Graduate student in statistics at Duke University. Former dev.to employee. I like to blog about data science on my Medium publication, perplex.city, and on dev.to
I think I disagree with this sentiment as a blanket statement, but might agree with something a little more contextualized. Surely you don't need a PhD to be a practicing data scientist, but there's definitely some level of mathematical rigor that you should be comfortable with. Do you have any examples of math or algorithms that you would think of as unnecessary to understand fully to use their applications?
I'm a very straightforward data science student in last year before master graduation. I like to learn bottom-up step by step and go through the logical discovery process myself as much as possible
I think I disagree with this sentiment as a blanket statement, but might agree with something a little more contextualized. Surely you don't need a PhD to be a practicing data scientist, but there's definitely some level of mathematical rigor that you should be comfortable with. Do you have any examples of math or algorithms that you would think of as unnecessary to understand fully to use their applications?
When you do coordinate descent with dual objective and you have to calculate the gradient of a specific matricial form function?