Source | towardsdatascience.com | Terence Shin
I just want to say that whether you choose data science or data engineering should ultimately depend on your interests and where your passion lies. However, if you’re sitting on the fence, unsure of which to choose because they are of equal interest, then keep reading!
Data science has been a hot topic for a while, but a new king of the jungle has arrived — data engineers. In this article, I’m going to share with you several reasons why you might want to consider pursuing data engineering over data science.
Note that this IS an opinionated article and take what you want from this. That being said, I hope you enjoy!
1. Data engineering is fundamentally more important than data science.
We’ve all heard the saying “garbage in, garbage out”, but only now are companies starting to truly understand the meaning of this. Machine learning and deep learning can be powerful but only in very special circumstances. Aside from the fact that there needs to be a substantial amount of data and a practical use for ML and DL, companies need to satisfy the data hierarchy of needs from the bottom up.
The same way that we have physical needs (i.e. food and water) before social needs (i.e. the need for relationships), companies need to satisfy several requirements which generally fall under the data engineering umbrella. Notice how data science, specifically machine learning and deep learning, are the very last things that matter.
Simply put, there can be no data science without data engineering. Data engineering is the foundation for a successful data-driven company.