IJRCS-Volume 7 Issue 1 Paper 1

Data Science: Reducing Environmental Complexities

Author’s Name : Trivenu Vembuluru

Volume 07 Issue 01 Year 2020  ISSN No:  2349-3828  Page no: 01-07



.Data Science has emerged as an ambitious new scientific field, related debates and discussions have sought to address why science in general needs data science and what even makes data science a science. However, few such discussions concern the intrinsic complexities and intelligence in data science. As data science focuses on a systematic understanding of complex data and business related problems. The core objective of data science is exploration of the complexities, among these complexities Environmental complexities is an important factor. By using some algorithms this complexity can be reduced.


Data science, Environmental complexities, algorithms


  1. Jeff Leek (2013-12-12). “The key word in “Data Science” is not Data, it is Science”. Simply Statistics.
  2. The Journal of Data Science. (2003, January). Contents of Volume 1, Issue 1, January 2003. Retrieved from http://www.jds- online.com/v1-1
  3. Barlow, Mike (2013). The Culture of Big Data. O’Reilly Media, Inc.
  4. Donoho, David (September 2015). “50 Years of Data Science” (PDF). Based on a talk at Tukey Centennial workshop, Princeton NJ Sept 18 2015.
  5. Data Science Journal. (2012, April). Available Volumes. Retrieved from Japan Science and Technology Information Aggregator, Electronic: http://www.jstage.jst.go.jp/browse/dsj/_vols
  6. K. Karimi and H.J. Hamilton (2011), “Generation and Interpretation of Temporal Decision Rules”, International Journal of Computer Information Systems and Industrial Management Applications, Volume
  7. Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4
  8. Brenner, N., Bialek, W., & de Ruyter van Steveninck, R.R. (2000).
  9. Andrecut, M. (2009). “Parallel GPU Implementation of Iterative PCA Algorithms”. Journal of Computational Biology. 16 (11): 1593–1599. doi:10.1089/cmb.2008.0221. PMID 19772385
  10. McLachlan, G. J. (2004). Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience. ISBN 0-471-69115-1. MR 1190469
  11. Ben-Hur, Asa, Horn, David, Siegelmann, Hava, and Vapnik, Vladimir; “Support vector clustering” (2001) Journal of Machine Learning Research, 2: 125–137.
  12. R. Ng and J. Han. “Efficient and effective clustering method for spatial data mining”. In: Proceedings of the 20th VLDB Conference, pages 144-155, Santiago, Chile, 1994.
  13. Rennie, J.; Shih, L.; Teevan, J.; Karger, D. (2003). Tackling the poor assumptions of Naive Bayes classifiers
  14. Everitt, Brian (1998). The Cambridge Dictionary of Statistics. Cambridge, UK New York: Cambridge University Press. ISBN 0521593468.