In my experience my professors always referenced Hastie, Tibshirani, Freeman, The Elements of Statistical Learning to be the reference for most of the tasks you would need to perform as a data scientist. For an AI/ML researcher Murphy is probably more comprehensive.
Best Data Science Books According to the Experts
51–60 of 66 posts
Re: Best Data Science Books According to the Experts
#52This is a completely, utterly worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better). The "General interest" books are all horse shit. If you're a pythonista, you should buy Wes McKinney's book. If you're not, you should buy John Mount and Nina Zumel's "Practical data science" which…
This book by Wes McKinney.
https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho...
Re: Best Data Science Books According to the Experts
#53The frequency with which Kevin Murphy's ML book gets left out of these lists in almost bewildering. If I had to choose 1 book as the ML bible, then it would be Murphy's (contrasted against Bishop and ESL) for the following reasons: 1. It uses CS jargon. (Bishop's book while great, uses Math/physics notation/jargon which add a barrier to entry) 2. It is more up to date and comprehensive (It covers everything from prob…
There is a new version coming out next year:
https://mitpress.mit.edu/books/machine-learning-second-editi...
Re: Best Data Science Books According to the Experts
#54The frequency with which Kevin Murphy's ML book gets left out of these lists in almost bewildering. If I had to choose 1 book as the ML bible, then it would be Murphy's (contrasted against Bishop and ESL) for the following reasons: 1. It uses CS jargon. (Bishop's book while great, uses Math/physics notation/jargon which add a barrier to entry) 2. It is more up to date and comprehensive (It covers everything from prob…
There is a new version coming out next year: https://mitpress.mit.edu/books/machine-learning-second-editi...
I really am looking forward to it.
I will probably revisit it entirely after that.
Re: Best Data Science Books According to the Experts
#55This is a completely, utterly worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better). The "General interest" books are all horse shit. If you're a pythonista, you should buy Wes McKinney's book. If you're not, you should buy John Mount and Nina Zumel's "Practical data science" which…
Seconding this comment. Based on experience in hiring data scientists and comparing notes with many others that hire data scientists, the most frequent gaps in knowledge are (1) statistics specifically and scientific computing in general and (2) disciplined software engineering. People good at (1) and bad at (2) write "PhD code" that may or may not be right but you can't tell because it's too disorganized. People goo…
Interesting I've never seen any issue with (1) with data scientists with Masters or PhD, more often I've seen it with software engineers who end up having to do data scientist work. Does that track with your experience?
As an software engineer (2) is the worst part of working with data scientists. The amount of times a 'professional' data scientists want to launch non-code reviewed, non source controlled, works-on-my-notebook model or analysis is shocking.
Re: Best Data Science Books According to the Experts
#56This is a completely, utterly worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better). The "General interest" books are all horse shit. If you're a pythonista, you should buy Wes McKinney's book. If you're not, you should buy John Mount and Nina Zumel's "Practical data science" which…
I personally found the Kleppman book to be great. I wouldn't characterize it as essential for data science per se but anyone designing data-intensive applications ought to give it a read.
Re: Best Data Science Books According to the Experts
#57Earlier quoted context omitted.
Seconding this comment. Based on experience in hiring data scientists and comparing notes with many others that hire data scientists, the most frequent gaps in knowledge are (1) statistics specifically and scientific computing in general and (2) disciplined software engineering. People good at (1) and bad at (2) write "PhD code" that may or may not be right but you can't tell because it's too disorganized. People goo…
"R inferno" will help with the software engineering bits I suppose. R is sort of designed to do this sort of work, and it assumes the end user is more of a statistician than a programmer. Lots of foot-guns. On the other hand, Python has a lot of them as well, and it's NOT designed for this kind of work. It's a sort of mixed bag: R core is vastly better than Python for this sort of task. There's a subset of R packages…
Honest question: what skills should a data scientist possess to graduate out of “shit tier”? Should we have all of the skills of statisticians, ML engineers, data engineers, software engineers, visualization designers, and domain/communication experts? Can it not be valuable to have some but not all of the above skill sets? Does it matter that software engineers are often “shit-tier statisticians” that understand just enough ML lingo to dismiss it as marketing hype?
I’ve gone out of my way over the years to make learning data science skills as approachable as possible for uninitiated (giving trainings, providing customized learning paths based on someone’s background, offering encouragement), and yet this is almost never reciprocated by engineer types. It’s always just, “data scientists can’t write production quality code”, with no explanation of what production quality entail, or without consideration of the fact that notebook-based data science can have advantages over perfectly modularized code with a battery of tests. See the comment above: “I'm not even sure what to recommend for developing good software judgment and habits.“. It’s like a chess coach admonishing their subject to simply “think harder”. Not helpful.
When curious and open-minded data scientists and software engineers work together, it can be magic. When people snipe at others for their “shitty” skills, it creates a petty and toxic environment.
This comment comes off as a bit of an admonition, but I would greatly appreciate a list like TFA for data scientists looking to shore up their fundamental CS and software development skills.
(PS — The first book I read when teaching myself R was R Inferno, so that ain’t it.)
Re: Best Data Science Books According to the Experts
#58Earlier quoted context omitted.
Seconding this comment. Based on experience in hiring data scientists and comparing notes with many others that hire data scientists, the most frequent gaps in knowledge are (1) statistics specifically and scientific computing in general and (2) disciplined software engineering. People good at (1) and bad at (2) write "PhD code" that may or may not be right but you can't tell because it's too disorganized. People goo…
Perspective for (2): it's because no one in graduate training really cares about code quality. Your PI focuses more of your attention on scientific writing, and so there's little to not time to polish your work. The incentives just don't support this work at the graduate training level.
I agree. The only incentive to write neat code is to save yourself the pain and suffering of having to go back through it yourself to fix or add things. I am currently doing data analysis in MATLAB for my PhD, and I know nobody will ever use my code besides me.
I’d like to learn to do my due diligence, but without someone training me, it just takes so much time to learn things like git. I’d rather be recording more data and submitting my paper so I can get the hell out of here
Re: Best Data Science Books According to the Experts
#59This is a completely, utterly worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better). The "General interest" books are all horse shit. If you're a pythonista, you should buy Wes McKinney's book. If you're not, you should buy John Mount and Nina Zumel's "Practical data science" which…
Whenever an article tries to appeal to authority "e.g. according to the experts", you know it is trash. It's part of my personal click-bait detection heuristic.
Re: Best Data Science Books According to the Experts
#60Earlier quoted context omitted.
"R inferno" will help with the software engineering bits I suppose. R is sort of designed to do this sort of work, and it assumes the end user is more of a statistician than a programmer. Lots of foot-guns. On the other hand, Python has a lot of them as well, and it's NOT designed for this kind of work. It's a sort of mixed bag: R core is vastly better than Python for this sort of task. There's a subset of R packages…
Honest question: what skills should a data scientist possess to graduate out of “shit tier”? Should we have all of the skills of statisticians, ML engineers, data engineers, software engineers, visualization designers, and domain/communication experts? Can it not be valuable to have some but not all of the above skill sets? Does it matter that software engineers are often “shit-tier statisticians” that understand jus…
> See the comment above: “I'm not even sure what to recommend for developing good software judgment and habits.“. It’s like a chess coach admonishing their subject to simply “think harder”. Not helpful.
Hey, it seems like you took this as gatekeeping or something. These skills can definitely be taught or self-learned, I've done it and seen it done many times.
My point was only that I don't know resources that can act as a shortcut (my actual word above), i.e. ways to skip over the longer path of gaining experience through long engagement with the topic. So maybe more like a chess coach saying they don't know any books that let a beginner jump ahead to being a more experienced player?
There are hundreds of past threads on HN about books to level up in software, so clearly some people have thoughts about this. I just don't know what to recommend a data scientist who needs these skills immediately.