31–40 of 81 posts
I work for one of the major bootcamps and I can tell you that interest has significantly declined over the past 1 year. The market for data science has indeed shrunk. The reason? I'm not sure, could be that it didn't live up to the hype.
It wasn't 'just' hype, but it was over-hyped. There are companies that have their act together from a data standpoint and can make use of data scientists, whatever that term actually means in the context of their organization, but most can't. So the companies who spun up a data science initiative but had no business doing so are now likely saying things like 'what do you mean we don't have the necessary data?' and 'w…
I work for one of the major bootcamps and I can tell you that interest has significantly declined over the past 1 year. The market for data science has indeed shrunk. The reason? I'm not sure, could be that it didn't live up to the hype.
I think what companies really want is smart generalists with advanced math, programming, and modeling skills coupled with domain knowledge. That skill set will always carry high value in technical companies. The reason it carries value is the skills are difficult to acquire. I think the recent decline in interest reflects the rise of new data science candidates that are taking the path of least resistance to a career…
I am wary when people wax lyrical about all of the ways they love using machine learning on data. It makes me nervous because i worry that they have a hammer and can't wait to use it on anything vaguely nail shaped.
Earlier quoted context omitted.
It’s an epistemology / ontology question, as folks familiar with the humanities would spot in little time. Aka it’s not “data” until something empowers the created metric a meaning. The map is not the territory. https://www.amazon.com/Raw-Data-Oxymoron-Infrastructures/dp/...
I know what all those words mean, I've studied critical theory, and I have no idea what your point is.
Over the past several months I keep seeing people trying to equate data science with machine learning, and it made me wonder if the people doing this are trying to salvage (or perhaps enhance) the investment they made in data science by trying to blur the lines between the two.
My guess is that data science roles will merge with business analyst roles. Python and r will slowly join excel as tools of choice for making tables and charts to stick in powerpoint slides and pdf reports. Meanwhile the machine learning side of things will be the domain of _something_ engineers with candidates more likely to come from the computer science/math/engineer world rather than the sciences. (Other then tho…
Data Scientist is a buzz word for Statistician. Business Analyst is buzz word for Industrial Engineer. For example 10 years ago if you studied at my university you would witness that some Statistics students were doing second major mostly at Industrial Engineering and vice versa. They are already related for many years but average Joe has no idea.
"AI" (or whatever rebranding it gets) always works in cycles, with a phase of excitement and overpromising followed by a phase of apparent underdelivering and skepticism. But what actually happens is that the innovations just become part of the normal tooling, and stop being called "AI". At some point there is no need to hire a "data scientist", as any python programmer is already expected to know how to use numpy, p…
The value is a "Data Scientist" isn't that they know how to use a tool - it's that tell know why to use _that_ tool (technique) and not this other one.
Of course, if a company wants to truly innovate in the area it will need PhDs or people with great dedicated knowledge in ML/Statistics/Particular Domain, if it needs to scale it will need good data engineers to create the data pipeline together with DBAs and experts in each tools (like Spark/Flink), but for most companies the basic above is already a great improvement to what they had before.