, Volume 28, Issue 2, pp 342–344 | Cite as

Comments on: Data science, big data and statistics

  • J. S. MarronEmail author

The authors are to be congratulated on a fine summary of the state of the art of the currently fashionable topic of big data/data science. There are many well-taken points made. Plenty of opinions are currently being circulated on this topic, but the authors offer some interesting perspectives that others have not, such as the importance of robustness ideas, underlining the importance of data analytic thinking in terms of networks, and the key concept of convergence of methodologies. The point as to how classical statistical methods were created in a different environment than the world we currently live in is particularly well taken. I fully agree with their conclusion that more explicit organizational efforts are needed to further both research and education in data science.

A perhaps useful model for the latter is the idea of team data science. The genesis of this is the realization that most of science already understands the major benefits of research being done by teams of...

Mathematics Subject Classification




  1. Bendich P, Marron JS, Miller E, Pieloch A, Skwerer S (2016) Persistent homology analysis of brain artery trees. Ann Appl Stat 10(1):198–218MathSciNetCrossRefGoogle Scholar
  2. Dryden IL, Mardia KV (2016) Statistical shape analysis with applications in R. Wiley Series in Probability and Statistics, 2nd edn. Wiley, ChichesterCrossRefzbMATHGoogle Scholar
  3. Feng Q, Jiang M, Hannig J, Marron JS (2018) Angle-based joint and individual variation explained. J Multivar Anal 166:241–265MathSciNetCrossRefzbMATHGoogle Scholar
  4. Lock EF, Hoadley KA, Marron JS, Nobel AB (2013) Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann Appl Stat 7(1):523–542MathSciNetCrossRefzbMATHGoogle Scholar
  5. Marron JS, Alonso AM (2014) Overview of object oriented data analysis. Biom J 56(5):732–753MathSciNetCrossRefzbMATHGoogle Scholar
  6. Pizer SM, Marron JS (2017) Object statistics on curved manifolds. In: Szekely G, Zheng G, Li S (eds) Statistical shape and deformation analysis. Elsevier, Amsterdam, pp 137–164Google Scholar
  7. Pizer SM, Hong J, Jung S, Marron JS, Schulz J, Vicory J (2014) Relative statistical performance of s-reps with principal nested spheres vs. PDMs. In: Proceedings of the shape 2014-symposium of statistical shape models and applications, pp 11–13Google Scholar
  8. Wang H, Marron JS (2007) Object oriented data analysis: sets of trees. Ann Stat 35(5):1849–1873MathSciNetCrossRefzbMATHGoogle Scholar
  9. Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM, Network Cancer Genome Atlas Research, et al (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120Google Scholar
  10. Yu Q, Risk BB, Zhang K, Marron JS (2017) Jive integration of imaging and behavioral data. NeuroImage 152:38–49CrossRefGoogle Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  1. 1.Department of Statistics and Operations ResearchUniversity of North CarolinaChapel HillUSA

Personalised recommendations