4 ms·

Well, I went from academia (after finishing my PhD) to industry, to get more Freedom, Ownership, Personal growth, Status and Expertise. Here is a blog post on

by stared 10y ago

Well, I went from academia (after finishing my PhD) to industry, to get more Freedom, Ownership, Personal growth, Status and Expertise. Here is a blog post on my transition from theoretical physics to data science (and how it made my life much better): http://p.migdal.pl/2015/12/14/sci-to-data-sci.html http://p.migdal.pl/2015/12/14/sci-to-data-sci.html I understand that Andriej Karpathy (my favourite author/lecturer in Deep Learning, by a large margin) had a wonderful PhD, in a fast-growing field, with a golden fall-back option. But most PhD students I know (including my former self) do things in disciplines no-one else cares about and are tied to their institute/advisor/place with little to no opportunity to change things when they go awry (cf. it's super easy to change a company). A non-trivial fraction of my friends suffered from depression or had a serious mental breakdown (again, including myself). In this light, while it contain a large number of helpful tips and valuable pieces of advise, why is it called "survival guide"?

shkr 10y ago
I think the title should have been more like "These are the great things that happened to me during my PhD"
laxatives 10y ago
Karpathy has the benefit of being one of the best known bloggers/teachers in the most popular graduate-level course in possibly the most financially successful subfields ever while graduating at the peak of industry spending (so far). He's almost certainly the student described in http://www.nytimes.com/2016/03/26/technology/the-race-is-on-to-control-artificial-intelligence-and-techs-future.html http://www.nytimes.com/2016/03/26/technology/the-race-is-on-... His experiences and successes are not generalizable. But he's probably my favorite blogger too and its at the very least interesting to hear his take on his experiences.
- nl 10y ago
  I'd assumed Karpathy was the the $1M student too, but apparently it was someone else (source: inter-university gossip). The same source did say the big4 were marking $300K offers on the NIPS floor last year, which I find even more extreme.
karpathy 10y ago
It seems that a larger-than-I-expected fraction of the response to my post concentrates on a very small part of it, especially the part where I enumerate some considerations for thinking about whether a PhD might be a good fit for you. By far the largest fraction of the post is concerned with tips/tricks for effectively navigating the PhD experience once you commit to going through it. I jokingly refer to it as a "survival guide" because (as I mention in the disclaimer paragraph) the experience is by no means a walk in the park.
- thr0waway1239 10y ago
  Also do you mind modifying the title to say a Survival Guide to a C.S. PhD? Absolutely everything the GP says is spot on, the C.S. PhD is really atypical amongst all the Ph.D. disciplines. E.g. the resource constraints that exist in many disciplines don't even exist in C.S. - it is almost completely the product of thought/ideas and ever cheapening computing power. I would suggest sending out your article to Ph.D. students in different disciplines, look at all the feedback, and then incorporate them if you are so inclined. The article might look quite different. Edit: after reading the comment from pgbovine, I felt I made an unnecessary personal comment, which I omitted. I rewrote the sentence to keep the main point.
  - pgbovine 10y ago
    My unsolicited advice: Don't change a word. At most, link to this HN thread so that readers can see different perspectives, but you wrote the article you wanted to write, not the one that anonymous online critics wanted you to write. If others want to write a response saying why you're misguided, by all means go ahead.
    - deleted 10y ago
      [deleted]
  - roel_v 10y ago
    I disagree. It's true that it's easier to get funding for CS but the career strategies described are exactly the same for most other fields. Many phd students just can't understand this post though; they think they're 'students' like they were in undergrad but it's whole different ballgame. The concept of the symbiotic relationship between adviser and student for example - most phd students just cannot see that, even after it's spelled out for them. Ito requires a certain mental maturity that most 23 year olds just do not have, it seems.
codepie 10y ago
I recently met a professor, he said that most of his students are working in Deep Learning and he is finding it difficult to get people to work on other interesting problems. So there is really a demand of students to work on other areas as well. But most of the students get carried away by the trending fields.
- sevensor 10y ago
  This is a problem in every field. Academic fads sweep everything else aside and then come crashing down.
BenoitP 10y ago
In your blog post, you write: > I do data science freelancing. That is, I take contracts related to machine learning (predicting things, e.g. user growth of a company), data visualization (custom charts in D3.js), preparing and conducting trainings in data analysis [...] Would you mind sharing a bit on your approach to contracting in this space? Here are a few questions: Do you do blind calls? Do you use a freelancing site? Do you work remotely? What is the typical contract, how much do you bill? Does one have to do public talks to get recognised? How much do clients value your having a PhD? How do you animate the networking? Do clients find you, or do you find them? Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject? If you had to specialise in one niche market, what would be, what would be your approach? Basically: what would be the steps you would take should you start only with the data science technical knowledge?
- stared 10y ago
  It's a longer story, with summary is in "My story" from http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph.... Since it had some turbulent nature, it was hard for me to put it into a coherent narrative. And it may be even harder to put it in a way that is beneficial for others (involves my particular situation, skillset, network of contacts, personality). If you mail me (my website's footer), I will send you a quick&dirty summary of my path & projects. In any case, some answers: > Do you do blind calls? Never! But if there is an opening for a full-time position sometimes I mailed them anyway, if they are interested in some specialised contracts; sometimes they were. > Do you use a freelancing site? No. I followed a mailing list with freelance projects in data viz. (By far the easiest place to start, as they can SEE my previous projects and current progress.) > Do you work remotely? Almost entirely. But for the last few weeks I am a bit more related to a particular company, and then I prefer to be on site (easier to talk etc). > What is the typical contract, how much do you bill? Since it varies a lot (and I want to increase it a bit), I am not comfortable to put in publicly. Expect for things I do it is also dependent on place in which I live, particular projects, negotiation skills, project uncertainty (data science is not webdev - each project has a research part). > Does one have to do public talks to get recognised? Yes. I mean, maybe it is not strictly necessary, but public talks (meetups, conferences, etc) and other public activity (blog posts, running communities) helped me a lot! (But I love it anyway.) > How much do clients value your having a PhD? A wonderful discussion starter, never a deal-maker. > How do you animate the networking? (Answered above.) > Do clients find you, or do you find them? In the last year (or more) it's only clients who contact me, and I accept (/follow up) or decline projects. > Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject? ? > If you had to specialise in one niche market, what would be, what would be your approach? If you were an animal, which... ;) > Basically: what would be the steps you would take should you start only with the data science technical knowledge? http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph...

by stared 10y ago

shkr 10y ago

I think the title should have been more like "These are the great things that happened to me during my PhD"

laxatives 10y ago

Karpathy has the benefit of being one of the best known bloggers/teachers in the most popular graduate-level course in possibly the most financially successful subfields ever while graduating at the peak of industry spending (so far). He's almost certainly the student described in http://www.nytimes.com/2016/03/26/technology/the-race-is-on-to-control-artificial-intelligence-and-techs-future.html http://www.nytimes.com/2016/03/26/technology/the-race-is-on-... His experiences and successes are not generalizable. But he's probably my favorite blogger too and its at the very least interesting to hear his take on his experiences.

nl 10y ago
I'd assumed Karpathy was the the $1M student too, but apparently it was someone else (source: inter-university gossip). The same source did say the big4 were marking $300K offers on the NIPS floor last year, which I find even more extreme.

karpathy 10y ago

It seems that a larger-than-I-expected fraction of the response to my post concentrates on a very small part of it, especially the part where I enumerate some considerations for thinking about whether a PhD might be a good fit for you. By far the largest fraction of the post is concerned with tips/tricks for effectively navigating the PhD experience once you commit to going through it. I jokingly refer to it as a "survival guide" because (as I mention in the disclaimer paragraph) the experience is by no means a walk in the park.

thr0waway1239 10y ago
Also do you mind modifying the title to say a Survival Guide to a C.S. PhD? Absolutely everything the GP says is spot on, the C.S. PhD is really atypical amongst all the Ph.D. disciplines. E.g. the resource constraints that exist in many disciplines don't even exist in C.S. - it is almost completely the product of thought/ideas and ever cheapening computing power. I would suggest sending out your article to Ph.D. students in different disciplines, look at all the feedback, and then incorporate them if you are so inclined. The article might look quite different. Edit: after reading the comment from pgbovine, I felt I made an unnecessary personal comment, which I omitted. I rewrote the sentence to keep the main point.
- pgbovine 10y ago
  My unsolicited advice: Don't change a word. At most, link to this HN thread so that readers can see different perspectives, but you wrote the article you wanted to write, not the one that anonymous online critics wanted you to write. If others want to write a response saying why you're misguided, by all means go ahead.
  - deleted 10y ago
    [deleted]
- roel_v 10y ago
  I disagree. It's true that it's easier to get funding for CS but the career strategies described are exactly the same for most other fields. Many phd students just can't understand this post though; they think they're 'students' like they were in undergrad but it's whole different ballgame. The concept of the symbiotic relationship between adviser and student for example - most phd students just cannot see that, even after it's spelled out for them. Ito requires a certain mental maturity that most 23 year olds just do not have, it seems.

codepie 10y ago

I recently met a professor, he said that most of his students are working in Deep Learning and he is finding it difficult to get people to work on other interesting problems. So there is really a demand of students to work on other areas as well. But most of the students get carried away by the trending fields.

sevensor 10y ago
This is a problem in every field. Academic fads sweep everything else aside and then come crashing down.

BenoitP 10y ago

In your blog post, you write: > I do data science freelancing. That is, I take contracts related to machine learning (predicting things, e.g. user growth of a company), data visualization (custom charts in D3.js), preparing and conducting trainings in data analysis [...] Would you mind sharing a bit on your approach to contracting in this space? Here are a few questions: Do you do blind calls? Do you use a freelancing site? Do you work remotely? What is the typical contract, how much do you bill? Does one have to do public talks to get recognised? How much do clients value your having a PhD? How do you animate the networking? Do clients find you, or do you find them? Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject? If you had to specialise in one niche market, what would be, what would be your approach? Basically: what would be the steps you would take should you start only with the data science technical knowledge?

stared 10y ago
It's a longer story, with summary is in "My story" from http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph.... Since it had some turbulent nature, it was hard for me to put it into a coherent narrative. And it may be even harder to put it in a way that is beneficial for others (involves my particular situation, skillset, network of contacts, personality). If you mail me (my website's footer), I will send you a quick&dirty summary of my path & projects. In any case, some answers: > Do you do blind calls? Never! But if there is an opening for a full-time position sometimes I mailed them anyway, if they are interested in some specialised contracts; sometimes they were. > Do you use a freelancing site? No. I followed a mailing list with freelance projects in data viz. (By far the easiest place to start, as they can SEE my previous projects and current progress.) > Do you work remotely? Almost entirely. But for the last few weeks I am a bit more related to a particular company, and then I prefer to be on site (easier to talk etc). > What is the typical contract, how much do you bill? Since it varies a lot (and I want to increase it a bit), I am not comfortable to put in publicly. Expect for things I do it is also dependent on place in which I live, particular projects, negotiation skills, project uncertainty (data science is not webdev - each project has a research part). > Does one have to do public talks to get recognised? Yes. I mean, maybe it is not strictly necessary, but public talks (meetups, conferences, etc) and other public activity (blog posts, running communities) helped me a lot! (But I love it anyway.) > How much do clients value your having a PhD? A wonderful discussion starter, never a deal-maker. > How do you animate the networking? (Answered above.) > Do clients find you, or do you find them? In the last year (or more) it's only clients who contact me, and I accept (/follow up) or decline projects. > Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject? ? > If you had to specialise in one niche market, what would be, what would be your approach? If you were an animal, which... ;) > Basically: what would be the steps you would take should you start only with the data science technical knowledge? http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph...