Abstract
With the increasing availability and trendiness of “big data”, data science has become a fast growing discipline. Data analysis techniques are shifting from classical statistical inferences to algorithmic machine learnings. Will the rise of data science lead to the fall of statistics? If education is the key to defend statistics as a discipline, what should statisticians teach to respond to the challenges brought by big data? This paper aims to provide the current situation of data science and statistics programs within the higher education sector in Australia and some personal thoughts on statistics education in this era.
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References
Anderson, C.: The End of Theory: The Data Deluge Makes the Scientific Method Obsolete (2008). https://www.wired.com/2008/06/pb-theory/. Accessed 26 July 2018
Berger, J.O., Sellke, T.: Testing a point null hypothesis: the irreconcilability of P value and evidence. J. Am. Stat. Assoc. 82, 112–122 (1987)
Cox, D.R.: Big data and precision. Biometrika 102, 712–716 (2015)
Davenport, T., Patil, D.: Data scientist: the sexiest job of the 21st century. Harvard Bus. Rev. 90, 70–76 (2012)
Dua, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml. Accessed 6 Oct 2018
Dunson, D.B.: Statistics in the big data era: failures of the machine. Stat. Probabil. Lett. 136, 4–9 (2018)
Evans, S.J., Mills, P., Dawson, J.: The end of the \(p\) value? Brit. Heart J. 60, 177–180 (1988)
Faraway, J., Augustin, N.: When small data beats big data. Stat. Probabil. Lett. 136, 142–145 (2018)
Gal, I., Ginsburg, L.: The role of beliefs and attitudes in learning statistics: towards an assessment framework. J. Stat. Educ. 2, 2 (1994). https://doi.org/10.1080/10691898.1994.11910471
Ioannidis, J.: The proposal to lower \(p\) value threshold to.005. J. Am. Med. Assoc. 319, 1429–1430 (2018)
James, G.: Statistics within business in the era of big data. Stat. Probabil. Lett. 136, 155–159 (2018)
Lin, M., Lucas Jr., H., Shmueli, G.: Too big to fail: large samples and the \(p\)-value problem. Inform. Syst. Res. 24, 906–917 (2013)
Meinshausen, N., Bühlmann, P.: Maximin effects in inhomogeneous large-scale data. Ann. Statist. 43, 1801–1830 (2015)
Reid, N.: Statistical science in the world of big data. Stat. Probabil. Lett. 136, 42–45 (2018)
SCImago: SJR-SCImago Journal & Country Rank. http://www.scimagojr.com. Accessed 26 July 2018
Secchi, P.: On the role of statistics in the era of big data: a call for a debate. Stat. Probabil. Lett. 136, 10–14 (2018)
Sherman, M.: Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties. Wiley, New York (2011)
Simmons, J.P., Nelson, L.D., Simonsohn, U.: False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011)
Sterne, J.: Teaching hypothesis tests - time for significant change? Statist. Med. 21, 985–994 (2002)
Wasserstein, R., Lazar, N.: The ASA’s statement on \(p\)-values: context, process, and purpose. Am. Stat. 70, 129–133 (2016)
Wit, E.C.: Big data and biostatistics: the death of the asymptotic Valhalla. Stat. Probabil. Lett. 136, 30–33 (2018)
Zieffler, A., Garfield, J., Alt, S., Dupuis, D., Holleque, K., Chang, B.: What does research suggest about the teaching and learning of introductory statistics at the college level? A review of the literature. J. Stat. Educ. 16, 2 (2017). https://doi.org/10.1080/10691898.2008.11889566
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Ip, R.H.L. (2019). The Role of Statistics Education in the Big Data Era. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_22
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DOI: https://doi.org/10.1007/978-981-13-6661-1_22
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