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  #31  
Old 01-13-2018, 01:05 PM
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I'm still confused as to why you're even using Scala. You spent a good chunk of time learning Python and the Scala API and Python API in Databricks are optimized in the exact same way. Unless you're writing low level functions on RDDs, Scala isn't going to help you. Might as well stick to Python.

-Riley
I've taken an interest in functional programming. There are a lot of cool things about it and, assuming I do it correctly, solves a lot of hairy issues that arise in testing with mutable objects.

I still use Python at work, and R as well. But right now I'm exploring the language because I think it's very interesting. Do what you like, no?
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  #32  
Old 01-13-2018, 01:10 PM
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Also, GraphX isn't implemented in Python yet, so I do have a specific use case for it in the short term.
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  #33  
Old 01-13-2018, 01:22 PM
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I wonder whether "Data Scientist" will ever make #1 on the best jobs list.
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  #34  
Old 01-13-2018, 02:02 PM
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Also, GraphX isn't implemented in Python yet, so I do have a specific use case for it in the short term.
Just as RDDs aren't implemented in Python. You should avoid GraphX over GraphFrames unless you have a real good reason:

https://graphframes.github.io/api/python/index.html
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Old 01-13-2018, 02:07 PM
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Also recognize that it won't just affect your first job search, but likely your career as a whole. Although unlikely, there is a world where my MS in Physics and a lot of working; networking, studying, etc, could have gotten me a "PhD Job" somewhere in the private sector. An initial luck in that strategy would have created heartbreak further down the road if I was trying to move to another position elsewhere. It isn't that exaggerated in Data Science and the hill can be climbed, but I'd be lying if I said that nobody will look down on your educational background and getting a job that has a tag as "data scientist" won't mean an automatic ability to move to places that are more sophisticated.

-Riley
I think you might be selling yourself too short... if you did get the "PhD Job" with only an MS Physics, and if you had strong performance, then that can only reflect well on you. Other employers, should you look elsewhere, should view your experience favorably.
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  #36  
Old 01-13-2018, 02:49 PM
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Just as RDDs aren't implemented in Python. You should avoid GraphX over GraphFrames unless you have a real good reason:

https://graphframes.github.io/api/python/index.html
Oh hey that's pretty neat. Stuff in Spark changes so quickly that the popular stuff today is radically different from just a couple years ago.I think I'll switch to that because I actually switched some of my data frames back into an RDD to calculate some things. Now I can avoid that...

RDDs seem like ancient history even though Spark was first released in 2014.

But graphframes also has a Scala API...
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  #37  
Old 01-13-2018, 03:25 PM
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Also recognize that it won't just affect your first job search, but likely your career as a whole. It isn't that exaggerated in Data Science and the hill can be climbed, but I'd be lying if I said that nobody will look down on your educational background and getting a job that has a tag as "data scientist" won't mean an automatic ability to move to places that are more sophisticated.
I am aware that I may be considered "less than" for a while, but given how much information that are freely accessible online and how may people who're currently in high school or college have been actively learning about these techniques, I believe that there will eventually be a generation of very skilled data scientists without a formal PhD education.

I think what makes the tech industry so unique is that things are moving so fast and what was considered "new" a only few years ago may be outdated today- like MapReduce. This kind of structure is not very compatible with the traditional spend-10-years-in-school-and-learn-about-techniques-invented-100-years-ago route. The reason why PhD's are so valued today is that there just aren't many skilled data scienctists out there today and PhD's are always a safer bet. There will always be R&D positions reserved for PhD's, but I am really hoping that there will be less stigma against the less *formally* educated down the road.

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OP, have you ever thought about being a software engineer instead? Correct me if I'm wrong, but your actual skill seems to be more appreciated than your credentials in that field.
I am not a huge fan of coding despite being better at coding than most actuaries, so I'd probably stay away from software engineering jobs...
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  #38  
Old 01-13-2018, 05:24 PM
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I am aware that I may be considered "less than" for a while, but given how much information that are freely accessible online and how may people who're currently in high school or college have been actively learning about these techniques, I believe that there will eventually be a generation of very skilled data scientists without a formal PhD education.
People in high school and with Bachelor's degrees - for the most part - only have a superficial understanding of what they're doing. They know the buzzwords, they may run lm() in R, read through ISLR, and consider themselves a "data scientist."

The stuff that is making people marketable - and around Riley's pay range - is stuff that you can't find through Coursera, Udacity, etc.. I don't expect this to change anytime soon. These venues are catering to those with no technical background whatsoever and are only doing as well as they are because of the hype.

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.

For example, Casella and Berger is a very, very popular mathematical stats textbook and has been around since 1990. It is horribly written. As are all of the mathematical stats books that I've read at the grad level. I have not found any source, online or on print, which teaches this material better than C & B. My stats professor for this course was amazing.
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  #39  
Old 01-13-2018, 05:52 PM
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People in high school and with Bachelor's degrees - for the most part - only have a superficial understanding of what they're doing. They know the buzzwords, they may run lm() in R, read through ISLR, and consider themselves a "data scientist."

The stuff that is making people marketable - and around Riley's pay range - is stuff that you can't find through Coursera, Udacity, etc.. I don't expect this to change anytime soon. These venues are catering to those with no technical background whatsoever and are only doing as well as they are because of the hype.

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.

For example, Casella and Berger is a very, very popular mathematical stats textbook and has been around since 1990. It is horribly written. As are all of the mathematical stats books that I've read at the grad level. I have not found any source, online or on print, which teaches this material better than C & B. My stats professor for this course was amazing.
I think the some of the things you're referring to, if I'm interpreting what you wrote correctly - stuff for which libraries in popular languages don't even exist yet, can fetch $300k+ per year if that's what you're actually doing in the private sector.

A coworker of mine mentioned that a lot of the stuff you see in academia appears several years, maybe even a few decades before it is ever implemented in industry.

There are actuaries who are making $150k+ per year running PROC logistic on small data. Heck, they're actually making that much by doing routine BF/CC methods in Excel. What separates them isn't really technical skill, but the ability to solve business problems, and a lot of the time the solutions to those problems aren't very complicated from a technical standpoint.
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  #40  
Old 01-13-2018, 07:14 PM
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The reason why PhD's are so valued today is that there just aren't many skilled data scienctists out there today and PhD's are always a safer bet.
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.

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What separates them isn't really technical skill, but the ability to solve business problems, and a lot of the time the solutions to those problems aren't very complicated from a technical standpoint.
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
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