Don't you find it odd that education start with the abstract to have you end up in reality.

Data science is not about well structured data, it's not about learning what others do. I

It's about finding meaningful information through experimentation with data and for that all you really need is a lot of experimentation, a creative mindset and the ability to extrapolate meaning and correlation out of seemingly uncorrelated data.

First experiment, learn to play around, explore, then apply theory.

Actually, I don't think the process of going from abstract to reality is that odd. The reason I say that is because I think understanding the abstract prepares you to be flexible. In terms of data science, the way I see this is that by experimenting without having a grounding in the algorithms, technologies etc. means that you can only really ever get good at the specific implementations you're working with. The abstract understanding is precisely what allows you to be versatile in adopting new technologies and seeing the implementation for what it is: one concrete version of the underlying abstract ideas.

There's nothing that says that formal education only prepares you to work with well-structured data. In fact, I think that in grad school you are challenged to work at the cutting edge almost all the time. Granted, a lot of classroom examples at the undergrad level are perhaps trivial, but my experience is that grad school doesn't by definition only serve to prep you with pointless examples.

> It's about finding meaningful information through experimentation with data and for that all you really need is a lot of experimentation, a creative mindset and the ability to extrapolate meaning and correlation out of seemingly uncorrelated data.

I totally agree, and I believe that school is not at odds with this. I'm working in the fields of planetary science and space engineering at the moment, and I am constantly stimulated to experiment with data, be creative and understand correlations.

Maybe for some reason my experience is just not a good 3-sigma example ...

We don't start being philosophers, we don't start with music theory to learn music.

We start as infants and learn to crawl, gobble, walk, talk, read, write, calculate through our interaction with the world, it informs us.

The danger of starting with the abstract is that you make everyone generic and thus give them the same same frame of reference to think within.

I am totally on-board with what you're saying, I guess I just don't buy into the idea that school != connected to the real world. I think the idea that everyone in academia is being philosophical and scholarly without contact with the real world is just wrong. There are plenty of practical skills that you can learn through school. Given the general anti-school sentiment I tend to come across in HN discussions, it might just be that I'm the odd one out when it comes to thinking that going to (grad) school is not at odds with gaining exposure to working with real world data.

It's not that school isn't connected to the real wold. It's that you don't need it besides a very few areas.

And if that is the case why put yourself to debt to go to school when you don't have to? Why pay a lot of money up front to acquire a knowledge you can get for free. You will have to learn the rest of your life anyway without having to go to school.

You have the worlds knowledge at your fingertips. You have any expert in any field you can ask about anything you want today for more or less free.

I would even claim advicing against school in most cases is a moral just cause.

I guess the debt argument is one that I don't have direct experience with, since I'm going to school in Europe, where the norm is not that you come out of school saddled with debt that you'll be paying off for the rest of your life. So in that sense, I guess I get your argument for the "moral cause", but I think that's more a problem specific to the US and the administration of colleges as opposed to the problem with academia as a whole.

It's true that you don't need school for a lot of things in life, but that's not the same as saying there's no value to going to school.

Again, what irked me was simply the statement in the blog that other than the odd stats class, there's no value in going to school if you're an aspiring data scientist.

I concede that there's a large difference between the added value of undergrad programs and grad school. I strongly disagree that you can substitute what grad school gives you by simply Googling it or by taking a few online classes. In particular, mastering your own research project I think gives you a lot of real-world skills and that includes working with real, unstructured data sets.

I am also from Europe and went to European school.

What exactly is the school giving you that you can't learn on your own, by experimenting, reading, learning, studying. You can take the same courses and so on.

You seem to assume that the school somehow has something unique that cant be replicated outside of it.

Why can't you master your own research projects? Why wouldn't you have access to real, unstructured data outside school?

Clearly the author managed just fine. I myself have managed doing quite well in another field without any formal training via a school.

I'm not saying you can't do it on your own. I do believe that there are multiple ways to skin a cat, but what I don't agree with is that opting to pick the route of going through school is not going to prep you to work with real-world data. You can argue that formal training is not necessary, but I fail to see how that means that there's no value in what's taught in school.

The very fact of the matter is that through my research I have gained exposure to working with real, unstructured data, and I have had the opportunity to develop skills that I believe stand me in good stead to pursue a career in industry as a data scientist.

So, basically I guess I'm reversing the argument and saying that as much as you can acquire any skills without going to school (and that includes primary and secondary school), there's nothing in my experience that points to the fact that picking the route of formal training is going to place you behind the 8-ball as an aspiring data scientist.

The author has managed fine indeed. My contention is with the following quote from the GigaOm post:

> "“I think the applied experience is a lot more important than the academic experience. It probably can’t hurt to take a stats class in college.”

I don't see applied experience and academic experience as being mutually exclusive concepts, so to make the statement that one is more important than the other is to fail to understand that as much as you can learn things on your own, school can also provide you with hands-on skils to become a data scientist.

They are not mutually exclusive, but the problem is this:

If you start with academic experience you your frame of reference is already established and its much harder to break out of that.

Sure you can get a job, but you are not more likely to be influential in your field just because you have an education.

Your first statement I don't agree with -> I don't see how you can't break out of your frame of reference. If your frame of reference is working with data and building skills that are equally applicable in a business environment, then I think you can make the transition without major hiccups. That's the belief of the people running the Insight Fellowship as well, which has had great success in providing a channel to bring people with an academic background into industry.

I absolutely agree, formal education doesn't not imply greater influence nor greater success, however, neither does it imply that you can't reach the highest heights. So there's a fallacy in the thought process that if you want to be a data scientist, school is not the a possible option. I think there are people who can be successful in the data science business who pick it up by themselves, with all the free material out there, and equally I think you can approach the field with formal training and make a name for yourself.

Anyway, by the sounds of it, we're probably reaching the point of agreeing to disagree.

(I'll admit to skimming up front)

I didn't get the impression that he was suggesting people interested in Data Science should forego theory and lecture altogether.

His argument for building applied skills first, was that it would motivate academic pursuit later (as well as being immediately applicable). If you're already reading for your Ph.D. we might assume you're not at a loss for motivation :-)

(okay now I went back and reread)

I also get the impression he's talking to an application-focused audience. 16 year olds looking at colleges, or 20 year olds considering a focus for a masters, are probably not the target audience of the Harvard Business Review.

Yea, I appreciate the fact that he isn't suggesting that people abandon school althogether. I think what irked me is his suggestion that school is maybe useful for the "odd stats class", suggesting to me that he doesn't believe that there is value beyond that.

Perhaps my reading of the blog post isn't entirely correct. I agree that he's not making any statement directly to dissuade people from going to school. I just think that it's important to note that my experience of being in school is that it has given me freedom to be creative, experiment, play around with new technologies, and ultimately build applied skills. Perhaps I'm in the minority that feels that way ...

EDIT: After reading the full blog post, I can see that his view is more nuanced than comes across on GigaOm.