> This metaphorical braid shows the integration of the foundational fields, labeled S, OR, and C. Figure I.1 Integration of Statistics, Operations Research, and Computing
Brilliant.
The single biggest issue I see working in this area today are two things:
Lack of historical context. People have no clue about the history of quantitative management (rocky, lots of ups and downs)
And sales. Terrible, terrible salesmanship from nerds.
Anyone involved in this field professionally needs to read this book.
Rapidly.
Thanks to the four authors and publisher for making this available as a PDF download.
I am definitely going to give this a read. After decades of more or less using GOFAI, the last eight years has mostly been machine learning and deep learning. Lately I have been scratching an itch to combine old fashioned symbolic AI with more modern deep learning (hybrid systems). I started a new job on Monday where I think this may happen.
From the table of contents, the four examples look appropriate for looking at ML in the context of large real world problems.
This book should be geared for one of those introduction courses, like “Introduction to Engineering”, “AI 101”, etc. I do agree with the vision of the authors that the book is a holistic view of data science, as I personally believe data science is not all about maths, programming; but consider the principles that surround it as a science.
This is also quite practical for large consultancy firms. Most of the chapters, I’ve had clients discuss with me (such as Responsible AI). Personally, I think it could have went away from the applications as it was too high level.
They should change the title. It is not a book for programmers about implementation of data science techniques. It is a book for managers about concerns surrounding the application of data science in various domains. The title should be something like "Social Concerns in the Application of Data Science."
What are other good (maybe intro) books to the Data Science / Engineering Space? I also really like "Designing Data-Intensive applications".
Fantastic book! Very grateful for it to be released publicly and looking forward to reviewing it.
Fan of his original AI book, looks like this one is less technical and more suited for business people.
I admire Peter Norvig's didactic and programming skills; but the poor typesetting makes this book harder to read than need be.
I was excited at clicking the topic - quickly read/skimmed the PDF, and it reads more like an industry analysis than a code/technical book related to the application of data science. Quite disappointed.
I was really looking forward to diving deeper into this, having enjoyed, and learnt, a lot from Peter Norvig's blog and writings in the past. I especially enjoyed the succinctness and density of knowledge in his writing.
Disappointingly, to me, this book seems to lack both of those properties. It seems to meander between talking about core data science concepts, and also about privacy, ethics etc. Given the title of the book perhaps this content makes sense, but it was not useful for me personally. Wish the authors the best.
Quick skim and so far I've learned that the term "black box" model is now part of exclusionary language. It's "opaque box".
Manuscript: https://datascienceincontext.com/manuscript/
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