BIG-DATA and the Challenges for Statistical Inference and Economics Teaching and Learning




New technologies, Paradigm, Logical reasoning, Instrumental skills, Scenarios, Interactivity, Modelling and simulation


The  increasing  automation  in  data  collection,  either  in  structured  or unstructured formats, as well as the development of reading, concatenation and comparison algorithms and the growing analytical skills which characterize the era of Big Data, cannot not only be considered a technological achievement, but an organizational, methodological and analytical challenge for knowledge as well, which is necessary to generate opportunities and added value.

In fact, exploiting the potential of Big-Data includes all fields of community activity; and given its ability to extract behaviour patterns, we are interested in the challenges for the field of teaching and learning, particularly in the field of statistical inference and economic theory.

Big-Data can improve the understanding of concepts, models and techniques used in both statistical inference and economic theory, and it can also generate reliable and robust short and long term predictions. These facts have led to the demand for analytical capabilities, which in turn encourages teachers and students to demand access to massive information produced by individuals, companies and public and private organizations in their transactions and inter- relationships.

Mass data (Big Data) is changing the way people access, understand and organize knowledge, which in turn is causing a shift in the approach to statistics and economics teaching, considering them as a real way of thinking rather than just operational and technical disciplines. Hence, the question is how teachers can use automated collection and analytical skills to their advantage when teaching statistics and economics; and whether it will lead to a change in what is taught and how it is taught.


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Author Biographies

Juan Luis Peñaloza Figueroa, Universidad Complutense de Madrid

Department of Statistics and Operational Research II

C. Vargas Perez, Universidad Complutense de Madrid

Department of Applied Economics IV


Akerkar R. (Ed.). (2014). Big Data Computing. CRC Press.

Anderson, C. (2009). "Living by Numbers". Wired Magazine. July 2009.New York: Conde Nast Publications.

Cobb, George, W. (2007). The Introductory Statistics Course: A Ptolemaic Curriculum. Journal of Technology Innovation in Statistics Education Vol. 1 No. 1. UCLA. (available at:

Cukier, Kenneth and Mayer-Schönberger, Viktor (2013). Big Data: A Revolution that Will Transform How We Live, Work and Think. John Murray Publishers. London, UK.

Dean, Jefry and Ghemwat, Sanjay (2010). Map Reduce: A Flexible Data Processing Tool. Communications of the ACM, Volume 53, Issuse.1, January 2010, pp 72-77.

Dean J, Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107-113.

Diebold, F. X. (2012). A Personal Perspective on the Origin(s) and Development of "Big Data: The Phenomenon, the Term, and the Discipline". Draft paper dated November 26, 2012.

Duboc, Leticia; Rosenblum, David S.; Wicks, Tony (2006). A Framework for Modelling and Analysis of Software Systems Scalability. Proceeding of the 28th international conference on Software engineering - ICSE '06. p. 949. ISBN 1595933751.

Fabinger, Michal and E. Glen Weyl, E. Glen (2015). A Tractable Approach to Pass-Through Patterns. Link: file:///C:/Users/JL/Downloads/SSRN-id2575471.pdf (access 07/04/2016).

García Ros, R., Pérez González, F. & Talaya González, I. (2008). Preferencias Respecto a Métodos Instruccionales de los Estudiantes Universitarios de Nuevo Acceso y su Relación con Estilos de Aprendizaje y Estrategias Motivacionales. Electronic Journal of Research in Educational Psychology, 6(16), 547-570.

Gartner, Eric Savitz (2012). "10 Critical Tech Trends for the Next Five Years". next-five years/.

Gould, Robert (2010). Statistics and the Modern Student. International Statistical Review, 78, 2, pp. 297-315. UK.

Hederich Martínez, C., Gravini Donado, M. & Camargo Uribe, A. (2011). El Estilo y la Ense-anza: Un Debate Sobre Cómo Enfrentar las Diferencias Individuales en el Aula de Clase. In R. Roig Vila y C. Laneve, C. (Eds.), La pratica educativa nella società dell'informazione. L'innovazione attraverso la ricerca (pp. 213-222). Alcoy-Brescia, Italia: Marfil y La Scuola Editrice.

Kambatla K, Kollias G, Kumar V, Grama A. (2014). Trends in Big Data Analytics. Journal of Parallel Distributed Computation, 74, pp. 2561-2573.

Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Stamford, CT: META Group Research Note, February 6, 2001.

Leedy, P. & Ormrod, J. (2001). Practical Research: Planning and Design. 7th Editon. Upper Saddle River, NJ: Merrill Prentice Hall. Thousand Oaks: SAGE Publications.

Maté Jiménez, Carlos (2014). Big Data: Un Nuevo Paradigma de Análisis de Datos. Revista Anales De Mecánica Y Electricidad. Nov-Dec. http://www.revista-

Meyer-Schonberger, Viktor and Cukier, Kenneth (2013). Big Data: A Revolution that Will Transform How We Live, Work and Think. John Murray Publishers. London. UK Company.

Mitchell, Ian and Wilson, Mark (2012). Linked Data. Connecting and Exploiting Big-Data. White-Paper, march. Fujitsu UK. Link: Images/Linked-data- connecting-and-exploiting-big-data-(v1.0).pdf.

Mujeeb, S. MD. And Naidu, L. Kasi (2015). A Relative Study on Big Data Applications and Techniques. International Journal of Engineering and Innovative Technology (IJEIT).Volume 4, Issue 10, April. ISSN: 2277-3754.

Müller, Martin U., Rosenbach, Marcel and Schulz, Thomas (2013). Living by Numbers: Big-Data Knows What your Future Holds. DER SPIEGEL No. 20. Germany (Translated from German by Christopher Sultan).

Peña, D., Prieto, J. and Viladomat, J. (2010) "Eigenvectors of a Kurtosis Matrix as Interesting Directions to Reveal Cluster Structure", Journal of Multivariate Analysis 9, 1995 -2007, 2010.

Peñaloza F., Juan Luis y Vargas P., Carmen G. (2015). "Construction and Evaluation of Scenarios as a Learning Strategy through Modelling-Simulation". In: Multidisciplinary Journal for Education, Social, and Technological Science. Vol. 14, Num. 1. pp. 40 – 62. DOI:

Pe-ñaloza F., Juan Luis y Vargas P., Carmen G. (2006). ¿Qué debe Cambiar en el Aprendizaje de la Estadística en las Ciencias del Comportamiento?. XIV Jornadas de ASEPUMA y II Encuentro Internacional, 21 y 22 de septiembre de 2006. Facultad de Ciencias Económicas y Empresariales, Badajoz, Espa-a.

Schelén, Olov, Elragal, Ahmed and Haddara, Moutaz (2015). A Roadmap for Big-Data Research and Education. Technical Report. Luleá University of Technology. ISBN 978-91- 7583-275-3.

Tkacz, Ewaryst and Kapczyn´ski, Adrían (2009). Internet: Technical Development and Applications, Springer, 2009.

Warren, Kim (2008). Strategic Management Dynamics. John Wiley & Sons, LTD. England.

Williams, Carrie (2007). Research Methods. Journal of Business & Economic Research. Volume 5, Number 3 – March.

Zhang, Junping, Wang, Fei-Yue, Wang, Kunfeng, Lin, Wei-Hua, Xu, Xin, Chen, Cheng (2011). Data-Driven Intelligent Transportation Systems: A Survey. IEEE Trans. Intell. Trans. Syst. 12 (4), pp. 1624–1639.

Zicari, R. V. (2014). Big Data: Challenges and Opportunities. In Akerkar R. (Ed). Big data computing. CRC Press.

Zikopoulos, P., deRoos, D., Bienko, C., Buglio, R., & Andrews, M. (2015). Big Data Beyond the Hype: A Guide to Conversations for Today's Data Center. New York, NY: McGraw-Hill. Education.




How to Cite

Peñaloza Figueroa, J. L., & Vargas Perez, C. (2017). BIG-DATA and the Challenges for Statistical Inference and Economics Teaching and Learning. Multidisciplinary Journal for Education, Social and Technological Sciences, 4(1), 64–87.