[stared]

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

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"?

[laxatives]

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-... 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]

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.

[shkr]

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

[karpathy]

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]

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]

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.

[roel_v]

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.

[thr0waway1239]

A friend of mine did a PhD in Biochemistry. Here is what I learnt, based on the many conversations I had with him while he was doing his PhD:

Freedom to choose the topic: Only to the extent that he could choose the advisor amongst a list of 3-4, which gets narrower after advisor selection

Ownership: I understand the sense in which the author wrote this, so I cannot disagree too much. But my friend would often explain how he could be most productive precisely by being a 'cog in the wheel' in the sense of how much cooperation was required from his lab mates for him to make any amount of reasonable progress

Exclusivity: The exclusivity is true in the sense it is described, but unlike in CS, did not lead to any major benefits (so sort of diminishes the leading statement about the appeal of the PhD). He went through a couple of post-docs, and then eventually landed what was a coveted position in his field - which still pays not very much

Status: This is the only part which I completely agree, and I actually respect non C.S. Ph.D.s all the more for their persistence because there is very rarely an escape hatch.

Personal freedom: Almost 100% not applicable. As I mentioned before, my friend could not make any progress without a ton of cooperation from lab mates, needed to be in the lab usually based on timings of other lab mates.

Maximizing future choice: Nearly every discipline other than CS would disagree with this. If you read stared's story, you get the distinct sense that his choices were maximized because he came into data science (i.e. he didn't feel that way while in his discipline)

Maximizing variance: This is immensely difficult for science Ph.D.s from what I understand, because the process from Bachelors to PhD, and often with a PostDoc or two on the way, is already too long for most of them and takes up their best years. So the statement "You’re young and there’s really no need to rush" is, well, a bit impractical.

I cannot comment on 'Personal Growth' and 'Expertise' - I don't know if you need a PhD for the former, and the latter is wonderful as long as the cost is not exorbitant (this exorbitant cost is common in other disciplines)

If the advice here is widely generalizable, then I would really like to see a few links to PhDs in, e.g. the physical sciences talking about their experiences with similar pleasure.

On the other hand, the stories I heard from my friends who did their Ph.D.s in other disciplines (Chemical Engineering, Mechanical Engg., Civil Engg., Chemistry, Physics to name some I remember) all had very similar patterns in their horror stories of the lack of resources and its impact on their journey.

So, is it at all possible that a CS Ph.D. who worked on an excellent topic (for which karpathy gets my kudos) in an internet-friendly, internet-visible, exploding field of work at a top institute might not paint anything close to the full picture?

True, that is the not the full article. But the best way to be safe is to never get into a fight - don't get into a PhD without understanding what you are signing up for. The section answering the question: "First, should you want to get a PhD?" is not well researched or widely sourced once you consider that Computer Science is not the only discipline in which people get their PhDs. Hence the suggestion to change the title to C.S. Ph.D.

[stared]

Yes, it is a small part of text, but one that may persuade someone into doing PhD, or give a false impression (e.g. the typical one, in which I used to believe: "follow your dreams in academia or get money at a dull job").

(BTW: I guess you know http://www.pgbovine.net/PhD-memoir.htm. Also from an uber-successful PhD student, but the full story, rather than a set of advice.)

It may be something about the field (growth, competition with industry). I think it was not a coincidence that out of many friends of mine who did their PhDs, only Wojciech Zaremba (now in OpenAI) had some non-trivial impact on the world.

I don't want to imply that even if everything works (topic, advisor, funding, the sense of meaning, the sense of progress, ...) it is any easy path. And I am really sure that even with your skills, work ethics (and luck) it was a challenge. Still, even if one field is rosy (DL or maybe CS in general), a typical PhD experience is hardly sth I would recommend blindly (vide links there: https://pinboard.in/search/u:pmigdal?query=academia+depressi...).

(On an unrelated note: thank you for "The Unreasonable Effectiveness of Recurrent Neural Networks", ConvNetJS and CS231N - they brought me into the deep learning world. :))

[BenoitP]

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]

It's a longer story, with summary is in "My story" from 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-ph...

[codepie]

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]

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