Steven Skiena

A good introductory book to statistical analysis data mining data science. This is clearly aimed at students - the Coda at its conclusion exhorts the reader to now get a data science job (no thanks, got a real job already), and there is an expectation in the word-frequency discussion that the reader has never encountered the word defenestrate (ha! just last week I had to defenstrate an intruder!).

It's always good to get Skiena's take on things -- I've read three or four of his books now -- and this one is no exception. The statistical-learner stuff is linked more closely to standard CS topics (e.g. algorithmic complexity) than in most other texts, and the overview of linear algebra is really quite good.

The only real downside is that it doesn't do what is says on the tin. Unlike The Algorithm Design Manual, this isn't presented as a taxonomy of data science methods with a briefing of when and how each should be supplied. More's the pity, as that particular book is sorely needed - even in this one, Skiena points out that most researchers become comfortable with one approach and use it for everything, rather than testing alternate approaches on new problems.

Instead, it's a standard Introduction to Data Science textbook with chapters devoted to topics of increasing complexity/sophistication. Well-written, often entertaining, with an excellent selection of exercises (including many Kaggle challenges and some publicly-available datasets - precisely the sort of project that a beginner needs to get their feet wet).… (altro)

A pretty good resource and one of the better books on the subject, in my opinion. However, many describe it as "introductory" algorithms, and I'm not sure I totally agree. Unless you already posses a solid foundation in related areas, a newbie will often find it hard to walk into this and immediately understand it. And maybe some will say that would be unrealistic, and I would be one of those. However, I actually have heard and seen others say exactly that, and again, I don't agree. Nonetheless, a pretty good book and solid resource. Recommended.… (altro)