Best Data Science Books According to the Experts
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worthless list: the only things that belong on it are Grus (good for python), maybe Bishop (maybe; it's woefully out of date and light on details) and Hastie (his other book is vastly better).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.<p>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.<p>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).<p>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.<p>Gads; what trash! Those experts: only one of them appears to actually be an expert; pedants and post-docs don't count.