5 ms·

A good programmer with even just a high level overview of ML and Stats concepts would be an incredibly valuable asset to a data science team. Most ML people are

A good programmer with even just a high level overview of ML and Stats concepts would be an incredibly valuable asset to a data science team. Most ML people are academics who tend to not have good software engineering skills, finding people who master both domains is really hard. Also to add to that most of the work in ML is feature engineering, data cleaning, testing and building pipelines which all require a good software engineering background.

carlmcqueen 9y ago
I work for a technical statistical team in the financial world, we've been hiring PHDs lately to my team which has just meant exactly what this comment points out. I do a lot of the grunt work of getting the data sourced, cleaned and ready and am called the 'data wizard' and other such annoying names. What's frustrating is I can run the last lines of code and read and understand the output of the last step, but as the original question asked, management would prefer someone with a phd or masters in customer analytics to be the expert of the data output.
- watty 9y ago
  Sounds more like a "data monkey" (no offense)
  - alexpetralia 9y ago
    Data monkey or data engineer? #loadedlanguage
    - venture_lol 9y ago
      Data Sanitation Engineering
- maxwin 9y ago
  It sucks that you are doing the boring data cleansing job. And those people with degrees do the more interesting higher level analysis.
  - stuffedBelly 9y ago
    That's actually quite common, especially in financial industry. I work at a financial company and do the same line of work building data management tools/ETL systems that provide data to researchers on demand. There are many interesting challenges but a lot of grunt work too. Many of my teammates have CFA certs and math + CS degree (though non-PhDs) so majority of them are definitely capable of performing high-level analysis/financial modeling. However, only a few are needed to do research, the rest implementing and maintaining data infrastructure, which is quite a lot of work and needs more head counts.
  - debacle 9y ago
    Data cleansing + pipelining requires a certain degree of cleverness.
Helmet 9y ago
Really? I don't know if I'm good, but I think I'm at least a "decent" programmer and I have a solid grasp of ML and Stats concepts and I've had absolutely no luck getting interviews, much less call backs for data science jobs.
- ooqr 9y ago
  Heck, before my first web dev job, it was VERY difficult to get a foot in the door. Now I feel like getting a restraining order on recruiters.
- rdeboo 9y ago
  I'd try to apply as Data Engineer, you'll be close to the data science work and can more easily pivot into there if you want.
- quadrature 9y ago
  If only you were willing to work in Canada :P.
  - colmvp 9y ago
    Have you seen the job listings for data scientists in Canada? It's paltry even here in Toronto compared to parts of the U.S. like SF which have a fraction of the population. Last time I checked, when I searched Data Scientist in SF there were 3000+ listings while in Toronto there maybe a little over a hundred. And of the few job listings I've seen, most have high standards (PhD or min. Masters, x years of experience) with old companies (banks, car companies). What's funny is that a lot of people in my circle in Canada are actually doing work for companies outside of the country (U.S., China...)
    - quadrature 9y ago
      I was speaking in context of the company i work for. You're right but we're getting there.
jcadam 9y ago
I'm currently working through Andrew Ng's ML course on Coursera. It's definitely high-level (though a fantastic overview of the fundamentals), and I plan to take something a little more mathematically rigorous at some point, but I'll probably want to take some refresher courses in Linear Algebra and calc before doing so. I'm not trying to learn about ML for purposes of employment, It's somewhat relevant to my current job, and I may have some interest in using it on my personal projects. But mostly I'm just learning for the 'fun' of it :) I don't have the time, money, or inclination to pursue a MS in data science atm (My current 'formal' education consists of a BS in Comp Sci and an MBA), but I may go back to school when the kids are grown, more for personal edification than anything else, however. A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37).
- cr0sh 9y ago
  > I'm currently working through Andrew Ng's ML course on Coursera. It's definitely high-level I agree that it is "high level" and glosses over (purposefully) the nitty-gritty details of the "black boxes" for the most part. I say this as someone who took the first incarnation of the course, which was known as "ML Class" in the fall of 2011, before Coursera came about. Despite it being high-level, though - this is what one of my "classmates" was able to create, about halfway or so thru the course: http://blog.davidsingleton.org/nnrccar/ http://blog.davidsingleton.org/nnrccar/ In 2012, I completed Udacity's CS373 course (https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r...). Today, I'm currently in the second term of Udacity's Self-Driving Car Engineer Nanodegree (the current lesson I'm on actually is a part of CS373 - so it's a kind of review lesson for me - heh). I'm having a great time learning about more in-depth understanding and knowledge relating to self-driving vehicles. Much of the learning can be applied to other areas of ML as well (learning how to use and abuse TensorFlow and Keras, for instance). > A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37). Don't let that stop ya! My plan after finishing this Udacity course is to actually work toward getting my BS and maybe MS in Comp Sci. By that time, I'll be well into my 44th year of age. I don't know if any of this will lead to a different direction in my career, but that isn't something I am really worried or planning about. I'm currently happy with where my career is; it pays the bills and allows for some fun, too. But if it should lead in another direction, so be it! I figure having this knowledge can't hurt me as a employment candidate, and will likely be seen as a plus. Worst case scenario, it will make my hobbyist robotics projects more interesting. I figure I have another 20 or more years in me doing software development (assuming it remains a career option, of course); I personally have met more that a few other developers that age or older who are still making a living at it. So I'm not ruling out the possibility of a lateral move toward something involving my knowledge of machine learning. Good luck with your studies!
deleted 9y ago
[deleted]

carlmcqueen 9y ago

I work for a technical statistical team in the financial world, we've been hiring PHDs lately to my team which has just meant exactly what this comment points out. I do a lot of the grunt work of getting the data sourced, cleaned and ready and am called the 'data wizard' and other such annoying names. What's frustrating is I can run the last lines of code and read and understand the output of the last step, but as the original question asked, management would prefer someone with a phd or masters in customer analytics to be the expert of the data output.

watty 9y ago
Sounds more like a "data monkey" (no offense)
- alexpetralia 9y ago
  Data monkey or data engineer? #loadedlanguage
  - venture_lol 9y ago
    Data Sanitation Engineering
maxwin 9y ago
It sucks that you are doing the boring data cleansing job. And those people with degrees do the more interesting higher level analysis.
- stuffedBelly 9y ago
  That's actually quite common, especially in financial industry. I work at a financial company and do the same line of work building data management tools/ETL systems that provide data to researchers on demand. There are many interesting challenges but a lot of grunt work too. Many of my teammates have CFA certs and math + CS degree (though non-PhDs) so majority of them are definitely capable of performing high-level analysis/financial modeling. However, only a few are needed to do research, the rest implementing and maintaining data infrastructure, which is quite a lot of work and needs more head counts.
- debacle 9y ago
  Data cleansing + pipelining requires a certain degree of cleverness.

Helmet 9y ago

Really? I don't know if I'm good, but I think I'm at least a "decent" programmer and I have a solid grasp of ML and Stats concepts and I've had absolutely no luck getting interviews, much less call backs for data science jobs.

ooqr 9y ago
Heck, before my first web dev job, it was VERY difficult to get a foot in the door. Now I feel like getting a restraining order on recruiters.
rdeboo 9y ago
I'd try to apply as Data Engineer, you'll be close to the data science work and can more easily pivot into there if you want.
quadrature 9y ago
If only you were willing to work in Canada :P.
- colmvp 9y ago
  Have you seen the job listings for data scientists in Canada? It's paltry even here in Toronto compared to parts of the U.S. like SF which have a fraction of the population. Last time I checked, when I searched Data Scientist in SF there were 3000+ listings while in Toronto there maybe a little over a hundred. And of the few job listings I've seen, most have high standards (PhD or min. Masters, x years of experience) with old companies (banks, car companies). What's funny is that a lot of people in my circle in Canada are actually doing work for companies outside of the country (U.S., China...)
  - quadrature 9y ago
    I was speaking in context of the company i work for. You're right but we're getting there.

jcadam 9y ago

I'm currently working through Andrew Ng's ML course on Coursera. It's definitely high-level (though a fantastic overview of the fundamentals), and I plan to take something a little more mathematically rigorous at some point, but I'll probably want to take some refresher courses in Linear Algebra and calc before doing so. I'm not trying to learn about ML for purposes of employment, It's somewhat relevant to my current job, and I may have some interest in using it on my personal projects. But mostly I'm just learning for the 'fun' of it :) I don't have the time, money, or inclination to pursue a MS in data science atm (My current 'formal' education consists of a BS in Comp Sci and an MBA), but I may go back to school when the kids are grown, more for personal edification than anything else, however. A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37).

cr0sh 9y ago
> I'm currently working through Andrew Ng's ML course on Coursera. It's definitely high-level I agree that it is "high level" and glosses over (purposefully) the nitty-gritty details of the "black boxes" for the most part. I say this as someone who took the first incarnation of the course, which was known as "ML Class" in the fall of 2011, before Coursera came about. Despite it being high-level, though - this is what one of my "classmates" was able to create, about halfway or so thru the course: http://blog.davidsingleton.org/nnrccar/ http://blog.davidsingleton.org/nnrccar/ In 2012, I completed Udacity's CS373 course (https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r...). Today, I'm currently in the second term of Udacity's Self-Driving Car Engineer Nanodegree (the current lesson I'm on actually is a part of CS373 - so it's a kind of review lesson for me - heh). I'm having a great time learning about more in-depth understanding and knowledge relating to self-driving vehicles. Much of the learning can be applied to other areas of ML as well (learning how to use and abuse TensorFlow and Keras, for instance). > A big shift in career, from software engineer to 'data scientist (or whatever they call it)' is probably not possible at my advanced age (37). Don't let that stop ya! My plan after finishing this Udacity course is to actually work toward getting my BS and maybe MS in Comp Sci. By that time, I'll be well into my 44th year of age. I don't know if any of this will lead to a different direction in my career, but that isn't something I am really worried or planning about. I'm currently happy with where my career is; it pays the bills and allows for some fun, too. But if it should lead in another direction, so be it! I figure having this knowledge can't hurt me as a employment candidate, and will likely be seen as a plus. Worst case scenario, it will make my hobbyist robotics projects more interesting. I figure I have another 20 or more years in me doing software development (assuming it remains a career option, of course); I personally have met more that a few other developers that age or older who are still making a living at it. So I'm not ruling out the possibility of a lateral move toward something involving my knowledge of machine learning. Good luck with your studies!

deleted 9y ago

[deleted]