31–40 of 122 posts
I'm from an engineering background, going in the direction of data scientist. Sometimes I find that my math skills could be stronger, and I try to read up on things when I encounter them, but still it sometimes feels like there is an infinite amount to learn. Maybe I could use some more systematic approach to it. Anyone else who has walked this path, and could come with some useful advice/resources?
I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. But not sure if an MBA would be a benefit. Thoughts from actual data scientists?
>. A big thing about the transition to tech is that you possibly start communicating with people who don’t really know what a vector space is. Be ready to have those conversations. Honestly ask yourself if you’re OK with having those conversations. This is so snobbish. What here is even think about ?
His audience is academics - who up until this point have been mostly in and around other mathematicians. Its important to ask yourself, "are you OK with having to sell/explain your work to people who aren't versed in even the basics of what you do?"
Obviously that isn't all data science jobs, but in my experience most of my colleagues have to evangelize their work within their organization to some level.
I had two blog posts exactly on that topic, math/phys -> data science: - why: http://p.migdal.pl/2015/12/14/sci-to-data-sci.html (on academia vs industry) - how: http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph... (also got reprinted at KDnuggets)
>. A big thing about the transition to tech is that you possibly start communicating with people who don’t really know what a vector space is. Be ready to have those conversations. Honestly ask yourself if you’re OK with having those conversations. This is so snobbish. What here is even think about ?
This is the wrong comparison. Read the second footnote. An intern is a developer in training. The post is talking about mathematicians (who spent a decade or more learning a highly technical field) going to work in an environment where, largely, their peers, boss and consumers of their work do not know, have no interest in and and will never learn the most basic concepts of their field.
The startups and Googles of this world are the exception when it comes to employer tech-savviness.
•Can you walk away from a problem when the solution is “good enough,” are you able to switch between tasks or problems with relative ease? Are you OK with simple solutions to problems that could have more complicated solutions, but only with rapidly diminishing returns? Translation: Can you be a good assembly line worker, devoid of pride or curiosity and just do the damned job? If so, great, we can use you! If not, p…
Well, I went from academia (theoretical physics) to data science, and "good enough" solutions is exactly the thing I like the most. That is - things that rather solve a concrete problem (given all constraints, including: dirty data, finite time, understanding the needs of clients) rather that have a pure and beautiful, but utterly useless for practical purposes, theorem to prove. But sure, tastes do vary.
What about the other way around? I'm from an engineering background, going in the direction of data scientist. Sometimes I find that my math skills could be stronger, and I try to read up on things when I encounter them, but still it sometimes feels like there is an infinite amount to learn. Maybe I could use some more systematic approach to it. Anyone else who has walked this path, and could come with some useful ad…
I can give recommendations on books for the former but not the latter.
I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. But not sure if an MBA would be a benefit. Thoughts from actual data scientists?
MBA courses for data science will bore her to tears. If she adds the R and Python and takes a couple graduate level math stats courses before she graduates, she'll know more than almost all the graduates from those programs
I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and other areas of math) is that almost everyone in it feels mediocre. It's like 10% of the people are 10x better than the other 90%, 1% are 10x better than the next 9%, .1% are 10x better than the next .9%, etc. The top two people in the field seem to be notably bett…