The science of statistics versus data science: What is the future?
Author
Abstract
Suggested Citation
DOI: 10.1016/j.techfore.2021.121111
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- William S. Cleveland, 2001. "Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics," International Statistical Review, International Statistical Institute, vol. 69(1), pages 21-26, April.
- Islam, Nazrul & Marinakis, Yorgos & Majadillas, Mary Anne & Fink, Matthias & Walsh, Steven T., 2020. "Here there be dragons, a pre-roadmap construct for IoT service infrastructure," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
- Robert N. Rodriguez, 2015. "Who Will Celebrate Our 200th Anniversary? Growing the Next Generation of ASA Members," The American Statistician, Taylor & Francis Journals, vol. 69(2), pages 91-95, May.
- Lo, Fang-Yi & Campos, Nayara, 2018. "Blending Internet-of-Things (IoT) solutions into relationship marketing strategies," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 10-18.
- Pedro Galeano & Daniel Peña, 2019. "Rejoinder on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 363-368, June.
- Steven Nahmias, 1979. "Simple Approximations for a Variety of Dynamic Leadtime Lost-Sales Inventory Models," Operations Research, INFORMS, vol. 27(5), pages 904-924, October.
- Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
- Geum, Youngjung & Lee, HyeonJeong & Lee, Youngjo & Park, Yongtae, 2015. "Development of data-driven technology roadmap considering dependency: An ARM-based technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 264-279.
- Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
- J. Hardin & R. Hoerl & Nicholas J. Horton & D. Nolan & B. Baumer & O. Hall-Holt & P. Murrell & R. Peng & P. Roback & D. Temple Lang & M. D. Ward, 2015. "Data Science in Statistics Curricula: Preparing Students to “Think with Data”," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 343-353, November.
- Dunson, David B., 2018. "Statistics in the big data era: Failures of the machine," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 4-9.
- Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
- Robert N. Rodriguez, 2013. "Building the Big Tent for Statistics," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 1-6, March.
- Peter J. Diggle, 2015. "Statistics: a data science for the 21st century," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 793-813, October.
- Olhede, Sofia C. & Wolfe, Patrick J., 2018. "The future of statistics and data science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 46-50.
- Learner, D. B. & Phillips, Fred Young, 1993. "Method and progress in management science," Socio-Economic Planning Sciences, Elsevier, vol. 27(1), pages 9-24, March.
- Daphne R. Raban & Avishag Gordon, 2020. "The evolution of data science and big data research: A bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1563-1581, March.
- Chen, Chih Ping & Weng, Ju-Yin & Yang, Chin-Sheng & Tseng, Fan-Mei, 2018. "Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 88-98.
- Iqbal, Rahat & Doctor, Faiyaz & More, Brian & Mahmud, Shahid & Yousuf, Usman, 2020. "Big data analytics: Computational intelligence techniques and application areas," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
- Faraway, Julian J. & Augustin, Nicole H., 2018. "When small data beats big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 142-145.
- Abigael C. Nachtsheim & John Stufken, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 345-348, June.
- Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
- Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
- Daniel A. Griffith, 2022. "Selected Payback Statistical Contributions to Matrix/Linear Algebra: Some Counterflowing Conceptualizations," Stats, MDPI, vol. 5(4), pages 1-16, November.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
- Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
- Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
- Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
- Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
- Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
- Montero-Manso, Pablo & Hyndman, Rob J., 2021.
"Principles and algorithms for forecasting groups of time series: Locality and globality,"
International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
- Pablo Montero-Manso & Rob J Hyndman, 2020. "Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality," Monash Econometrics and Business Statistics Working Papers 45/20, Monash University, Department of Econometrics and Business Statistics.
- Nasios, Ioannis & Vogklis, Konstantinos, 2022. "Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1448-1459.
- Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
- Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
- Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
- Shalini R. Urs & Mohamed Minhaj, 2023. "Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 606-622, June.
- Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N., 2022. "Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1482-1491.
- Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
- Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "The M5 competition: Background, organization, and implementation," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1325-1336.
- Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022.
"Forecasting: theory and practice,"
International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
- Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
- Reiko Aoki & Juan P. M. Bustamante & Gilberto A. Paula, 2022. "Local influence diagnostics with forward search in regression analysis," Statistical Papers, Springer, vol. 63(5), pages 1477-1497, October.
- Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023.
"Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
- Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS) WORMS/21/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
- Centobelli, Piera & Cerchione, Roberto & Esposito, Emilio & Oropallo, Eugenio, 2021. "Surfing blockchain wave, or drowning? Shaping the future of distributed ledgers and decentralized technologies," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
- Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
More about this item
Keywords
Perspective; Science; Statistics; Data science; Similarities; Differences; Pragmatism;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005448. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.