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Data Science Resources: Books/Journals

Recent Book Acquisitions

Arabnia HR, Daimi K, Stahlbock R, et al. (2020). Principles of data science. Cham: Springer.

Batra U, Roy NR, & Panda B. (2020). Data science and analytics. Cham: Springer.

Berthold MR (2020). Guide to intelligent data science: how to intelligently make use of real data. Cham: Springer International Publishing.

Carter N. (2021). Data science for mathematicians. Boca Raton, FL: CRC Press.

Grus J. (2019). Data science from scratch: first principles with Python. Beijing: O'Reilly.

Inmon Wh, Linstedt D, & Levins M. (2019). Data architecture: a primer for the data scientist. 2nd edition. London: Elsevier Academic Press. (QA 76.9.D37 I56 2019).

Kotu V, & Balachandre D. (2019). Data science: concepts and practice. Cambridge, MA: Morgan Kaufmann. (QA 76.9.D343.K674 2019).

Larose CD, & Larose DT. (2019). Data science using Python and R. Hoboken, NJ: John Wiley & Sons. (QA 76.9.D343 L376 (2019).

Sakr S, & Zomaya AY. (2019). Encyclopedia of big data technologies. Cham: Springer.

Skiena SS. (2017). The data science design manual. Cham: Springer.

 

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