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Syllabus for the Data Science Curriculum, Columbia University.
Looks a lot like a small book on classical stats: a bit of Python, linear regression, logistic regression and classification constitutes the core. Read the Columbia University eBook at http://columbia-applied-data-science.github.io/appdatasci.pdf. I believe the authors spend way too much time on singular value decomposition, advanced matrix algebra, general linear model, Newton optimization / root finding algorithms to find maximum of theoretical likelihood functions (for logistic regression and in some L1 context). In my opinion, this is NOT part of data science and should not be taught in a data science program. Examples of big data success stories or failures (with technical details) are missing, as well as real-world projects for students. There is nothing on visualization or dashboards. But the more you read, towards the end, the more interesting it gets, with several pages on memory and Python optimization, which I enjoyed reading (I learned new stuff) and which I think - is part of data science (the computer science arm).
For a far more modern and comprehensive perspective on data science, read our Data Science eBook.
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The author's background is engineering data science. While Vincent's background is business data science. These are two very different animals, and most University programs I've seen so far are about engineering data science, because that's their strength.
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