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sad for me as a data scientist
ОтветитьAgree max! I think Data Science is WAYYY too overhyped whereas their business value is really isnt that much when compared to other roles. Data Scientists are usually caught in this weird grey area between Data Engineers and Data Analysts Its also a v vague title that could mean n number of things, dependant on the org anyway.
I'm personally more inclined towards business intelligence rn as im starting my career cause most stakeholders really do not care what kind advanced analysis u run on their data, they just care about how accurate the insight is and what it is (at least in my experience)
At the end of the day, these roles are mostly support roles that help a certain fucntion of a company i.e sales, marketing or product etc
As someone who has worked both data analyst and data scientist positions at small to large companies, I 100% agree! 90% of the problems are better and more quickly solved with analytics and data pipelines than with ML. It’s sad for the people who learn a lot of fancy algorithms and never get to use them.
ОтветитьVery Insightful and well said 🔥 Glad I got your video ✨
ОтветитьThe one thing I feel an overriding sense of with the data field in general is that it's increasingly hard to stay "on top" of it and still have a balanced, normal life. This is contrary motion, because AI has made a lot of the nuts and bolts of our job easier...but in my job we're just using that to accelerate our ML designs, which demands so much more study than hacking together Python and SQL ever did.
Maybe it's just me, but I find I have to spend so much time learning how all these new methods work and dig into the theory (EXTREMELY so in ML modelling, where you seriously should know the maths behind what you're running) I don't know if I can realistically pursue it anymore.
The gap between true programmer "lifers" and those who just want to do it in their 9-5 is really starting to show imo. Granted, a few of them are also simply gifted also!
My experience is that Data Science, ML, and Big Data are typically pretty useless for 95% of use cases. Unless you want to do dynamic pricing or have huge volumes of B2C data that need to be analyzed its objectively not very useful in improving business solutions.
ОтветитьThe need to trains humans on the AI model is a great way to put it
ОтветитьI couldn’t agree more !!!
Ответитьhey, i am an engineer and doing de & bi masters, now i have an analyst role and strong desire for de role, you know there are lots of challenges and things to learn that path, rather 20 years of pure sql and dashboarding this kind of mix up is makes sense or not? what would you do
ОтветитьFirst 88 seconds, solve all the major problems for most of the companies.
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