Skip to main content
SpringerLink
Log in

An Introduction to Data Science and Its Applications

  • Chapter
  • First Online:
Data Science and Productivity Analytics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 290))

Abstract

Data science has become a fundamental discipline, both in the field of basic research and in the resolution of applied problems, where statistics and computer science intersect. Thus, from the perspective of the data itself, machine learning, operation research, methods and algorithms, and data mining techniques are aligned to address new challenges characterised by the complexity, volume and heterogeneous nature of data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamo, J. M. (2001). Data Mining for association rules and sequential patterns. Sequential and parallel algorithms. Springer.

    Google Scholar 

  • Agrawal, R., & Srikant, R. (1996). Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, Santiago, Chile.

    Google Scholar 

  • Almiñana, M., Escudero, L. F., Pérez-Martín, A., Rabasa, A., & Santamaría, L. (2012). A classification rule reduction algorithm based on significance domains. TOP, 22, 397–418.

    Google Scholar 

  • Alter, S. L. (1977). A taxonomy of decision support systems. Sloan Management Review, 19(1), 39–56.

    Google Scholar 

  • Alter, S. L. (1980). Decision support systems: Current practice and continuing challenge. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Ashri, R. (2018). Building AI software: Data-driven vs. model-driven AI and why we need an AI-specific software development paradigm. https://hackernoon.com/building-ai-software-data-driven-vs-model-driven-ai-and-why-we-need-an-ai-specific-software-640f74aaf78f.

  • Berner, E. S., & La Lande, T. J. (2016). Overview of clinical decision support systems. Clinical Decision Support Systems, 1–17.

    Google Scholar 

  • Bloemer, J. M., Brijs, T., Vanhoof, K., & Swinnen, G. (2003). Comparing complete and partial classification for identifying customers at risk. Research in Marketing, 604, 1–15.

    Google Scholar 

  • Bonczek, R. H., Holsapple, C. W., & Whinston, A. B. (1981). Foundations of decision support systems. New York: Academic Press.

    Google Scholar 

  • Box, G. E. P., & Jenkins, G. M. (1973). Some comments on a paper by Chatfield and Prothero and on a review by Kendall. Journal of the Royal Statistical Society. Series A (General), 136(3), 337–352.

    Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. The Wadsworth and Brooks-Cole statistics-probability Series. Taylor & Francis.

    Google Scholar 

  • Chapelle, O., Vapnik, V., & Bousquet, O. (2002). Choosing multiple parameters for support vector machines machine learning, 46, 131.

    Google Scholar 

  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From Big Data to big impact. MIS Quarterly, 36(4), 1165–1188.

    Google Scholar 

  • Desanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management Science, 33(5), 589–609.

    Google Scholar 

  • Esteve, M., Miró, F., & Rabasa, A. (2018). Classification of tweets with a mixed method based on pragmatic content and meta-information. International Journal of Design & Nature and Ecodynamics, 13(1), 60–70.

    Google Scholar 

  • Exastax. (2017). Top 7 Big Data Use Cases in Insurance Industry. Retrieved December 31, 2018 from https://www.exastax.com/big-data/top-7-big-data-use-cases-in-insurance-industry/.

  • García, S., Luengo, J., Sáez, J. A., López, V., & Herrera, F. (2013). A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning. IEEE Transactions on Knowledge and Data Engineering, 25(4), 734–750.

    Google Scholar 

  • Gorry, G. A. and Scott-Morton M. A. (1971). A Framework for Management Information Systems, Sloan Management Review, October, pp 55–70.

    Google Scholar 

  • Hall, P., & Xue, J. H. (2014). On selecting interacting features from high-dimensional data. Computational Statistics & Data Analysis, 71, 694–708. https://doi.org/10.1016/j.csda.2012.10.010.

    Google Scholar 

  • Hardy, V., O’Connor, Y., Heavin, C., Mastellos, N., Tran, T., O’Donoghue, J., et al. (2017). The added value of a mobile application of Community Case Management on under-5 referral, re-consultation and hospitalization rates in two districts in Northern Malawi: Study protocol for a pragmatic stepped wedge cluster-randomized controlled trial. Trials, 18, 475. https://doi.org/10.1186/s13063-017-2213-z.

    Google Scholar 

  • He, Z., He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24, 1641–1650.

    Google Scholar 

  • Hey, T., Tansley, S., & Tolle, K. (2009). The fourth paradigm: Data-intensive scientific discovery. Ed. Microsoft Research.

    Google Scholar 

  • Hunt, D. L., Haynes, R. B., Hanna, S. E., & Smith, K. (1998). Effects of computer-based clinical decision support systems on physician performance and patient outcomes: A systematic review. JAMA, 280(15), 1339–1346. https://doi.org/10.1001/jama.280.15.1339.

    Google Scholar 

  • Ismail, N. A., & Hussin H. (2013). E-CRM features in the context of airlines e-ticket purchasing: A conceptual framework. In 5th International Conference on Information and Communication Technology for the Muslim World (Ict4m).

    Google Scholar 

  • Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119–127.

    Google Scholar 

  • Kumar, D. S., Sathyadevi, G., & Sivanesh, S. (2011). Decision support system for medical diagnosis using Data Mining. International Journal of Computer Science Issues, 8(3), 147–153.

    Google Scholar 

  • Lashari, S. A., Ibrahim, R., Senan, N., & Taujuddin, N. S. (2018). Application of Data Mining techniques for medical data classification: A review. In Proceedings of the MATEC Web of Conferences (Vol. 150, p. 06003).

    Google Scholar 

  • Lu, Z. C., Qin, Z., Zhang, & Fang, J. (2014). A fast feature selection approach based on rough set boundary regions. Pattern Recognition Letters, 36, 81–88. https://doi.org/10.1016/j.patrec.2013.09.012.

    Google Scholar 

  • MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281–297). University of California Press.

    Google Scholar 

  • Mehta, M., Agrawal, R., Rissanen, J. (1996). SLIQ: A fast scalable classifier for Data Mining. In: P. Apers, M. Bouzeghoub, G. Gardarin (Eds), Advances in database technology—EDBT 1996 (Vol. 1057). Lecture notes in computer science. Springer.

    Google Scholar 

  • Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of Big Data to health care. JAMA, 309(13), 1351–1352.

    Google Scholar 

  • Peres-Neto, P. R., Jackson, D. A., & Somers, K. M. (2005). How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Computational Statistics & Data Analysis, 49(4), 974–997.

    Google Scholar 

  • Pérez-Martín, A., Pérez-Torregrosa, A., & Vaca, M. (2018). Big data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89, 448–454.

    Google Scholar 

  • Potter, R., Probyn, K., Bernstein, C., Pincus, T., Underwood, M., & Matharu, M. (2018). Diagnostic and classification tools for chronic headache disorders: A systematic review. Cephalalgia. https://doi.org/10.1177/0333102418806864.

    Google Scholar 

  • Power, D. J. (1997). What is DSS? The Online Executive Journal for Data-Intensive Decision Support, 1(3).

    Google Scholar 

  • Power, D. J. (2001). Supporting decision makers: An expanded framework. In A. Harriger (Ed.), E-Proceedings 2001 Informing Science Conference (pp. 431e–436e).

    Google Scholar 

  • Power, D. J. (2002). Decision support systems: Concepts and resources for managers. Westport, CT: Greenwood/Quorum.

    Google Scholar 

  • Power, D. J., & Sharda, R. (2007). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43(3), 1044–1061.

    Google Scholar 

  • Power, D. J. (2008). Decision support systems concept. In F. Adam, P. Humphreys (Eds.), Encyclopedia of decision making and decision support technologies (pp. 232–235). IGI-Global.

    Google Scholar 

  • Power, D. J., & Heavin, C. (2017). Decision support, analytics, and business intelligence (3rd ed.). New York, NY: Business Expert Press.

    Google Scholar 

  • Power, D., & Heavin, C. (2018). Data-based decision making and digital transformation. New York, NY: Business Expert Press.

    Google Scholar 

  • Provost, F., & Fawcett, T. (2013). Data Science and its relationship to Big Data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508.

    Google Scholar 

  • Quinlan, J. R. (1986). Machine Learning, 1, 81. https://doi.org/10.1007/BF00116251.

    Google Scholar 

  • Quinlan, J. R. (1993). C4.5: Programs for machine learning. Series in machine learning. USA: Morgan Kaufmann Publishers.

    Google Scholar 

  • Rusov, J., & Mishita, M. (2016). Model of decision support system used for assessment of insurance risk. Journal of Applied Engineering Science, 14(1), 13–20. https://doi.org/10.5937/jaes14-8845.

    Google Scholar 

  • Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology, decision support systems 33, 111–126.

    Google Scholar 

  • Simon, H. (1960). The new science of management decision. New-York: Harper and Row.

    Google Scholar 

  • Sprague, R. H., & Watson, M. J. (1979). Bit by Bit: Toward decision support systems. California Management Review, 22(1), 60–67.

    Google Scholar 

  • Sprague, R., & Watson, H. (1993). Decision Support Systems: Putting Theory into Practice. Englewood Cliffs, New Jersey: Prentice Hall

    Google Scholar 

  • Tsai, C. F. (2009). Feature selection in bankruptcy prediction. Knowledge-Based Systems, 22(2), 120–127.

    Google Scholar 

  • Tsai, C. F., & Hsiao, Y. C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258–269.

    Google Scholar 

  • Tsami, M., Adamos, G., Nathanail, E., Budilovich, E., Yatskiv, I., & Magginas, V. (2018). A decision tree approach for achieving high customer satisfaction at urban interchanges. Transport and Telecommunication, 19(3), 194–202.

    Google Scholar 

  • Uusitalo, L., et al. (2015). An overview of methods to evaluate uncertainty of deterministic models in decision support. Environmental Modelling and Software, 63, 24–31.

    Google Scholar 

  • Verhoef, P. C., & Donkers, B. (2001). Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32, 189–199.

    Google Scholar 

  • Waller, M., & Fawcett, S. (2013). Data Science, predictive analytics, and Big Data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.

    Google Scholar 

  • Yang, L., Liu, S., Tsoka, S., & Papageorgiou, L. G. (2017). A regression tree approach using mathematical programming. Expert Systems with Applications, 78(15), 347–357.

    Google Scholar 

  • Yin, Ch., Xia, L., Zhang, S., & Wang, J. (2018). Improved clustering algorithm based on high-speed network data stream. Soft Computing, 22(13), 4185–4195.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Operations Research, University Miguel Hernandez, Elche, Alicante, Spain

    Alex Rabasa

  2. Business Information Systems, University College Cork, Cork, Ireland

    Ciara Heavin

Authors
  1. Alex Rabasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ciara Heavin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Rabasa .

Editor information

Editors and Affiliations

  1. School of Management, University of Bradford, Bradford, UK

    Vincent Charles

  2. Center of Operations Research, University Miguel Hernandez, Elche, Alicante, Spain

    Juan Aparicio

  3. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Joe Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rabasa, A., Heavin, C. (2020). An Introduction to Data Science and Its Applications. In: Charles, V., Aparicio, J., Zhu, J. (eds) Data Science and Productivity Analytics. International Series in Operations Research & Management Science, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-030-43384-0_3

Download citation

Publish with us

Policies and ethics

Search

Navigation