Book Review

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Book Information:

Authors: Brian Steele, John Chandler and Swarna Reddy

Title: Algorithms for data science

Additional book information: Springer, Cham, 2016, xxii+430 pp., ISBN 978-3-319-45797-0, Hardcover US $79.99, eBook US $59.99

- [1]
B. S. Baumer, D. T. Kaplan, and N. J. Horton,
*Modern data science with R*, Chapman and Hall/CRC Press, 2017.`http://mdsr-book.github.io` - [2]
L. Breiman,
*Statistical modeling: The two cultures*, Statistical Science**16**(2001), no. 3, 199-231. - [3]
B. Cassel and H. Topi,
*Strengthening data science education through collaboration*. Report on Workshop on Data Science Education, 2015, Funded by the Natl. Sci. Found., Oct. 3-5, Arlington, VA. - [4]
W. S. Cleveland,
*Data science: An action plan for expanding the technical areas of the field of statistics*, International Statistics Review**60**(2001), no. 1, 21-26. - [5]
M. Davidian,
*Aren't*, AmStat News, ``President's Corner'', July 1, 2013.*we*data science? - [6]
R. De Veaux, et al., ``Curriculum guidelines for undergraduate programs in data science'',
*Annual Review of Statistics and its Applications*, Vol. 4, pp. 15-30, 2017.`http://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-060116-053930` - [7]
R. De Veaux, et al., ``Curriculum guidelines for undergraduate programs in data science: Appendix--Detailed courses for a proposed data science major'',
*Annual Review of Statistics and its Applications*, Vol. 4, appendix, 2017.`http://www.annualreviews.org/doi/suppl/10.1146/annurev-statistics-060116-053930/suppl_file/st04_de_veaux_supmat.pdf` - [8]
D. Donoho,
*50 years of data science*, presentation at the Tukey Centennial Workshop, Princeton, NJ, September 18, 2015.`http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf` - [9]
D. van Dyck, M. Fuentes, M. Jordan, M. Newton, B. K. Ray, D, Temple Lang, H. Wickham,
*ASA Statement on the role of statistics in data science*, AmStat News, October 1, 2015. **[10]**Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani,*An introduction to statistical learning. With applications in R*, Springer Texts in Statistics, vol. 103, Springer, New York, 2013. MR**3100153**- [11]
M. I. Jordan, ``On computational thinking, inferential thinking and data science'',
*Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures*, keynote address, 2016.`http://dl.acm.org/citation.cfm?id=2935826` - [12]
McKinsey & Company,
*Big data: The next frontier for innovation, competition, and productivity*, 2011.`http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation` - [13]
McKinsey & Company,
*The age of analytics: Competing in a data-driven world*, December 2016.`http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world` - [14]
D. Nolan and D. Temple Lang,
*Data science in R: A case studies approach to computational reasoning and problem solving*. Chapman and Hall/CRC Press, 2015. - [15]
J. W. Tukey,
*The future of data analysis*, The Annals of Mathematical Statistics**33**(1962), no. 1, 1-67. - [16]
J. W. Tukey,
*Exploratory data analysis*, Addison-Wesley, 1977. - [17]
B. Yu,
*Let us own data science*, IMS Bulletin Online, October 1, 2014.

Review Information:

Reviewer: Richard D. De Veaux

Affiliation: Williams College

Email: rdeveaux@williams.edu

Reviewer: Nicholas R. De Veaux

Affiliation: Center for Computational Biology, Flatiron Institute, Simons Foundation

Email: nrdeveaux@gmail.com

Journal: Bull. Amer. Math. Soc.

MSC (2010): Primary 62-07, 68-01, 68P99, 68Q01, 68W01

DOI: https://doi.org/10.1090/bull/1596

Published electronically: September 5, 2017

Review copyright: © Copyright 2017 American Mathematical Society