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Best Data Science Books According to the Experts

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Re: Best Data Science Books According to the Experts

#61

Post by jointpdf »

ims wrote: post #60
jointpdf wrote: post #57

Earlier quoted context omitted.

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…

What you said wasn’t egregious or anything, no worries. I’ve seen some incomprehensible code from data scientists with PhDs, stuff that has no excuse. I also know of one single resource for essential coding skills specific to data scientists either.

Sometimes a rant on a topic brews in my head for weeks or months, and I will uncork it on a random passerby that brings up the subject—which happened to be you this time.

But, I’ve had coworkers who like clockwork sneer at anything a data scientist wrote. “Why did you do it that way?”. When asked for advice on how to improve it, they huffily say nevermind. It’s ingratiating as hell.

Re: Best Data Science Books According to the Experts

#62

Post by auraham »

alanfranz wrote: post #18
auraham wrote: post #5

I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended. Introduction to Statistical Learning is also available for free online: http://faculty.marshall.usc.edu/gareth-james/ISL/ Although I only read a few chapters from that book, I really like it (but I would have preferred a python version of the book). Personally, if you have…

You can find a couple of repos (google them) that show the exercises in Python, I had written a post on my own blog some time ago: https://www.franzoni.eu/machine-learning-a-sound-primer/

Good to know, thanks for the link!

Re: Best Data Science Books According to the Experts

#63

Post by auraham »

yboris wrote: post #38
auraham wrote: post #5

I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended. Introduction to Statistical Learning is also available for free online: http://faculty.marshall.usc.edu/gareth-james/ISL/ Although I only read a few chapters from that book, I really like it (but I would have preferred a python version of the book). Personally, if you have…

I wholeheartedly second "Deep Learning with Python" by François Chollet! It's an excellent 'zero-to-hero' text for understanding deep neural networks, some common architectures, and the code (and theory) to get them to work. One thing missing is how to prepare data for deep learning -- but that's just standard ETL you learn elsewhere.

You can check Géron's book to know more about data preparation, specifically the second chapter. This chapter details an end-to-end machine learning project (price prediction). Here, the author describes scikit learn's pipelines for automating preprocessing tasks for your dataset.

Re: Best Data Science Books According to the Experts

#64

Post by scared2 »

skwb wrote: post #47
ims wrote: post #46

Earlier 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.

Actually the pi doesn't even know about the concept of code quality. Or even your code at all.

Re: Best Data Science Books According to the Experts

#65

Post by scared2 »

scottlocklin wrote: post #50
ims wrote: post #46

Earlier 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…

R should be killed.

Re: Best Data Science Books According to the Experts

#66

Post by Eugeleo »

ims wrote: post #46
scottlocklin wrote: post #36

This 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…

Which books would you recommend to solve the first problem? I’m a CS student, but would love to work with data one day, so I’m looking for some probability/statistics/data science books to read through. I’ve heard great things about Elements of Statisticsl Learning, for example, but I’m not sure whether it’s not too DS oriented, giving poor foundations in statistics and probability.
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