Here is my review for, Weapons of Math Destruction: https://www.audiobookreviews.com/single_post.php?id=634
One of the absolute WORST books I have ever read.
Best Data Science Books According to the Experts
31–40 of 66 posts
Re: Best Data Science Books According to the Experts
#32My favorite is "Learning from Data". Builds from first principles and simple algorithms, so I really understand what makes machine learning work, and how to debug as well as build my own techniques vs use off the shelf black boxes.
Re: Best Data Science Books According to the Experts
#33As a dumb marketer. I very much enjoyed John Foreman's Data Smart. https://www.goodreads.com/book/show/17682206-data-smart
I skimmed the book a few days ago and thought it looked interesting, and put it in my reading list.
Got any other companion book recos?
I'm particularly interested in data sets that I could play with in Tableau, Google Data Studio, and Excel.
Re: Best Data Science Books According to the Experts
#34The 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…
The book in question is Kevin Murphy, machine learning from a probabilistic perspective [1]
Just googling kevin Murphy ML gives me a lot of ads and pages about hair product.
Re: Best Data Science Books According to the Experts
#35The 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…
Personally I had a really poor experience with Murphy's book because I bought one of the first editions (don't recall whether it was first or second). The list of known errors is really long [1], and the author didn't even bother to organize it properly. The author even decided to rewrite a chapter because it contained too many errors [2].
1. https://www.cs.ubc.ca/~murphyk/MLbook/errata.html
2. https://www.cs.ubc.ca/~murphyk/MLbook/pml-print3-ch19.pdf
Re: Best Data Science Books According to the Experts
#36This 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.
Re: Best Data Science Books According to the Experts
#37Deep Learning with Python (Chollet) and Hands-On Machine Learning (Géron) may be a bit redundant currently. With 2.x versions of Tensorflow, Keras (which is what Deep Learning with Python covers) has been completely integrated into into the Tensorflow API (which is covered in Hands-On Machine Learning). Both books are good, but the newest edition of Hands-On Machine Learning is updated for Tensorflow 2.0, and so it is probably the more relevant of the two.
Re: Best Data Science Books According to the Experts
#38I 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.
Re: Best Data Science Books According to the Experts
#39The article notes that "Designing Data-Intensive Applications" is perhaps not a typical data science book, but it is still very useful. I agree. It is a fantastic book - one of the best technical books I have read. I wrote why, and a summary of it here: https://henrikwarne.com/2019/07/27/book-review-designing-dat...
There's a very real chance that it's mentioned in the article purely because it has the word "data" in the title.
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Re: Best Data Science Books According to the Experts
#40More free books: * Vectors, Matrices, and Least Squares — IMO the best beginner-friendly and applications-focused intro to (or review of) linear algebra. Covers a ton of fundamental ground while keeping things consistent and concise. Lots of exercises and a Julia supplement book. ( http://vmls-book.stanford.edu/ ) * Mathematics for Machine Learning — good coverage of the most important math concepts relevant to ML (…
Have you already read them all?
I’ve read all of VMLS and Forecasting (and ISLR from the original list), and maybe half of SLP. MML I have skimmed through for review / used as a reference, and the deep learning book is high on my queue.
I tend to not be a cover-to-cover reader, so I usually deep dive into a single topic for a while (e.g. forecasting, information retrieval) and read papers/tutorials/chapters related to that topic and the math concepts related to it.
PS I feel like impostor syndrome is so common among data scientists because there is so much material that feels like “must know”. Don’t feel like you need to memorize thousands of textbook pages to be effective, and you could spend a lifetime mastering any of these individual subjects. JIT learning is a great skill to have.