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Comment by mlevental | Hacker News Reader

there's a salty purist like you listing buzzwords from casella and berger in every thread. your brethren in the data structures threads are those repping CLRS or taocp. and yet data science as an industry marches on (not to say anything of dev).

>prove, and improve on existing techniques

how many "data scientists" in industry are proving or improving existing techniques in their day to day?

>Like how a mechanic doesn't need to know any physics

this is absolutely correct since, you know, they're able to provide value for customers by fixing their cars

i would say that this is exactly what elitism looks like: you take a look at someone that's successful but under different terms than you yourself see fit, and claim it's not genuine success.

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>there's a salty purist like you listing buzzwords from casella and berger in every threa

That cracked me up lol. I was hired as a data science at big tech with only some high level data science skills. I first learned to write productuon quality code. I then (and now) am working through Casella Berger, I'm 1/3 through. It's hard! I've had to stop and brush up on math a lot. I love learning theoretical stats, I feel it makes me a better data scientist. But I also know I've worked with phd data scienctists who worked through all of C&B but couldn't code, and they weren't as valuable as the more self-taught code-first data scientists (at least on my team).

I had never heard of Casella & Berger (but I'm the salty guy on the data structures threads going on about how great TAOCP is). I looked it up on Amazon and it looks interesting and like exactly what I've been looking for, but there's no "look inside this book" or even a table of contents on there. Before I pick it up, how much math background would I probably need to be able to penetrate it? I'm up on calculus and linear algebra, but I only took one statistics course as an undergrad 25 years ago.

there's a pdf floating around - for me it's the first hit when i search casella berger on google

>I'm up on calculus and linear algebra, but I only took one statistics course as an undergrad 25 years ago.

it is a serious graduate math stats book but i personally don't consider it a graduate math book (because it does not use measure theory). it is proof focused because it's used for foundational grad stats classes (i.e. prep for quals) but the proofs aren't that hard to follow (if you've had a good calculus class [epsilon-delta definition of limits]).

some of the exercises are brutal algebraically though. good things there's a solutions manual floating around :)

> some of the exercises are brutal algebraically though

This is definitely true, and I wouldn't recommend this book to anyone unless they considered this a feature. For me, it was/is a feature, because I suck at algebra, and this is helpful.

There's a huge difference. If I ask "why is this model applicable to this situation?" or even "is this model applicable to this situation?" then I want the practitioner to have the ability to provide and understand the answer. Yes, data science as an industry, but that's because wrong answers are largely undetectable.

If I roll out the treatment cell of an experiment but shouldn't have, because I didn't account for within-client correlation, the world marches on and the business thinks they are making data driven decisions. They'll never know they left millions on the table. Or maybe I'm right for the wrong reasons and I was merely more confident than I should have been and the world still marches on oblivious because there are very very few correction mechanisms.

The academic math types with no ML experience don't fare much better than the no-math practitioners with experience from what I've seen. They generally have terrible mental models of how the math behaves when applied to real data.

IME the competent data scientist needs to have a high-level understanding of the concepts underlying ML (stats, optimization, central limit theorem) and to be comfortable reading math to understand the low-level details when they are relevant. In addition to actually understanding the ML techniques they're using obviously.

On top of that, I would rather work with someone who can code well than a pure mathematician with a ton of experience proving theorems (and no coding ability). Outside of a few industry research jobs, the cost:benefit ratio of making substantial theoretical breakthroughs in most everyday ML work doesn't justify a lot of time doing theoretical proofs.

The person you're replying to isn't talking about "theoretical breakthroughs", he's talking about a basic ability to interpret and detect errors in your model.

These are abilities that "data scientists" do, indeed, lack.

My impression of working closely with data-scientists is that what you describe comes more from experience, than from purely theoretical knowledge of the math behind. Ideally it's both, but if having to choose, I'd rather bet on someone who did it already a dozen times and knows what to expect.

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