Book Review
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MathSciNet review:
3891925
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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
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
L. Breiman, Statistical modeling: The two cultures, Statistical Science 16 (2001), no. 3, 199–231.
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.
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.
M. Davidian, Aren’t we data science?, AmStat News, “President’s Corner”, July 1, 2013.
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
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
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
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.
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, An introduction to statistical learning, Springer Texts in Statistics, vol. 103, Springer, New York, 2013. With applications in R. MR 3100153, DOI 10.1007/978-1-4614-7138-7
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
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
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
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.
J. W. Tukey, The future of data analysis, The Annals of Mathematical Statistics 33 (1962), no. 1, 1–67.
J. W. Tukey, Exploratory data analysis, Addison-Wesley, 1977.
B. Yu, Let us own data science, IMS Bulletin Online, October 1, 2014.
References
- 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
- L. Breiman, Statistical modeling: The two cultures, Statistical Science 16 (2001), no. 3, 199–231.
- 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.
- 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.
- M. Davidian, Aren’t we data science?, AmStat News, “President’s Corner”, July 1, 2013.
- 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
- 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
- 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
- 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.
- 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, DOI 10.1007/978-1-4614-7138-7
- 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
- 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
- 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
- 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.
- J. W. Tukey, The future of data analysis, The Annals of Mathematical Statistics 33 (1962), no. 1, 1–67.
- J. W. Tukey, Exploratory data analysis, Addison-Wesley, 1977.
- 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.
56 (2019), 143-149
DOI:
https://doi.org/10.1090/bull/1596
Published electronically:
September 5, 2017
Review copyright:
© Copyright 2017
American Mathematical Society
