Self-Made in Data Science: A Good Idea?

What exactly does 'self-made' mean? Self-made or not isn't a binary condition. After all, everyone is self-made to some extent. As Andrew Ng in his video with Lex Fridman says, everyone is self-made, including himself. An inventor like him of almost a brand new field, in its current form at least, is self-made if they had to synthesize all the information pretty independently to create something new. Even academics from the core DS fields are learning new languages or methodologies they weren't taught in school all on their own, and the degree to which they do this might also depend on the level of their education and not just their discipline. Right now, I might as well be extremely self-made as I'm going through SAS for a Biostatistician role all over again, not having touched it for 5 years. If I'm writing this article, though, you'd be guessing right if I had a socially constructed definition of 'self-made' in mind - those without formal education in the core DS fields (e.g. (Bio)Statistics, Computer Science, Software Engineering and Applied Mathematics), and who are relying largely on bootcamps and/or projects for their own learning and certification.

Self-made Pros:

1) You can synthesize from many different sources. One has greater accuracy and depth, another has greater breadth, another is presented in a way that helps you digest the information better, and yet another has ethical caveats.

2) Learn at your own pace - this is self-explanatory.

3) Your learning might be more applied if you learn from people's projects and learn how they apply methodologies to the real world. You might be able to get away with just the right amount of distillation of technicalities to cut straight to the heart of application that's still technically correct.

4) Learning from doing your own projects shows you can scope out interesting and relevant problems, and is thus a good display of your ability.

Self-made cons:

1) Lack of quality control

This is just one reason but might be the most important one of all. The lack of quality (control) is not intrinsic to the experience of being self-made but just reflects the status quo as of the time of writing. Just as an example, in Siraj Raval's video on linear regression using gradient descent, when asked by someone in the audience if gradient descent was the only optimizer that could be used for linear regression, he pretty much said yes, but I could tell from his microexpressions that he was a bit hesitant. He would've done better to just admit that he didn't know - OLS is actually far more ubiquitously used than gradient descent for linear regression and, as you might've guessed, less computationally burdensome. It's not to say that OLS is necessarily better - it's that saying gradient descent is the only optimizer used is flat-out inaccurate and has some negative repercussions - students won't be able to compare the pros and cons of each in their spare time or even be clued in to the fact that least squares is one of the most important concepts in regression, with all its different varieties being used for a multitude of purposes, e.g. weighted least squares being used when there is unequal variance between groups. This error of Siraj's was near the beginning of Siraj's video and I'm afraid I don't have the time for the altruism of going through the rest of the video, any more of Siraj's videos or many other Data Science pedagogues'. (I've gone through Andrew Ng's Deep Learning course and I'm not pro enough to critique it, but it was good enough for me to spot, years after the course, yet another Siraj mistake of hiding his plagiarism by Google Translating 'gates' to 'doors,' the latter of which I knew I'd never see in real deep learning literature.) However, I don't think I'd need to further illustrate how scary the status quo of quality-unvetted informal courses can be - the famous podcaster Lex Fridman, who has a PhD in AI, actually lent Siraj a lot more credibility than he already had when he interviewed Siraj. If someone who has unquestionable credentials like Lex could've glossed over Siraj's suitability in mass pedagogy, it says a lot about the state of affairs today. If you think I'm just nitpicking on one flaw that Siraj made, I'd recommend that you look up Reddit and other social media threads on Siraj's courses (as well as all his numerous other scandals, which, while not entirely related to this topic, might make you question how intellectually honest someone is when they've displayed deceit (and I don't just mean intellectual posturing) on multiple levels.

On hindsight, after everyone must have, by now, known about Siraj's reputation, it might be helpful to now look back upon his academic credentials and work history. He didn't complete his Bachelor's in Computer Science, and hadn't been working in Data Science for more than 3 years before starting his teaching career. As for having a Computer Science degree, may my anecdote serve as some caution and urge greater probing: after looking at a course titled 'Algorithms' that was singled out on a Computer Science graduate's CV as proof of Data Science legitimacy, I asked, "It seems that there are 2 types of algos people learn in Computer Science - data structure algorithms and ML prediction ones. Unless it's titled 'ML Algorithms,' may I assume the course was just in data structures?" (Data structures, by the way, aren't really used in most ML jobs and are only used at places like Google Brain when you want to completely reinvent the wheel with deep learning architectures.) He nodded, perhaps a little sheepishly but still with the confidence I can only envy of our new grads today. I then asked, "Do you additionally know k-means?" (Yeah, it's a cliche but I picked the one that I thought most CS grads who actually have a modicum of ML knowledge would know.) He shook his head. When we look at a degree like a Computer Science degree and see words like 'algorithms,' we need to really understand what's being taught. Drill down to the atomic level and never let titles, buzzwords whose meanings we often only assume, etc. obscure us to what's really important. However, the sad part is that those who often take bootcamps don't have the ability to vet for these credentials, while those who do don't have the time to vet the quality of these bootcamps or even the instructor's credentials/coursework. Maybe it's not so bleak if you can actually afford higher formal education - as I wrote in another article (link below), almost anyone with a PhD in Computer Science or (Bio)statistics would've covered the main areas of: statistical inference, prediction and computing. Short of that, a Master's is not a bad idea at all. If you're sticking to a Bachelor's, it's very important (just like for everyone else) for you to know what you don't know (perhaps by looking at the curricula of higher degrees and not just social media/the WWW) and very carefully pick out (and probably cram) all the relevant DS coursework into your precious undergrad days.

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2) Related to 1) is how ad-hoc and piecemeal the curricula of bootcamps can be.

I still never understood why Coursera's Data Science course, at least a few years ago, did not include hypothesis testing. It seems pretty arbitrary which topics are chosen and they're probably related to the disciplinary biases of one or two course instructors. One wonders, instead, why people haven't put together a few people from a few of the core Data Science disciplines who've had some work experience to formulate the bootcamp criteria. That aside, perhaps it's simply because of the nature of these courses - they are pretty much nano-courses/nano-degrees and you can't fit everything into one course. The problem with the ability to sample small bits of a wide field like Statistics/ML is not only that you don't get a holistic education compared with through a formal education, but also that you may actually be mistaken as to what the (sub)field is about and either not continue to enrich yourself in that area, or, if you're like any of those many people on social media, run your mouth about this or that (sub)field you've taken only one course in. I think the phenomenon of that person who's taken one Stat 101 course and concluded that Statistics is just p-values and linear regression has become a very familiar trope by now. Their pontifications on LinkedIn and other social media are just as rife. Never mind - it's just social media, yeah? But how do these people learn to collaborate in multidisciplinary teams though when they have such distorted and arrogant views of disciplines their colleagues have actually painstakingly learned in great depth and to the benefit of their team and company too?

Then there is yet another phenomenon of bootcamps, by their typically short nature, prioritizing prediction over statistical inference since it's much faster to teach students how to code and set up a few lines of commands to predict an outcome than to teach code, a few lines of model commands, as well as a huge range of concepts like variable interactions, dealing with multicollinearity, feature selection, variable transformations, etc. all of which are either unnecessary or not absolutely crucial when doing prediction (especially ML ones) models. Thus, students are missing not just a few canonical methodologies but missing entire broad functions like statistical inference. It would be fine if students even knew about statistical inference and remembered its importance but sought their education on it elsewhere, but it seems as though swathes of students don't even know what statistical inference really means or that it even exists. This means that students will be missing out on scoping out the best opportunities for stakeholders and not fulfilling the aims of stakeholders, many of whom really want to know statistical significance and coefficients so they know which input variables to leverage to affect the outcome. Barring industries like computer vision, I'll wager that statistical inference is probably the more common use case than pure prediction without statistical inference, yet our purportedly applied bootcamps seem to give it a complete pass. Statistical inference aside, all statistical inference models can perform prediction besides statistical inference. Hence, when students don't know the range of functions to deal with statistical inference models (typically regressions) like interactions, feature selection, dealing with multicollinearity, variable transformations, etc, they are missing out on the predictive potential of these statistical inference (regression) models. While many have unquestioningly absorbed the idea that ML models tend to be better for prediction, they miss the caveat that that is 1) typically only beyond a huge sample size; 2) when you don't do anything to your regression models. Regarding 2), in other words, regressions are often like diamonds in the rough; if you do nothing to them as opposed to those things I mentioned like forming your own interaction variables, they will probably underperform ML ones, but if you invest the time to deal with them appropriately, they can often be on a par with if not outperform ML models with the additional advantage of providing statistical significance, confidence intervals and coefficients too.

Relying on doing your own projects also runs the risks of sampling from just a few areas of Data Science and this is dangerous if you don't know what you don't know and miss out on learning a wide variety of important skills. Also, you might risk focusing only on areas that greatly interest you as opposed to being forced to expose yourself to a panoply of topics that may or may not interest you.

3) Learn at your own pace

This is a double-edged sword. You may not push yourself to study regularly enough and to complete the course fast enough to achieve your goals such as making enough money to buy a house before prices rise even further.

4) Missing out on some benefits of the formal school environment

When you're completing courses on your armchair, you're not really observed by professors or classmates for teamwork in real life. You may not really get to interact as much with people and with the full range of nuances to show who you really are as a data scientist - your curiosity, what exactly piques your interest, what ethical considerations you have, how you translate your learnings to others, etc. All these are traits that professors and schoolmates write about when they write letters of recommendation or refer you for jobs among their industry connections.