The title word of this post is Marathi for “phew!”—not for “hush hush.” (But, to me, the Marthi word is more expressive. [BTW, the “hu” is to be pronounced as “hoo”.])
The reason for this somewhat accentuated and prolonged exhalation is this: I am done with the version “0.1-beta” of my note on flux (see my last post). I need a break.
As of now, this note is about 27 pages, and with figures and some further (< 5%) additions, the final number of pages for the version 0.1 should easily go into the early 30s. … To be readable, it will have to brought down to about 15 pages or fewer, including diagrams. Preferably, < 10 pages. Some other day. For now, I find that I have grown plain sick and tired of working on that topic. I need to get away from it all. I am sure that I will return to it later on—may be after a month or so. But for the time being, I simply need a break—from it. I’ve had enough of this flux and vectors and tensors thing. … Which brings me to the next topic of this post.
There are also other announcements.
I think that I have also had enough of QM.
QM was interesting, very interesting, to me. It remained that way for some four decades. But now that I have cracked it (to my satisfaction), my interest in the topic has begun dwindling down very rapidly.
Sure I will conduct a few simulations, deliver the proposed seminar(s) I had mentioned in the past, and also write a paper or two about it. But I anticipate that I won’t go much farther than what I have already understood. The topic, now, simply has ceased to remain all that interesting.
But, yes, I have addressed all the essential QM riddles. That’s for certain.
And then, I was taking a stock of my current situation, and here are a few things that stood out:
- I am not getting an academic job in Pune (because of the stupid / evil SPPU rules), and frankly, the time when a (full) Professor’s job could have meant something to me is already over. If I were to get such a job well in time—which means years ago—then I could have done some engineering research (especially in CFD), guided a few students (in general in computational science and engineering), taught courses, developed notes, etc. But after having lost a decade or so due to that stupid and/or evil Metallurgy-vs-Mechanical Branch Jumping issue, I don’t have the time to pursue all that sort of a thing, any more.
- You would know this: All my savings are over; I am already in debts.
- I do not wish to get into a typical IT job. It could be well paying, but it involves absolutely no creativity and originality—I mean creativity involving theoretical aspects. Deep down in my heart, I remain a “theoretician”—and a programmer. But not a manager. There is some scope for creativity in the Indian IT industry, but at my “seniority,” it is mostly limited, in one way or the other, only to “herd-management” (to use an expression I have often heard from my friends in the industry). And, I am least bothered about that. So, to say that by entering the typical Indian IT job, my best skills (even “gifts”) would go under-utilized, would be an understatement.
- For someone like me, there is no more scope, in Pune, in the CFD field either. Consultants and others are already well established. I could keep my eyes and ears open. But it looks dicey to rely on this option. The best period for launching industrial careers in CFD here was, say, up to the early naughties. … I still could continue with some research in CFD. But guess it no longer is a viable career option for me. Not in Pune.
However, all is not gloomy. Not at all. Au contraire.
I am excited that I am now entering a new field.
I will not ask you to take a guess. This career route for people with my background and skills is so well-established by now, that there aren’t any more surprises left in it. Even an ANN would be able to guess it right.
Yes, that’s right. From now on, I am going to pursue Data Science.
This field—Data Science—has a lot of attractive features, as far as I am concerned. The way I see it, the following two stand out:
- There is a very wide variety of application contexts; and
- There is a fairly wide range of mathematical skills that you have to bring to bear on these problems.
Notice, the emphasis is on the width, not on the depth.
The above-mentioned two features, in turn, lead to or help explain many other features, like:
- A certain open ended-ness of solutions—pretty much like what you have in engineering research and design. In particular, one size doesn’t fit all.
- A relatively higher premium on the individual thinking skills—unlike what your run-of-the-mill BE in CS does, these days [^].
Yes, Data Science, as a field, will come to mature, too. The first sign that it is reaching the first stage of maturity would be an appearance of a book like “Design Patterns.”
However, even this first stage is, I anticipate, distant in future. All in all, I anticipate that the field will not come to mature before some 7–10 years pass by. And that’s long enough a time for me to find some steady career option in the meanwhile.
There are also some other plus points that this field holds from my point of view.
I have coded extensively—more than 1 lakh (100,000) lines of C++ code in all, before I came to stop using C++, which happened especially after I entered academia. I am already well familiar with Python and its eco-system, though am not an expert when it comes to writing the fastest possible numerical code in Python.
I have handled a great variety of maths. The list of equations mentioned in my recent post [^] is not even nearly exhaustive. (For instance, it does not even mention whole topics like probability and statistics, stereology, and many such matters.) When it comes to Data Science, a prior experience with a wide variety of maths is a plus point.
I have not directly worked with topics like artificial neural networks, deep learning, the more advanced regression analysis, etc.
However, realize that for someone like me, i.e., someone who taught FEM, and had thought of accelerating solvers via stochastic means, the topic of constrained optimization would not be an entirely unknown animal. Some acquaintance has already been made with the conjugate gradient (though I can’t claim mastery of it). Martingales theory—the basic idea—is not a complete unknown. (I had mentioned a basic comparison of my approach vis-a-vis the simplest or the most basic martingales theory, in my PhD thesis.)
Other minor points are these. This field also (i) involves visualization abilities, (ii) encourages good model building at the right level of abstraction, and (iii) places premium on presentation. I am not sure if I am good on the third count, but I sure am confident that I do pretty well on the first two. The roots of all my new research ideas, in fact, can be traced back to having to understand physical principles in settings.
Conclusion 1: I should be very much comfortable with Data Science. (Not sure if Data Science itself (i.e., Data Scientists themselves) would be comfortable with me or not. But that’s something I could deal later on.)
Conclusion 2: Expect blogging here going towards Data Science in the future.
A Song I Like:
(Marathi) “uff, teri adaa…”
Singer: Shankar Mahadevan
Lyrics: Javed Akhtar
[By any chance, was this tune at least inspired (if not plagiarized) from some Western song? Or is it through and through original? …In any case, I like it a lot. I find it wonderful. It’s upbeat, but not at all banging on the ears. (For contrast, attend any Ganapati procession, whether on the first day, the last day, or any other day in between. You will have ample opportunities to know what having your ears banged out to deafness means. Nay, these opportunities will be thrust upon you, whether you like it or not. It’s our “culture.”)]