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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worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and l

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worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better).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 is comparable to Grus. If you don't know Linear Algebra; the "done right" book is absolutely not done right for people that work with data in the real world: Strang is infinitely better, and Trefethan and Bau and Gollub if you need to go deep. As someone pointed out below: Kevin Murphy's ML book is actually very good for teaching you how the things work. What's more, you can download python or Matlab for every algorithm in his book (as you can for Bishop at this point). Nobody needs the Deep Learning books listed. In spite of the hype, it's just not that important compared to naive bayes, LDA and GBM; most people don't have access to the hardware or data sets that make DL useful, and those who do probably studied DL in grad school. Gads; what trash! Those experts: only one of them appears to actually be an expert; pedants and post-docs don't count.

scared2 6y ago
I agree the title is annoyingly misleading.
scared2 6y ago
I have put list per "expert" to make you feel better. Herman: - ISL - Hands on - Chollet - spark+desk Miller: - Grus - thinks stats - la done right - bishop - data intensive
danso 6y ago
Thanks for reaffirming my habit of checking the comments before clicking through. Learned about a lot of useful resources from this and other comments!
ims 6y ago
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 good at (2) but bad at (1) get fine-ish looking numbers out of their good looking code but you can't tell whether it's right because they may have ignored or misunderstood fundamental assumptions and correctness of the underlying methods. There are also seemingly tens of thousands of people on the market who have little experience in either but have adapted projects from examples online into their Github potfolio and put all of the relevant terms into their resume anyway. I think most aspiring data scientists would be better served going with more introductory texts and really understanding them. Maybe Blitzstein and Hwang's "Introduction to Probability" and then McElreath's "Statistical Rethinking" or Wasserman's "All of Statistics" for people who need more stats. I'm not even sure what to recommend for developing good software judgment and habits. There doesn't seem to be a shortcut for that. Maybe "Fluent Python" or "Effective Python" for Python people? No idea for the R ecosystem.
- skwb 6y ago
  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.
  - LetThereBeNick 6y ago
    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
  - scared2 6y ago
    Actually the pi doesn't even know about the concept of code quality. Or even your code at all.
- scottlocklin 6y ago
  "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 which are as good as scikit learn (which is very good indeed), but there is also a pile of total shit. R's package manager is also better than anything in the python universe, but node bros manage to screw it up. I loathe python from long experience, so I polarized on R, but python is definitely winning. I just assume anyone who calls themselves a data scientist is going to be a shit tier programmer who needs to improve over time at this point. The exceptions kind of prove the rule. Imposing test-driven discipline will cure some of the worst tendencies. I don't have good references on stats and linear regression tier data science, but I'll take someone who understand the basics (I dunno, calculating useful moments from empirical distributions, feature selection in linreg) over some weenie who has some cribbed ipython file in his githubs who claims to understand Hastie.
melling 6y ago
This book by Wes McKinney. https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-ebook/dp/B075X4LT6K https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho...
gxqoz 6y ago
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.
umeshunni 6y ago
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.

scared2 6y ago

I agree the title is annoyingly misleading.

scared2 6y ago

I have put list per "expert" to make you feel better. Herman: - ISL - Hands on - Chollet - spark+desk Miller: - Grus - thinks stats - la done right - bishop - data intensive

danso 6y ago

Thanks for reaffirming my habit of checking the comments before clicking through. Learned about a lot of useful resources from this and other comments!

ims 6y ago

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 good at (2) but bad at (1) get fine-ish looking numbers out of their good looking code but you can't tell whether it's right because they may have ignored or misunderstood fundamental assumptions and correctness of the underlying methods. There are also seemingly tens of thousands of people on the market who have little experience in either but have adapted projects from examples online into their Github potfolio and put all of the relevant terms into their resume anyway. I think most aspiring data scientists would be better served going with more introductory texts and really understanding them. Maybe Blitzstein and Hwang's "Introduction to Probability" and then McElreath's "Statistical Rethinking" or Wasserman's "All of Statistics" for people who need more stats. I'm not even sure what to recommend for developing good software judgment and habits. There doesn't seem to be a shortcut for that. Maybe "Fluent Python" or "Effective Python" for Python people? No idea for the R ecosystem.

skwb 6y ago
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.
- LetThereBeNick 6y ago
  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
- scared2 6y ago
  Actually the pi doesn't even know about the concept of code quality. Or even your code at all.
scottlocklin 6y ago
"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 which are as good as scikit learn (which is very good indeed), but there is also a pile of total shit. R's package manager is also better than anything in the python universe, but node bros manage to screw it up. I loathe python from long experience, so I polarized on R, but python is definitely winning. I just assume anyone who calls themselves a data scientist is going to be a shit tier programmer who needs to improve over time at this point. The exceptions kind of prove the rule. Imposing test-driven discipline will cure some of the worst tendencies. I don't have good references on stats and linear regression tier data science, but I'll take someone who understand the basics (I dunno, calculating useful moments from empirical distributions, feature selection in linreg) over some weenie who has some cribbed ipython file in his githubs who claims to understand Hastie.

melling 6y ago

This book by Wes McKinney. https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython-ebook/dp/B075X4LT6K https://www.amazon.com/Python-Data-Analysis-Wrangling-IPytho...

gxqoz 6y ago

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.

umeshunni 6y ago

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.