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Good news is, it's a time of change and goldrush, and salaries have significantly increased since 8 years ago. Bad news is, what you have learned seems to repr
by james-revisoai 3y ago
Good news is, it's a time of change and goldrush, and salaries have significantly increased since 8 years ago.
Bad news is, what you have learned seems to represent what is now in declining state, the technology that was more useful before ChatGPT (this may be subjective). Python, sci-kit learn, numpy have been made more and more obsolete by ChatGPT itself taking over many custom model roles (that sci-kit learn, pytorch or spacy would have helped you do), and chatgpt can simply code stats tasks/dashboards so rapidly there is much less employment focused on this than a few years ago. Lots of data engineering is useful, but for analytics rather than predictive (machine learning tasks), since the predictive stuff, you'll probably find you don't need things like feature detection, normalisation etc for depending on how willing you are to try the cutting edge tools coming out.
Why don't you considering combining the two at the twine to your advantage - Have a look at ChatGPT and using add ons or other recent technology (e.g. Code Interpreter) - you could be in the prime and ideal position to take advantage of this shift, without preconceptions about exactly what should be the "next generation app".
- bilsbie 3y agoYou’re completely right but I can’t imagine most employers are going to be interested in chatgpt abilities. I’m not sure what technologies employers are interested in for data science right now.
- g_murad 3y agoThank you James. I appreciate your suggestions. - For the good news: Yes that seems like it bu only if I can get a job :) - For the bad news: I did already start to look at ChatGPT and utilizing it in my projects. That was exactly my thought when I started using it, thinking all that technology it is going to make obsolete.