I worked in 3 DS roles over ~5 years, and recently made the "official" jump to SWE. I've also interviewed dozens of candidates for several openings during that time.
This post rings extremely true to my experience, and largely aligns with what I've been telling people for the last couple of years. I see so many bootcamp or Masters grads with a wildly skewed understanding of what the job entails. I also see a lot of MBA types diluting the meaning of the DS term as a whole.
A "data science" curriculum as such will basically prepare you only for an analyst role. You're not going to be able to compete with the glut of science PhDs flooding every open role, either. DS may be your title but you will not be doing any of the exciting things you want to be doing. To differentiate yourself you need to specialize, and good engineering skills are a prime way to do that.
MBA-type here, the way I have seen data science described in large organizations is much more like an analyst with a more robust set of tools than excel for deriving information/wisdom from data. Using the tools that other "data scientists" develop to solve business problems.
That's almost certainly diluting the term but it's much closer to the work I do than the title might imply. Since 90ct of business problems can be solved with regression, typically logistic or decision trees, knowing what tools are appropriate to apply to a certain problem is more valuable than being able to actually write those tools. Bootcamps don't spend enough on the why of what we do because I think it's just something you pick up through experience.
I think the data science role was always vague. Depending on the company it could mean Analyst, Data Engineer, Machine Learning Engineer, Machine Learning Researcher or a combination thereof.
This post rings extremely true to my experience, and largely aligns with what I've been telling people for the last couple of years. I see so many bootcamp or Masters grads with a wildly skewed understanding of what the job entails. I also see a lot of MBA types diluting the meaning of the DS term as a whole.
A "data science" curriculum as such will basically prepare you only for an analyst role. You're not going to be able to compete with the glut of science PhDs flooding every open role, either. DS may be your title but you will not be doing any of the exciting things you want to be doing. To differentiate yourself you need to specialize, and good engineering skills are a prime way to do that.