I think your diagram is for switiching to a data scientist role. In data scientist job ads I often see companies that want a phd, advanced stats skills, and de

I think your diagram is for switiching to a data scientist role. In data scientist job ads I often see companies that want a phd, advanced stats skills, and depending on the role AI related skills. They want to see track record in these, but will happily take a fresh PHD student who did a project that involved these. They don't want a software engineer who did some code camp courses, they want an accademic. Conversely, data engineering, I see ads wanting a cross over of big data ETL technologies and devops - i.e. pyspark, kubernetes, and depending on the role experience of scaling and productionising AI, without actually needing a deep knowledge of AI algorithms. This could be more viable for a software engineer who did some online courses, as they specify tools experience not academic background. However, it would be difficult for an experienced software engineer to switch into an experienced data engineer role, because it is expensive to set up data infrastructure at scale, so you can't switch over with a hobby project in the same way you could e.g. switch from experienced front end to experienced full stack by showing a significant webapp. Actually might be affordable in the silicon valley bubble I guess.