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I am looking for a book and resource for (advanced) data science. I understand there are numerous ML books and articles discussing algorithms at all levels; th
by quanto 3y ago
I am looking for a book and resource for (advanced) data science. I understand there are numerous ML books and articles discussing algorithms at all levels; there are also numerous introductory books on data science, e.g. how to call functions in sklearn. What I am looking for is a book on data exploration, data modeling, and prediction error analysis; with some level of rigor in both theory and implementation.
Any recommendation for such a book or resource?
DatabookV2 discussed here looks great for natural science + ML. It reminds me of this widely-cited paper
A high-bias, low-variance introduction to Machine Learning for physicists
https://arxiv.org/abs/1803.08823 https://arxiv.org/abs/1803.08823
- nextos 3y agoSee this MLSS '20 presentation and all recommended bibliography [1], in particular top left box in slide 123. For rigorous modeling, Gelman et al. books are great [2-3]. [1] https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFpobXeJ/view https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp... [2] https://avehtari.github.io/ROS-Examples https://avehtari.github.io/ROS-Examples [3] http://www.stat.columbia.edu/~gelman/book http://www.stat.columbia.edu/~gelman/book
- quanto 3y agoAh, thank you. I did hear about Gelman's books before, but I haven't read them in detail. Will check those out. Do you have references/books for non-Bayesian approach as well?
- nextos 3y agoYou could try this one: https://hastie.su.domains/CASI_files/PDF/casi.pdf https://hastie.su.domains/CASI_files/PDF/casi.pdf However, IMHO many things come more naturally in a Bayesian setting, such as hierarchical models.
- petulla 3y agoESLR then Murphy ML: A PP
- quanto 3y agoESL is indeed widely recommended for ML but perhaps, I am looking for something more data-exploration focused. In any case, did you find Kevin Murphy's MLPP to be helpful? I read the book in detail and found the book as an introductory mathematical book for discussing ML algorithms, rather than doing any real world data-modeling or prediction error analysis -- it may help with ML, but not sure if it will help me with DS. And the book (at least the edition I used) was full of non-trivial errors.
- segmondy 3y agohttps://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf https://hastie.su.domains/ElemStatLearn/printings/ESLII_prin...
- ericdfoley 3y agoBishop Pattern Recognition and Machine Learning is an awesome starting point. I think it's way more coherent (although it probably is less comprehensive) than Elements of Statistical Learning.
- quanto 3y agoI do like both of the books; but I found them more focused on mathematical analysis of algorithms rather than analysis of the real world noisy data (and choosing/making which algorithm to use).
- spenczar5 3y agoCosma Shalizi's "Advanced Data Analysis from an Elementary Point of View" sounds right up your alley: https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ I used it to learn depth on exactly those topics myself. It's just great.
- quanto 3y agoThank you. Will check it out.
- mindcrime 3y agoPossibly "Learning from Data"[1][2] would be of interest? [1]: https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069 https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/... [2]: https://amlbook.com/ https://amlbook.com/