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  #41  
Old 01-13-2018, 08:00 PM
danielpst danielpst is offline
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From my perspective (I'm not a data scientist, but have been considering the route), I thought I knew everything that I needed to know to apply stats in the real world once I had graduated with a B.S. stats. My perspective quickly changed once I finished the core classes of my master's degree. And things like Elements of Statistical Learning - if you intend on reading such a text and understanding what it is doing - depend on a graduate-level background.

It's easy to think that since we have the internet now that you should be able to find topic X and learn about it. But, even for rote exercises in grad-level stats, I've been finding that not to be the case.
Hmm that's a good point. I'm probably still in the "don't know what I don't know" camp.
I worked through Elements of Statistical Learning and Machine Learning in Action, but they are just two books out of a bunch of materials a grad student has to go through. I'll continue to learn on my own, but I'll probably go back to grad school at some point if I'm no longer satisfied with what I'm learning. Hopefully I'll have a better sense of what's really required to be a great data scientist through industry experience by then.
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  #42  
Old 01-13-2018, 08:22 PM
Locrian Locrian is offline
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One thing I'll say is that if you decide to build a strong statistical and machine learning background through things like grad classes and Elements etc., you might be well chasing the tail of those subjects' usefulness. I'm not sure I see the forecast/modeling type data scientist's future as being nearly as bright as others do.

Honestly, this forum's obsession with data science seems even less well-advised than its recent obsession with wall street/quant work, if such a thing is possible.
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  #43  
Old 01-13-2018, 09:25 PM
clarinetist clarinetist is offline
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One thing I'll say is that if you decide to build a strong statistical and machine learning background through things like grad classes and Elements etc., you might be well chasing the tail of those subjects' usefulness. I'm not sure I see the forecast/modeling type data scientist's future as being nearly as bright as others do.
And I agree with this.

But: you do need that background to even start developing a surface-level understanding.

E.g.: unlike some people, I'm not comfortable running lm() in R and expecting that the output is clean and ready, particularly when I'm working with categorical or collinear data. The only reason I know to look for these things is because I have the background to understand these issues.
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  #44  
Old 01-13-2018, 11:02 PM
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It is almost solely because PhDs have been primed to learn things they don't already know and have demonstrated a passion for learning. There are very few people who can justify a PhD solely from a financial perspective (hint: they value other things as well). There are a slew of people who just popped out of a BS program that would be content with coasting in whatever job they get and that is what you're dealing with. Not your own ability, but the aggregate perception of people in your same bucket.



What separates them isn't the technical skill, but a credential, many years of domain expertise and a willingness to do mundane work. This fallacy about one type of quantitative person being able to solve business problems and another type of quantitative person not being able to solve business problems is annoying. Neither an actuarial credential or a graduate education automatically gives that to anyone.

-Riley
It all depends on what "ability" means. I think you are talking about "do I have the mental faculties to understand and solve the problem given relevant experience and training?" vs "do I currently possess all the skills I need to solve the problem?" One is a statement about capability to acquire skills but the other is about having acquired them. Frankly, no one cares about the former. There are many smart pepole in the world. Many who are smarter than actuaries and could probably do the job better than current actuaries had they all spent a decade at an insurance company. All the value is in acquired skills, not raw abilities. Who cares what you hypothetically could do. Your value is in what you can do today.
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Last edited by JohnLocke; 01-13-2018 at 11:06 PM..
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  #45  
Old 01-13-2018, 11:18 PM
nonlnear nonlnear is offline
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It all depends on what "ability" means. I think you are talking about "do I have the mental faculties to understand and solve the problem given relevant experience and training?" vs "do I currently possess all the skills I need to solve the problem?" One is a statement about capability to acquire skills but the other is about having acquired them. Frankly, no one cares about the former. There are many smart pepole in the world. Many who are smarter than actuaries and could probably do the job better than current actuaries had they all spent a decade at an insurance company. All the value is in acquired skills, not raw abilities. Who cares what you hypothetically could do. Your value is in what you can do today.
There are companies whose management cares very much about the former. Maybe less in insurance than in other faster-moving industries, but this is not an actuarial thread. If anything, it seems to be a thread about leaving actuarial behind.
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  #46  
Old 01-13-2018, 11:55 PM
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It all depends on what "ability" means. I think you are talking about "do I have the mental faculties to understand and solve the problem given relevant experience and training?" vs "do I currently possess all the skills I need to solve the problem?" One is a statement about capability to acquire skills but the other is about having acquired them. Frankly, no one cares about the former. There are many smart pepole in the world. Many who are smarter than actuaries and could probably do the job better than current actuaries had they all spent a decade at an insurance company. All the value is in acquired skills, not raw abilities. Who cares what you hypothetically could do. Your value is in what you can do today.
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There are companies whose management cares very much about the former. Maybe less in insurance than in other faster-moving industries, but this is not an actuarial thread. If anything, it seems to be a thread about leaving actuarial behind.
I see where you're going with this, and in some aspects I agree but if that logic were carried to an extreme, nobody would be willing to hire ELs because they can't really do anything. I think to some extent there is an expectation that a hire will get better over time. Is there some more elaboration that would take this example into consideration?
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  #47  
Old 01-14-2018, 12:04 AM
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What separates them isn't the technical skill, but a credential, many years of domain expertise and a willingness to do mundane work. This fallacy about one type of quantitative person being able to solve business problems and another type of quantitative person not being able to solve business problems is annoying. Neither an actuarial credential or a graduate education automatically gives that to anyone.
No worries, I wasn't trying to make that dichotomy at all, and I've also been annoyed at being tagged as someone who can't see the big picture because I know how to use the command line - so my sympathies, if that's something you've ever experienced.

But first and foremost, actually solving those business problems takes priority. A simple model that improves profitability 2% that actually gets used, beats a more complex model that improves it by 10% but falls on deaf ears and never gets used - doesn't matter if the latter required a lot more brains to put together, it didn't actually solve the problem. The promotion goes to the person who is able to bring consensus between people who have conflicting goals to get those projects done.
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  #48  
Old 01-14-2018, 12:09 AM
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There are companies whose management cares very much about the former. Maybe less in insurance than in other faster-moving industries, but this is not an actuarial thread. If anything, it seems to be a thread about leaving actuarial behind.
A lot of analytical roles would value the former, in my opinion. It's rare to have someone who immediately knows how to build the specific model required for the given use case. It's assumed that the data scientist won't have all of the relevant technical skills and domain knowledge, and will fill in the gaps over time.
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  #49  
Old 01-14-2018, 12:49 AM
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whoanonstop whoanonstop is offline
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Originally Posted by Locrian View Post
One thing I'll say is that if you decide to build a strong statistical and machine learning background through things like grad classes and Elements etc., you might be well chasing the tail of those subjects' usefulness. I'm not sure I see the forecast/modeling type data scientist's future as being nearly as bright as others do.
I agree to the extent that the scope of data science jobs now and in the future will not revolve solely around the 3-4 cool techniques people have picked up from Coursera.

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Originally Posted by JohnLocke View Post
It all depends on what "ability" means. I think you are talking about "do I have the mental faculties to understand and solve the problem given relevant experience and training?" vs "do I currently possess all the skills I need to solve the problem?" One is a statement about capability to acquire skills but the other is about having acquired them. Frankly, no one cares about the former. There are many smart pepole in the world. Many who are smarter than actuaries and could probably do the job better than current actuaries had they all spent a decade at an insurance company. All the value is in acquired skills, not raw abilities. Who cares what you hypothetically could do. Your value is in what you can do today.
Although myopic, I agree with the bold above. However, with respect to a graduate education, it would be unfair to write off that time solving hard problems (even if not directly related) as not acquiring skills.

As for the comment that nobody cares about the former, personally, I don't give a crap what a person's educational background is if the person can prove that they can do it, but there are most definitely people in the world who do care.

-Riley
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  #50  
Old 01-14-2018, 12:53 AM
clarinetist clarinetist is offline
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Although myopic, I agree with the bold above. However, with respect to a graduate education, it would be unfair to write off that time solving hard problems (even if not directly related) as not acquiring skills.
Yeah, because a graduate degree is just an extended Bachelor's degree, amirite?
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