As such, monotony is the last thing one would experience when working in data engineering.
Although the three fields are very different from each other, electrical engineers, data scientists, and data engineers are all paid to solve technical and technological problems.
Indeed, it’s possible to master the three fields at the same time. For instance, you can be a software engineer and a data scientist while getting the best of both worlds.
That’s exactly what we would recommend if you’re interested in working in Big Data.