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blahi

That seems more about computer science and graphs than what the average analyst would be doing.

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Table of Contents:

* High Dimensional Space

* Best Fit Subspace & SVD

* Random Walks & Markov Chains

* Machine Learning

* Massive Data: Streaming, Sketching, Sampling

* Clustering

* Topic Models, Hidden Markov Process, Graphical Models, and Belief Propagation

How many dimensions would you prefer your hardcopy to span?

I'm new to the field (which his why the book interests me), so I have to ask: Is your TOC a confirmation or a contradiction to the parent comment?

The table of contents is pretty much the core topics of serious data science (as opposed to "learn to use data science libraries in $lang in 21 days")

So yes, this book that is avaialble for download from a TURING Award winner's web page IS a Computer Science book for Data Science.

Avrim Blum is not a Turing award winner; his father Manuel is.

this book was posted on John E. Hopcroft's webpage - https://www.cs.cornell.edu/jeh/ who also has won a Turing award.

A contradiction - data science is the place where statistics and computer science meet but this book definitely isn't characterised as a book about computer science and graphs. From the TOCs, it is heavily based in statistics.

Data Scientist here. I use these, often.

hi, sorry for responding in this thread but I saw your profile and was wondering if you had contact info I could reach you at? would be really interested in learning more about your work

See my profile.

Edit: I'm not sure why email is not showing up. Here is an old site with contact info: https://sites.google.com/site/thomasroderick/

There is a difference between Data Scientist, Data Engineer, and Data Analyst.

"Data Scientist" is for Ph.D.s or sat least Masters, those are the less common jobs.

At least that's the theory, we all know what happens with titles... anyway, the point is that we can safely assume that this particular article "Foundations of Data Science" refer to the actual "scientist" role.

https://www.datacamp.com/community/tutorials/data-science-in...

https://bigdatauniversity.com/blog/data-scientist-vs-data-en...

That would be a very useful separation. Unfortunately, job listings and titles used in industry do not separate themselves nearly so cleanly as that. I managed a "data science" team for a few years; nearly all our work is analysis. We still would call ourselves "data scientists" because it elevates our prestige within the company, which makes our results more respected and listened-to. It's a political thing, and it also helps in hiring.

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