I cannot say that data science was my life passion or something. Still, I was good at doing the job, and I felt satisfied if I managed to finish my tasks on a day. But the days when I could get any results were limited. I got frustrated and did not see any development.
I’ll explain to you why.
My impression from the data science job market is that it is changing too fast. Too fast for a person to become a good specialist. I started with Tableau. That was trendy then. Everybody wanted it. I managed to learn the basics when everybody suddenly wanted to switch to Microsoft Power BI! Only half a year after I saw Tableau for the first time, and it already turned outdated.
So I learned Power BI, and it took another five months. At the time, I did not recognize any advantage of doing data analysis with Power BI instead of Excel, except for beautiful colors. I mean, it is a good visualization tool, and it is cheaper than Tableau, but as a data scientist, you get bored.
I studied statistics at an advanced level at the university, and my idea of data science was exactly this: doing analysis, not just drawing nice charts. I started with business analyst jobs, with doing data visualizations, hoping to grow into a data scientist or data engineer. But it became clear for me soon that at least in Germany, the demand for data scientists is not especially high. I happened to stumble over a few job announcements, but they posed another problem. A candidate had to have strong programming skills. Moreover, I remember precisely that a couple of years ago, that meant programming languages like R and Python, but meanwhile, companies rather look for a cloud and data science specialist.
In my Tableau job, I started to learn R, and in the next job, I could already apply my skills. But since Power BI stayed my main working tool, I started to look for a new job. I was sure that only a few months ago, all job posts required R. Now it was Python. Nobody needed R anymore.
I got a job in a company that used some other technology, neither R nor Python, and learned Python by myself, applying it to my tasks from time to time, till I could switch completely from R to Python to support my analysis. That third technology I was confronted with was on the hype then. It was a mixture of a programming language and a visualization tool.
At that time, I encountered another tendency in the data science job market. Certificates and certifications were booming. My employer insisted on me doing at least two certificates. I did one, and it helped me to do my job better, really. But the preparation time for the exam exploded to an enormous size, and during several weeks before Day D, I could only think about the exam questions. I felt like being back to school.
Many jobs are changing fast. I did search engine optimization, and each new big Google update just made us start from scratch. Still, I had many routines that were the same for years, and I could do some SEO even now. With data science, I think I would not be able to find a job after a few months’ break.
It is not only the stability in the job market that is important. To get better in your job, you need to train, to repeat the same things over a long period of time. If the tasks or the technologies are changing too fast, you quit learning. You are only running and trying to catch up.
The last technology I work with released many updates. So does Python and R, indeed. But in R and Python, you have the logic that has not been changed for decades. If you miss a package, you can still go on programming. The visualization tools sometimes just stop working after some new updates, for instance, because the file format had been changed.
I enjoyed programming in Python, but this time it was the job market that changed. Suddenly, none in Germany wanted a data engineer anymore. I tried to write a mobile application with Python that would do some data science and be interactive, but Python has not enough packages that could be integrated into such an app to make it useful. This is another story.
I felt that I needed some routine and a job that would remain stable in the next few years. I needed to know that if I do something today, I invest in my future.