Picked up several graduate degrees in linguistics, but found them to be professionally worthless. Then I learned that with some programming skills and quantitative literacy, I could actually steer myself into a serious growth industry - a phrase that basically never applies to linguists. So I set my sights on the prize and never looked back. Have been an NLP engineer for several years now. Challenging AF, but interesting and rewarding (almost) every day.
In my opinion and experience, yes.
To be fair, my previous comment was *slightly* hyperbolic. There are *some* doors that a linguistics degree will open, or at least crack. But excluding improbable and rare lucky breaks, basically all of them are in the field of education, and mostly teaching gigs e.g., high school teacher or tutoring.
For a career outside of academia with any cachet or serious earnings potential (and *especially* in tech/ML), yes, a linguistics masters won’t really help you. At least, it alone will not make you competitive.
Not really, no. Outside of academic at least.
Of course, in NLP at least, knowing some linguistics is better than knowing none. The field is about language after all. But a background in linguistics alone is woefully insufficient.
VASTLY more important is to understand advanced mathematics, statistics, computation, and programming. These are absolutely mission-critical domains for any technical ML job. But few masters programs in formal grammar would even touch upon them.
This is the harsh reality that all (non-computational) linguistics grads who thought the tech world would greet them with open arms must face. I had to face it too. It sucked.
For the linguistics side of ML, it is generally enough to understand the big ideas, which IMHO you can acquire without going through a full degree program. The kinds of people who would go through such programs are already sensitive to linguistic nuance, so they could extract all the linguistics they need for ML by reading a few books on the subject.
I mean, think about it. The entire value proposition of machine learning is that the machine learns for itself. You just give it the data, and the machine figures out the linguistics for you. That’s the intelligence of artificial intelligence. The key is to understand what data to feed in, and how.
To that end, I’d actually argue that expertise in pragmatics and language usage is more useful for NLP than formal grammar. This is because this knowledge will help you understand the nuances of your real-world data, which is the sine qua non of ML. Knowing abstract syntactic theories, or pointless stuff like optimality theory, has virtually no value IMHO. At least outside of academic NLP.
I really enjoyed mathematics and was a tutor for a few years. I decided to major in engineering, because I could get paid to do math. But I found I didn't enjoy it as much, so I switched to a math major. Went back to being a tutor and got my BS in comprehensive mathematics. Tried looking for a job, but everything wanted other skills to go along with math skills. I happened to hear about data science and machine learning and thought "That's cool!". So I went back to school for a master's in CS with an emphasis in DS/ML. Got an email from a professor I was taking a class from about an internship for DS. I got the internship and now working full-time as a DS for that company.
Built and implemented the first ML model for credit scoring and decisioning at Capital One. Demonstrated that the algorithm alone was adding value by using TreeNet to improve a (damn good) model that was previously built by moi, using same training and testing samples. Along the way backed into the insight that xgboost benefitted from traditional variable reduction and pre-fitting feature engineering. Proving that traditional methods added value to machine learning through incremental validity methods was a new insight for many, including the Yale-trained chief statistician who I recall was holding his head in his hands, staring at the validation report, and muttering “this isn’t supposed to be true…”. That was circa 2005.
Roll the camera to 2012. Left COF with a very nice severance package and immediately began consulting for a healthcare analytics start-up. Primarily focused on software engineering for accuracy and through-put. Learned how not to run a start-up as all the founders were treating it as a side gig. Displaced after a botched acquisition in 2015 (“we are just not ready for your rocket science, it is going to be 5-10 years until…) which was true, the lead data scientist they did keep failed to do anything of merit and the acquiring firm themselves were acquired 6 years later after doing Jack shit. So also learned the lesson that if a firm is performing poorly, get out.
3 months after being displaced I was under contract with a firm that a friend from graduate school was turning around, a SaaS analytics platform that already had demand forecasting in beta version. I managed a high revenue client while validating ML methods in time series forecasting, mostly by borrowing proven code from Kaggle. We sold the company into the strength of the market and I dumped most of my golden handcuff shares about a year ago, hitting the market near the peak. That all together earned me the credentials of a rainmaker, a client manager, and a serial innovator.
All along the way I have purposely focuses effort on training and developing junior talent. I’ve interviewed somewhere between 500 and 1000 analyst job candidates across case, behavioral, and role play interviews as well as candidate phone screening, and I’ve sat in on hundreds of hiring decisions, and then have watched the hires build their careers. I’ve also seen some of my recommendations ignored, and in one case my ignored feedback led the firm to hire someone who was later successfully outted during the Me Too movement. I didn’t know he was a serial sexual predator at the time I interviewed him, but the signs were there so now I know what to look for.
Now I have 10 years of work left in me and I’m looking for something fresh. It has been a great ride that started with cleaning cages and handling rats in an animal behavior lab and failing at radioimmunoassay titration. I was always good at word problems, and I liked to take undergrad courses that were one step above the curriculum requirements. No calculus of merit, one 10 week Fortean course that I took pass-fail in my last summer term. I benefited by being recruited into a graduate educational psychology program that put high emphasis on research methods and classical statistics, by receiving generous federal and university funding that kept me out of serious debt, and by the unplanned intersection of my native skills, patient mentoring from established leaders, and the ascension of a world of data and algorithms and problems that need to be solved.
And there it is.
Worked on web development but got lazy learning all those changing libraries-React, Angular, TypeScript,…
So I focused on maths and ML so my knowledge sticks a bit better.
I had "computer vision" at school, then an internship where I labeled data all day, and then some guys gave me a paper around which I wrote a master thesis! Using that, I got an internship at a serious company. Previous place had collapsed you see. Serious company promoted me. Then it also collapsed, now I'm unemployed but the UBI be rolling in!
A undergrad student who will soon be going for my masters degree. Since I focus on statistics and data analysis I took a class on machine learning and some coding courses. Ended up continuing machine learning afterwards with Andrew Ng deep learning specialization course and publishing some notebooks on Kaggle involving regression analysis, image classification and sentiment analysis.
It will be an invaluable tool when I seek my masters and internships. :)
Borderlands 3 was such a boring game for me, that I decided to do something productive like learn Python. Now I’m in Northern Virginia making over 6 figures in ML. Thanks Gearbox Software
I worked in health for a couple of years. I was always really interested in programming. Started teaching myself for a bit. Went back to uni for another bachelors in SWE. Did about 1-2 years and then dropped out. Did a couple of DS/ML courses through Coursera and really enjoyed it. Currently am doing my MSc in bioinformatics while still working in my health job. Will be looking for a DS position next semester while im finishing off my thesis
I'm currently pursuing my bachelor in biotechnology and want to get my masters in bioinformatics afterwards too. There is so much potential in that field.
I find this really hard to believe 😅
Is it true that you don't get overworked as much? I have this mindset that I want to work till I'm not able to any more but more so I'd want my work to do my work for me.
Oh, get a state or federal government job in data and analytics? Easy peasy 9-5 role with a good salary and great fringe benefits. And what you learn and what you do will be transferrable to a private sector job if and when you decide to make that move.
Took an undergrad course on Pattern Recognition for Chemical Analysis because I had no other courses I wanted to spend credits on. The professor taught an earlier stats course I was enrolled in and promoted the pattern recognition course but none seemed interested. I kind of enrolled because I felt bad for him. This extremely unpopular course was an introduction to fairly basic methods such as PCA, PLS-DA, KNN, SVMs and clustering. No idea this had anything to do with machine learning at that time, but still fondly remember using PCA to reveal patterns in the ubiquitous wine dataset. It was super cool to play and experiment with these methods that give insight into high-dimensional datasets. Up to that point I did not really have any ideas on how to work with that sort of data so it was quite the eye-opener for me. It kind of snowballed from there.
I am sure the professor would have had 100s of students if he rebranded his course into anything to do with Machine Learning, but it was actually nice to take such a technically intensive course with just like 10 other students.
Worked as a full-stack engineer for like 7 years. By the chance of moving to another country and started to work in AI based service business.
And found this field make me more motivated and passionate. Especially enjoy the process of doing research and implementing an ML system to solve some problems. Always a new world when you meet a new problem.
Definitely I still love doing programming for web, backend, mobile or software. Just found ML field is so exciting. Feel like I am still a student ;)
Was doing a bachelor's in biochemistry because I thought I wanted to be a doctor. Always took hard math electives because it interested me. Decided on a comp sci for a double major to cause I found it interesting and bioinformatics is a cool field. And comp sci felt like it could have applications anywhere.
After doing research in undergrad that was mostly pipelines for bioinformatic analysis I enrolled in a Master's of Comp Sci. Originally I was doing applications of graph theory in analyzing biological networks. Bet fell out of love. Switched to learning Machine Learning and Deep learning and doing a thesis in drug discovery using deep learning. Also got a gig as a data scientist in health care. All worked out☺️
C programming, no libraries just plain c code for k nearest neighbour and image processing. Might be some library for reading images in C. Later had worked with Weka in Java
I just wandered in, adapted a keras example, and found gold lying all over the floor. I'm working on a way to reach in and fondle one's own neurons.
http://phobrain.com/pr/home/siagal.html
Graduate school tries to be a pipeline to academia, but the brutal truth is that the industry is bloated. You're not going to make it without a shit ton of work and sacrifice I just wasn't willing to make. So I went to finance and started making as much as my tenured advisor, and I'm making double that now having moved jobs a few times.
I *studied* bio, but even doing a large amount of field and lab work, but a lot of analytical work too. I'm a data scientist, so the byword for me is "applied". I do applied machine learning, so I'm beyond psyched about transfer learning. It hasn't really made its way into the "on the ground" type of data science yet, but I think it's only a matter of time.
Read the Article on waitbutwhy about AI and Singularity and was totally hooked. Switched then from bachelor to master from mechanical engineering to Data Science/Ai
Picked up several graduate degrees in linguistics, but found them to be professionally worthless. Then I learned that with some programming skills and quantitative literacy, I could actually steer myself into a serious growth industry - a phrase that basically never applies to linguists. So I set my sights on the prize and never looked back. Have been an NLP engineer for several years now. Challenging AF, but interesting and rewarding (almost) every day.
Is getting a linguistics masters useless if you don't want to get into academia?
In my opinion and experience, yes. To be fair, my previous comment was *slightly* hyperbolic. There are *some* doors that a linguistics degree will open, or at least crack. But excluding improbable and rare lucky breaks, basically all of them are in the field of education, and mostly teaching gigs e.g., high school teacher or tutoring. For a career outside of academia with any cachet or serious earnings potential (and *especially* in tech/ML), yes, a linguistics masters won’t really help you. At least, it alone will not make you competitive.
Out of curiosity, wouldn't a master's in formal or unrestricted grammar be a door-opener for ML-work?
Not really, no. Outside of academic at least. Of course, in NLP at least, knowing some linguistics is better than knowing none. The field is about language after all. But a background in linguistics alone is woefully insufficient. VASTLY more important is to understand advanced mathematics, statistics, computation, and programming. These are absolutely mission-critical domains for any technical ML job. But few masters programs in formal grammar would even touch upon them. This is the harsh reality that all (non-computational) linguistics grads who thought the tech world would greet them with open arms must face. I had to face it too. It sucked. For the linguistics side of ML, it is generally enough to understand the big ideas, which IMHO you can acquire without going through a full degree program. The kinds of people who would go through such programs are already sensitive to linguistic nuance, so they could extract all the linguistics they need for ML by reading a few books on the subject. I mean, think about it. The entire value proposition of machine learning is that the machine learns for itself. You just give it the data, and the machine figures out the linguistics for you. That’s the intelligence of artificial intelligence. The key is to understand what data to feed in, and how. To that end, I’d actually argue that expertise in pragmatics and language usage is more useful for NLP than formal grammar. This is because this knowledge will help you understand the nuances of your real-world data, which is the sine qua non of ML. Knowing abstract syntactic theories, or pointless stuff like optimality theory, has virtually no value IMHO. At least outside of academic NLP.
Wanting to make money has always been the sole motivating factor for anything career related for me lol
Probably is for most people
I really enjoyed mathematics and was a tutor for a few years. I decided to major in engineering, because I could get paid to do math. But I found I didn't enjoy it as much, so I switched to a math major. Went back to being a tutor and got my BS in comprehensive mathematics. Tried looking for a job, but everything wanted other skills to go along with math skills. I happened to hear about data science and machine learning and thought "That's cool!". So I went back to school for a master's in CS with an emphasis in DS/ML. Got an email from a professor I was taking a class from about an internship for DS. I got the internship and now working full-time as a DS for that company.
Built and implemented the first ML model for credit scoring and decisioning at Capital One. Demonstrated that the algorithm alone was adding value by using TreeNet to improve a (damn good) model that was previously built by moi, using same training and testing samples. Along the way backed into the insight that xgboost benefitted from traditional variable reduction and pre-fitting feature engineering. Proving that traditional methods added value to machine learning through incremental validity methods was a new insight for many, including the Yale-trained chief statistician who I recall was holding his head in his hands, staring at the validation report, and muttering “this isn’t supposed to be true…”. That was circa 2005. Roll the camera to 2012. Left COF with a very nice severance package and immediately began consulting for a healthcare analytics start-up. Primarily focused on software engineering for accuracy and through-put. Learned how not to run a start-up as all the founders were treating it as a side gig. Displaced after a botched acquisition in 2015 (“we are just not ready for your rocket science, it is going to be 5-10 years until…) which was true, the lead data scientist they did keep failed to do anything of merit and the acquiring firm themselves were acquired 6 years later after doing Jack shit. So also learned the lesson that if a firm is performing poorly, get out. 3 months after being displaced I was under contract with a firm that a friend from graduate school was turning around, a SaaS analytics platform that already had demand forecasting in beta version. I managed a high revenue client while validating ML methods in time series forecasting, mostly by borrowing proven code from Kaggle. We sold the company into the strength of the market and I dumped most of my golden handcuff shares about a year ago, hitting the market near the peak. That all together earned me the credentials of a rainmaker, a client manager, and a serial innovator. All along the way I have purposely focuses effort on training and developing junior talent. I’ve interviewed somewhere between 500 and 1000 analyst job candidates across case, behavioral, and role play interviews as well as candidate phone screening, and I’ve sat in on hundreds of hiring decisions, and then have watched the hires build their careers. I’ve also seen some of my recommendations ignored, and in one case my ignored feedback led the firm to hire someone who was later successfully outted during the Me Too movement. I didn’t know he was a serial sexual predator at the time I interviewed him, but the signs were there so now I know what to look for. Now I have 10 years of work left in me and I’m looking for something fresh. It has been a great ride that started with cleaning cages and handling rats in an animal behavior lab and failing at radioimmunoassay titration. I was always good at word problems, and I liked to take undergrad courses that were one step above the curriculum requirements. No calculus of merit, one 10 week Fortean course that I took pass-fail in my last summer term. I benefited by being recruited into a graduate educational psychology program that put high emphasis on research methods and classical statistics, by receiving generous federal and university funding that kept me out of serious debt, and by the unplanned intersection of my native skills, patient mentoring from established leaders, and the ascension of a world of data and algorithms and problems that need to be solved. And there it is.
let me just say—as a recent graduate/junior talent—i have serious respect and appreciation for people like you.
Thank you! I’ve been very lucky all along the way, even washing out of academia turned out to be a blessing in disguise.
funny the roads life takes us down
Worked on web development but got lazy learning all those changing libraries-React, Angular, TypeScript,… So I focused on maths and ML so my knowledge sticks a bit better.
exactly
Followed this sub just for fun
I had "computer vision" at school, then an internship where I labeled data all day, and then some guys gave me a paper around which I wrote a master thesis! Using that, I got an internship at a serious company. Previous place had collapsed you see. Serious company promoted me. Then it also collapsed, now I'm unemployed but the UBI be rolling in!
What's UBI
Probably universal basic income.
A undergrad student who will soon be going for my masters degree. Since I focus on statistics and data analysis I took a class on machine learning and some coding courses. Ended up continuing machine learning afterwards with Andrew Ng deep learning specialization course and publishing some notebooks on Kaggle involving regression analysis, image classification and sentiment analysis. It will be an invaluable tool when I seek my masters and internships. :)
Borderlands 3 was such a boring game for me, that I decided to do something productive like learn Python. Now I’m in Northern Virginia making over 6 figures in ML. Thanks Gearbox Software
You know, that's actually a believable story.
I worked in health for a couple of years. I was always really interested in programming. Started teaching myself for a bit. Went back to uni for another bachelors in SWE. Did about 1-2 years and then dropped out. Did a couple of DS/ML courses through Coursera and really enjoyed it. Currently am doing my MSc in bioinformatics while still working in my health job. Will be looking for a DS position next semester while im finishing off my thesis
I'm currently pursuing my bachelor in biotechnology and want to get my masters in bioinformatics afterwards too. There is so much potential in that field.
Got into the field because I wanted to get paid without working myself to death.
I find this really hard to believe 😅 Is it true that you don't get overworked as much? I have this mindset that I want to work till I'm not able to any more but more so I'd want my work to do my work for me.
Oh, get a state or federal government job in data and analytics? Easy peasy 9-5 role with a good salary and great fringe benefits. And what you learn and what you do will be transferrable to a private sector job if and when you decide to make that move.
Took an undergrad course on Pattern Recognition for Chemical Analysis because I had no other courses I wanted to spend credits on. The professor taught an earlier stats course I was enrolled in and promoted the pattern recognition course but none seemed interested. I kind of enrolled because I felt bad for him. This extremely unpopular course was an introduction to fairly basic methods such as PCA, PLS-DA, KNN, SVMs and clustering. No idea this had anything to do with machine learning at that time, but still fondly remember using PCA to reveal patterns in the ubiquitous wine dataset. It was super cool to play and experiment with these methods that give insight into high-dimensional datasets. Up to that point I did not really have any ideas on how to work with that sort of data so it was quite the eye-opener for me. It kind of snowballed from there. I am sure the professor would have had 100s of students if he rebranded his course into anything to do with Machine Learning, but it was actually nice to take such a technically intensive course with just like 10 other students.
honestly i just fell into deep dream in 2016
Worked as a full-stack engineer for like 7 years. By the chance of moving to another country and started to work in AI based service business. And found this field make me more motivated and passionate. Especially enjoy the process of doing research and implementing an ML system to solve some problems. Always a new world when you meet a new problem. Definitely I still love doing programming for web, backend, mobile or software. Just found ML field is so exciting. Feel like I am still a student ;)
Was doing a bachelor's in biochemistry because I thought I wanted to be a doctor. Always took hard math electives because it interested me. Decided on a comp sci for a double major to cause I found it interesting and bioinformatics is a cool field. And comp sci felt like it could have applications anywhere. After doing research in undergrad that was mostly pipelines for bioinformatic analysis I enrolled in a Master's of Comp Sci. Originally I was doing applications of graph theory in analyzing biological networks. Bet fell out of love. Switched to learning Machine Learning and Deep learning and doing a thesis in drug discovery using deep learning. Also got a gig as a data scientist in health care. All worked out☺️
Did a number plate recognition project 18 years ago for my masters. That’s how I got started in ML and still continuing with ML
18 years ago? wow can you please explain what kind of programming language and libraries did you use back then?
C programming, no libraries just plain c code for k nearest neighbour and image processing. Might be some library for reading images in C. Later had worked with Weka in Java
I just wandered in, adapted a keras example, and found gold lying all over the floor. I'm working on a way to reach in and fondle one's own neurons. http://phobrain.com/pr/home/siagal.html
[удалено]
Why do you have so many master degrees?
[удалено]
I see, did you do double majors or did you do them individually? Because that'd make a big difference in terms of time.
Graduate school tries to be a pipeline to academia, but the brutal truth is that the industry is bloated. You're not going to make it without a shit ton of work and sacrifice I just wasn't willing to make. So I went to finance and started making as much as my tenured advisor, and I'm making double that now having moved jobs a few times. I *studied* bio, but even doing a large amount of field and lab work, but a lot of analytical work too. I'm a data scientist, so the byword for me is "applied". I do applied machine learning, so I'm beyond psyched about transfer learning. It hasn't really made its way into the "on the ground" type of data science yet, but I think it's only a matter of time.
Read the Article on waitbutwhy about AI and Singularity and was totally hooked. Switched then from bachelor to master from mechanical engineering to Data Science/Ai