Back in 2011, lukeprog posted a textbook recommendation thread. It's a nice thread, but not every topic has a textbook recommendation. What are some other heuristics for selecting textbooks besides looking in that thread?
Amazon star rating is the obvious heuristic, but it occurred to me that Amazon sales rank might actually be more valuable: It's an indicator that profs are selecting the textbook for their classes. And it's an indicator that the textbook has achieved mindshare, meaning you're more likely to learn the same terminology that others use. (But there are also disadvantages of having the same set of mental models that everyone else is using.) BTW, my dad claims Goodreads star ratings can have a more informative spread than Amazon ones.
Somewhere I read that Elements of Statistical Learning was becoming the standard machine learning text partially because it's available for free online. That creates a wrinkle in the sales rank heuristic, because people are less likely to buy a book if they can get it online for free. (Though Elements of Statistical Learning appears to be a #1 bestseller on Amazon, in bioinformatics.)
Another heuristic is to read the biographies of the textbook authors and figure out who has the most credible claim to expertise, or who seems to be the most rigorous thinker (e.g. How Brands Grow is much more data-driven than a typical marketing book). Or try to figure out what text the most expert professors are choosing for their classes. (Oftentimes you can find the syllabi of their classes online. I guess the naive path would probably look something like: go to US News to see what the top ranked universities are for the subject you're interested in. Look at the university's course catalog until you find the course that covers the topic you want to learn. Do site:youruniversity.edu course_id on Google in order to find the syllabus for the most recent time that course was taught.)
Reposting my comment from lukeprog's thread with slight edits.
It would be useful for me if some of you guys shared your methodology of choosing textbook / course / whatever for learning X, especially if X has something to do with math, computer science or programming.
My methodology (especially for math, computer science and programming) (in no particular order):
I know Russian and English but I prefer English textbooks, because there are more textbooks in English produced. If their usefulness for me is a gaussian distribution, then top textbooks in English are probably better, than top textbooks in Russian.
Go to libgen, search for the keyword and sort by the publisher or by year. Identify potentially good publishers, e.g. top universities of the world or top universities in that field, or Springer
Check rating on goodreads and/or on amazon
Check top comments by usefulness on goodreads and/or amazon
Download the book, look at the Contents section, see how useful they seem to me
Google best textbook on ${subject name}, ${book title 1} vs ${book title 2}. Pay special attention to results on stackexchange. Do the same google search with site:reddit.com.
New ideas which I haven't tried yet:
Look for a subreddit or freenode chat on the topic I am interested in. See if there's a recommended reading list pinned there.
Back in 2011, lukeprog posted a textbook recommendation thread. It's a nice thread, but not every topic has a textbook recommendation. What are some other heuristics for selecting textbooks besides looking in that thread?
Amazon star rating is the obvious heuristic, but it occurred to me that Amazon sales rank might actually be more valuable: It's an indicator that profs are selecting the textbook for their classes. And it's an indicator that the textbook has achieved mindshare, meaning you're more likely to learn the same terminology that others use. (But there are also disadvantages of having the same set of mental models that everyone else is using.) BTW, my dad claims Goodreads star ratings can have a more informative spread than Amazon ones.
Somewhere I read that Elements of Statistical Learning was becoming the standard machine learning text partially because it's available for free online. That creates a wrinkle in the sales rank heuristic, because people are less likely to buy a book if they can get it online for free. (Though Elements of Statistical Learning appears to be a #1 bestseller on Amazon, in bioinformatics.)
Another heuristic is to read the biographies of the textbook authors and figure out who has the most credible claim to expertise, or who seems to be the most rigorous thinker (e.g. How Brands Grow is much more data-driven than a typical marketing book). Or try to figure out what text the most expert professors are choosing for their classes. (Oftentimes you can find the syllabi of their classes online. I guess the naive path would probably look something like: go to US News to see what the top ranked universities are for the subject you're interested in. Look at the university's course catalog until you find the course that covers the topic you want to learn. Do site:youruniversity.edu course_id on Google in order to find the syllabus for the most recent time that course was taught.)