I started working as a software engineer, I wrote code in Python long ago, still from the university. True, frankly, initially with mathematics, I did not have much in school. And when Data Science started to form as a separate direction, I realized that this is one of those areas where people really make computers do what they are intended for. That is not to shift the bait from one place to another or not to draw beautiful interfaces (I somehow worked in a web studio, useful and interesting experience, but there is not at all of any projects there is any benefit).
In this sphere, there really is a global universal meaning, but the trick is that it requires a good knowledge of mathematics. This was one of the important reasons for me not to go directly to the data scientist, since I would have had to change the course of development. For data analysts that Python, that Spark - it's just tools, and the basis - a mathematical device, the selection of parameters, the adjustment of models. I too liked programming to dive headlong into another pool.
Then I suddenly realized that, in fact, people who are good at solving mathematical problems have another weakness - they often do not have the experience of industrial coding, building up some finished product. And I cheekily concluded that I have something in this is an advantage.
So I came to Data Engineering. Because, firstly, it allows me to do what I love, but in a more interesting sphere for me. And secondly, it is a demanded area. It's cool when you get paid for something that you genuinely enjoy doing, right?
The longer I do this, the clearer I see that Data Science is a very diverse sphere, in which everyone can find their niche. When I started teaching, my goal was to convey this idea to the students: it's extremely rare for people who know everything and solve mathematics and write code, for any problem it is worth looking more broadly. In this area there are so many tasks, it's not only models, but also architecture building, deployment, setup of a complete workflow chain from a prototype to some finished product. All these steps are important, and each requires an appropriate specialist.