Manuscript: https://datascienceincontext.com/manuscript/

Download: https://datascienceincontext.com/wp-content/uploads/2022/05/...

> 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.

Are you sure? You sound like someone who’s interested in actually doing things; the book looks like endless rambling about high level ethical/conceptual/societal context for data science. It looks about as interesting as picking up a random volume of a random social sciences journal. I think Peter Norvig’s python code is beautiful and amazing though, and probably same for most things he’s done.

The code is the least important part of data science.

I didn't mean to suggest otherwise.

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."

It's a book for data scientists.

Data scientists are not software engineers.

They analyze data, they don't produce applications.

Not everything in the world is written for the benefit of software engineers.

I agree with you - mostly.

But in the book, computation is one of the three braids comprising data science. And they include software engineering in computation.

See book sections 1.2, 1.2.4, 2.1.3 etc

—

What I am trying to untie is, should data engineering be distinguished from data science, or not?

Previous poster didn't mention software engineers.

programmers != software engineers?

Yea I thought that was common knowledge.

If you're privy to the top-secret distinction between the two that nobody else knows, please share it.

same shit

While data science is not principally about programming, I think that having Norvig as one of the authors will lead to false expectations of the book unless the title is made more informative.

Social concerns are as a concern for engineers, as they are for their managers. However, I agree on changing the title somewhat, in order to indicate, that this is not a book about computer programming.

I think a more philosophical text by Norvig is quite welcome.

This is better than yet another machine learning algorithm book.

I didn't say there was anything wrong with the book, just the title.

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.

Funny, I was going to say it seems similar to that one - dense with words but light on technical theory (I was tempted to say 'content' but I'm sure that's unduly harsh, as you say this will suit some). Maybe I didn't spend enough time with it, my recollection is just of it waffling on and on about agents.

I admire Peter Norvig's didactic and programming skills; but the poor typesetting makes this book harder to read than need be.

I assume part of this is because the manuscript hasn't been properly typeset for publishing yet.

Typesetting seems fine to me? What's the issue? Wouldn't mind the lines being shorter but it's to be expected from a textbook.

you could try copy paste the pdf on obsidian, there was an extension/plugin that turned pdf to markdown.

I'm 99% sure this was authored in Google Docs.

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".

I can't see where they criticize the term "black box". So, ironically, this is just a complaint that they used some terminology you don't approve of.

It appears they are just using an alternative phrasing without making comment, but if they were actually making a comment then wouldn’t the overall objection be “not to read too much into the wording”, but that is a catch 22. Objecting to someone taking wording too seriously to the point of changing the wording is in itself taking the wording too seriously.

Because the opposite of black box is clear box so it makes sense to use the opposite of clear which is opaque.

> Because the opposite of black box is clear box

Not a native speaker (never heard "clear box" as terminus technicus), but a mathematician: If it is "the (only) opposite", the relation ist symmetric. So the opposite of "clear box" is "black box" again. Skipping the injective part, "black box" would be still one possible opposite.

it's also known as open box or glass box and very rarely white box.

> very rarely white box.

I would not consider the attribute "rare" to be appropriate here.

For example: I regularly deal with adversarial networks, and both black- and white-box attacks are quite common there.

It has nothing to do with racism, but good on you for bringing racism into the argument though.

Black box vs clear/open box are very well known terms. Some people use the term opaque box instead because it a more clear antonym to clear/open box.

> It has nothing to do with racism, but good on you for bringing racism into the argument though.

Nonsense. This is all part of the "oppressive language" concept peddled by the neo-Marxist authoritarian types.

> Some people use the term opaque box instead because it a more clear antonym to clear/open box.

This is the first time in my life that I head the term "opaque box" used instead of "black box" in this context. I couldn't even find this phrase using a Google search.