Announcement video: https://www.youtube.com/watch?v=oEXR9EnAtm4

Code can be found at: https://github.com/dynamicslab/

More info / video links /etc (from 1e):

http://databookuw.com/

Very good youtube channel, if anyone is wondering

Steve Brunton has an awesome YouTube channel as well. For those in the data science / machine learning space, it's very interesting to see those techniques applied to control and other areas of engineering - things they never taught us in engineering school!

The co-author Nathan Kutz is a great instructor too. I recall going through his optimization course on Coursera back when MOOCs were new; instructors excited about the course material make learning fun.

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

See 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-rFp...

[2] https://avehtari.github.io/ROS-Examples

[3] http://www.stat.columbia.edu/~gelman/book

Ah, 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?

You could try this one: https://hastie.su.domains/CASI_files/PDF/casi.pdf

However, IMHO many things come more naturally in a Bayesian setting, such as hierarchical models.

Cosma Shalizi's "Advanced Data Analysis from an Elementary Point of View" sounds right up your alley: https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/

I used it to learn depth on exactly those topics myself. It's just great.

Thank you. Will check it out.

Bishop 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.

I 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).

Possibly "Learning from Data"[1][2] would be of interest?

[1]: https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[2]: https://amlbook.com/

ESLR then Murphy ML: A PP

ESL 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.

Highly recommended book, they also have a paper copy if you want to support their work. Very pleasing to see other people noticed they updated the text.

https://www.amazon.com/Data-Driven-Science-Engineering-Learn...

Have a wonderful day =)

A bit confused with the "announcement", this book has been out for a while. I have the paper copy from almost 8 months ago. Did something change? In any case, as others have noted very good book.

I don't know, I just saw this hit my YT recommendations a few hours ago. I had no idea there was a 2E out. But I'm glad this did show up when it did, as I had the 1E parked in my cart at Amazon and was close to ordering a copy. Now I'll make sure to get the newer version.

Canonical UW URLs use the uw.edu domain: https://faculty.uw.edu/sbrunton/DataBookV2.pdf (huge 100MB PDF, as others have noted).

(Washington got pretty lucky to be granted this domain and universities are no longer being granted contractions like this. Wisconsin and Waterloo might be somewhat jealous.)

Only Wisconsin. Canadian universities usually have .ca rather than .edu

FYI, huge PDF. $ ls -sh DataBookV2.pdf 103M DataBookV2.pdf