What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

Applied mathematics; Computational Sciences; Data assimilation; Data fusion; Data Science; Data-driven modelling; Digital twins; Education; High-dimensional statistics; Interdisciplinary research; Machine Learning; Modelling; Research; Scientific Computing; Simulation; Training; Computational science; Data driven modelling; Engineering perspective; IT in business; Research papers; Information Systems; Modeling and Simulation; Computer Science Applications; Computational Theory and Mathematics; Applied Mathematics

Abstract :

[en] Data Science is today one of the main buzzwords, be it in business, industrial or academic settings. Machine learning, experimental design, data-driven modelling are all, undoubtedly, rising disciplines if one goes by the soaring number of research papers and patents appearing each year. The prospect of becoming a “Data Scientist” appeals to many. A discussion panel organised as part of the European Data Science Conference (European Association for Data Science (EuADS)) https://euads.org/edsc/ asked the question: “What makes Data Science different?” In this paper, we give our own, personal and multi-faceted view on this question, from a Statistics and an Engineering perspective. In particular, we compare Data Science to Statistics and discuss the connection between Data Science and Computational Sciences.

Disciplines :

Mathematics
Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

Bordas, Stéphane P. A. ; Department of Computational Engineering, University of Luxembourg, Esch-sur-Alzette, Luxembourg ; Institute of Mechanics and Advanced Materials, School of Engineering, Cardiff University, Wales, United Kingdom

External co-authors :

yes

Language :

English

Title :

What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences

Publication date :

November 2018

Journal title :

International Journal of Data Science and Analytics

ISSN :

2364-415X

eISSN :

2364-4168

Publisher :

Springer Science and Business Media Deutschland GmbH

Volume :

Issue :

Pages :

167 - 175

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://link.springer.com/article/10.1007/s41060-017-0090-x/fulltext.html

Funders :

European Research Council

Available on ORBilu :

since 25 November 2023

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Bibliography

Sagiroglu, S., Sinanc, D.: Big data: a review. In: International Conference on Collaboration Technologies and Systems (CTS). IEEE 2013, pp. 42–47 (2013)
Hayashi, C.: What is data science? Fundamental concepts and a heuristic example. In: Hayashi, C., Yajima, K., Bock, H.H., Ohsumi, N., Tanaka, Y., Baba, Y. (eds.) Data Science, Classification, and Related Methods, pp. 40–51. Springer, New York (1998)
Loukides, M.: What is data science, Big Data Now. Current Prespectives from O’Reilly Radar. Media, Inc., Sebastopol (2011)
Akerkar, R., Sajja, P.S.: Intelligent Techniques for Data Science, 1st edn. Springer, Switzerland (2016). 10.1007/978-3-319-29206-9 DOI: 10.1007/978-3-319-29206-9
Kitchin, R.: Big Data–hype or revolution? In: Sloan, L., Quan-Haase, A. (eds.) The SAGE Handbook of Social Media Research Methods, p. 27. SAGE, Beverley Hills (2017)
Hicks, S.C., Irizarry, R.A.: A guide to teaching data science. arXiv preprint arXiv:1612.07140 (2016)
Tang, R., Sae-Lim, W.: Data science programs in US higher education: an exploratory content analysis of program description, curriculum structure, and course focus. Educ. Inf. 32(3), 269 (2016)
Patil, D.: Building Data Science Teams. O’Reilly Media, Inc., Sebastopol (2011)
Cioffi-Revilla, C.: Unpublished: link to paper on Researh Gate (2016)
McDowell, D.L., Kalidindi, S.R.: The materials innovation ecosystem: a key enabler for the materials genome initiative. MRS Bull. 41(04), 326 (2016) DOI: 10.1557/mrs.2016.61
Cao, L.: Data science: a comprehensive overview. ACM Comput. Surv. (CSUR) 50(3), 43 (2017) DOI: 10.1145/3076253
Bandeira, A.S.: Ten lectures and forty-two open problems in the mathematics of data science. Lecture Notes (2015)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267 (1996)
Ledoit, O., Wolf, M.: Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Stat. 30(4), 1081 (2002) DOI: 10.1214/aos/1031689018
Bühlmann, P., Van De Geer, S.: Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer, New York (2011) DOI: 10.1007/978-3-642-20192-9
Samworth, R.J.: Big Data: A New Era for Statistics, pp. 43–46. The Eagle, Bryan (2014)
Diggle, P.J.: Statistics: a data science for the 21st century. J. R. Stat. Soc. Ser. A (Stat. Soc.) 178(4), 793 (2015) DOI: 10.1111/rssa.12132
Willcox, K., Bounova, G.: Mathematics in engineering: identifying, enhancing and linking the implicit mathematics curriculum. In: Proceeding of the 2004 American Society for Engineering Education Annual Conference and Exposition, American Society for Engineering Education, pp. 1–13 (2004)
Rüde, U., Willcox, K., McInnes, L.C., De Sterck, H., Biros, G., Bungartz, H., Corones, J., Cramer, E., Crowley, J., Ghattas, O., Gunzburger, M., Hanke, M., Harrison, R., Heroux, M., Hesthaven, J., Jimack, P., Johnson, C., Jordan, K., Keyes, D., Krause, R., Kumar, V., Mayer, S., Meza, J., Morken, K., Oden, J.T., Petzold, L., Raghavan, P., Shontz, S.M., Trefethen, A., Turner, A., Voevodin, V., Wohlmuth, B., W.C.S.: Research and Education in Computational Science and Engineering. arXiv preprint arXiv:1610.02608 (2016)
Ley, C., Verdebout, T.: Modern Directional Statistics. Chapman and Hall, Boca Raton, FL (2017) DOI: 10.1201/9781315119472
Hamelryck, T., Mardia, K., Ferkinghoff-Borg, J.: Bayesian Methods in Structural Bioinformatics. Springer, New York (2012) DOI: 10.1007/978-3-642-27225-7
Strang, G., Fix, G.J.: An Analysis of the Finite Element Method, vol. 212. Prentice-Hall, Englewood Cliffs, NJ (1973)
Zienkiewicz, O., Taylor, R.: The Finite Element Method, vol. 3. McGraw-Hill, London (1977)
Bathe, K.J.: Finite Element Method. Wiley Online Library, London (2008) DOI: 10.1002/9780470050118.ecse159
Dhatt, G., Lefrançois, E., Touzot, G.: Finite Element Method. Wiley, London (2012) DOI: 10.1002/9781118569764
Hughes, T.J.: The Finite Element Method: Linear Static and Dynamic Finite Element Analysis. Courier Corporation, North Chelmsford (2012)
Nguyen, V.P., Rabczuk, T., Bordas, S., Duflot, M.: Meshless methods: a review and computer implementation aspects. Math. Comput. Simul. 79(3), 763 (2008) DOI: 10.1016/j.matcom.2008.01.003
Hughes, T.J., Cottrell, J.A., Bazilevs, Y.: Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement. Comput. Methods Appl. Mech. Eng. 194(39), 4135 (2005) DOI: 10.1016/j.cma.2004.10.008
Nguyen, V.P., Anitescu, C., Bordas, S.P., Rabczuk, T.: Isogeometric analysis: an overview and computer implementation aspects. Math. Comput. Simul. 117, 89 (2015) DOI: 10.1016/j.matcom.2015.05.008
Marussig, B., Zechner, J., Beer, G., Fries, T.P.: Fast isogeometric boundary element method based on independent field approximation. Comput. Methods Appl. Mech. Eng. 284, 458 (2015) DOI: 10.1016/j.cma.2014.09.035
Atroshchenko, E., Xu, G., Tomar, S., Bordas, S.: Weakening the tight coupling between geometry and simulation in isogeometric analysis: from sub-and super-geometric analysis to Geometry Independent Field approximaTion (GIFT). arXiv preprint arXiv:1706.06371 (2017)
Song, C., Wolf, J.P.: The scaled boundary finite-element method? Alias consistent infinitesimal finite-element cell method? For elastodynamics. Comput. Methods Appl. Mech. Eng. 147(3–4), 329 (1997) DOI: 10.1016/S0045-7825(97)00021-2
Wolf, J.P., Song, C.: The scaled boundary finite-element method-a primer: derivations. Comput. Struct. 78(1), 191 (2000) DOI: 10.1016/S0045-7949(00)00099-7
Natarajan, S., Ooi, E.T., Saputra, A., Song, C.: A scaled boundary finite element formulation over arbitrary faceted star convex polyhedra. Eng. Anal. Bound. Elem. 80, 218 (2017) DOI: 10.1016/j.enganabound.2017.03.007
Ooi, E., Song, C., Natarajan, S.: A scaled boundary finite element formulation with bubble functions for elasto-static analyses of functionally graded materials. Comput. Mech. 60, 1–25 (2017) DOI: 10.1007/s00466-017-1443-y
Saputra, A., Talebi, H., Tran, D., Birk, C., Song, C.: Automatic image-based stress analysis by the scaled boundary finite element method. Int. J. Numer. Methods Eng. 109(5), 697 (2017) DOI: 10.1002/nme.5304
Brebbia, C., Cruse, T.: The boundary element method. J. Appl. Mech. 46, 718 (1979) DOI: 10.1115/1.3424642
Simpson, R., Trevelyan, J.: A partition of unity enriched dual boundary element method for accurate computations in fracture mechanics. Comput. Methods Appl. Mech. Eng. 200(1), 1 (2011) DOI: 10.1016/j.cma.2010.06.015
Simpson, R.N., Bordas, S.P., Trevelyan, J., Rabczuk, T.: A two-dimensional isogeometric boundary element method for elastostatic analysis. Comput. Methods Appl. Mech. Eng. 209, 87 (2012) DOI: 10.1016/j.cma.2011.08.008
Scott, M.A., Simpson, R.N., Evans, J.A., Lipton, S., Bordas, S.P., Hughes, T.J., Sederberg, T.W.: Isogeometric boundary element analysis using unstructured T-splines. Comput. Methods Appl. Mech. Eng. 254, 197 (2013) DOI: 10.1016/j.cma.2012.11.001
Peng, X., Atroshchenko, E., Kerfriden, P., Bordas, S.: Isogeometric boundary element methods for three dimensional static fracture and fatigue crack growth. Comput. Methods Appl. Mech. Eng. 316, 151 (2017) DOI: 10.1016/j.cma.2016.05.038
Fuentes, D., Oden, J., Diller, K., Hazle, J., Elliott, A., Shetty, A., Stafford, R.: Computational modeling and real-time control of patient-specific laser treatment of cancer. Ann. Biomed. Eng. 37(4), 763 (2009) DOI: 10.1007/s10439-008-9631-8
Oden, T., Moser, R., Ghattas, O.: Computer predictions with quantified uncertainty, part I. SIAM News 43(9), 1 (2010)
Oden, J.T., Prudhomme, S.: Control of modeling error in calibration and validation processes for predictive stochastic models. Int. J. Numer. Methods Eng. 87(1–5), 262 (2011) DOI: 10.1002/nme.3038
Hawkins-Daarud, A., Prudhomme, S., van der Zee, K.G., Oden, J.T.: Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth. J. Math. Biol. 67(6–7), 1457 (2013) DOI: 10.1007/s00285-012-0595-9
Prudencio, E., Bauman, P., Faghihi, D., Ravi-Chandar, K., Oden, J.: A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection. Int. J. Numer. Methods Eng. 102(3–4), 379 (2015) DOI: 10.1002/nme.4669
Oden, J.T., Lima, E.A., Almeida, R.C., Feng, Y., Rylander, M.N., Fuentes, D., Faghihi, D., Rahman, M.M., DeWitt, M., Gadde, M., Cliff, Z.J.: Toward predictive multiscale modeling of vascular tumor growth. Arch. Comput. Methods Eng. 23(4), 735 (2016) DOI: 10.1007/s11831-015-9156-x
Vanik, M.W., Beck, J.L., Au, S.: Bayesian probabilistic approach to structural health monitoring. J. Eng. Mech. 126(7), 738 (2000) DOI: 10.1061/(ASCE)0733-9399(2000)126:7(738)
Rappel, H., Beex, L.A., Hale, J.S., Bordas, S.: Bayesian inference for the stochastic identification of elastoplastic material parameters: introduction, misconceptions and insights. arXiv preprint arXiv:1606.02422 (2016)
Rappel, H., Beex, L.A., Bordas, S.P.: Bayesian inference to identify parameters in viscoelasticity. Mech. Time Depend. Mater. pp. 1–38 (2017)
Giffard-Roisin, S., Delingette, H., Jackson, T., Fovargue, L., Lee, J., Rinaldi, A., Ayache, N., Razavi, R., Sermesant, M.: Sparse Bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology. In: International Conference on Functional Imaging and Modeling of the Heart. Springer, New York, pp. 230–238 (2017) DOI: 10.1007/978-3-319-59448-4_22
Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E.R., Batchelor, T., Unkelbach, J., Ayache, N.: Bayesian personalization of brain tumor growth model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, New York, pp. 424–432 (2015)
Le Folgoc, L., Delingette, H., Criminisi, A., Ayache, N.: Sparse Bayesian registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, New York, pp. 235–242 (2014)
Cotin, S., Delingette, H., Ayache, N.: Real-time elastic deformations of soft tissues for surgery simulation. IEEE Trans. Vis. Comput. Graph. 5(1), 62 (1999) DOI: 10.1109/2945.764872
Bui, H.P., Tomar, S., Courtecuisse, H., Cotin, S., Bordas, S.: Real-time error control for surgical simulation. IEEE Trans. Biomed. Eng. (2017). http://ieeexplore.ieee.org/abstract/document/7932498/
Bui, H.P., Tomar, S., Courtecuisse, H., Audette, M., Cotin, S., Bordas, S.: Controlling the error on target motion through real-time mesh adaptation: applications to deep brain stimulation. arXiv preprint arXiv:1704.07636 (2017)
Haouchine, N., Dequidt, J., Peterlik, I., Kerrien, E., Berger, M.O., Cotin, S.: Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In: 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, pp. 199–208 (2013)
Beger, R.D., Dunn, W., Schmidt, M.A., Gross, S.S., Kirwan, J.A., Cascante, M., Brennan, L., Wishart, D.S., Oresic, M., Hankemeier, T., et al.: Metabolomics enables precision medicine:"a white paper, community perspective". Metabolomics 12(10), 149 (2016) DOI: 10.1007/s11306-016-1094-6
Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. 2011, 1 (2011) DOI: 10.1155/2011/154798
Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p. 1818 (2012)
Cerrone, A., Hochhalter, J., Heber, G., Ingraffea, A.: On the effects of modeling as-manufactured geometry: toward digital twin. Int. J. Aerosp. Eng. 2014, 10 (2014) DOI: 10.1155/2014/439278
West, T.D., Pyster, A.: Untangling the digital thread: the challenge and promise of model-based engineering in defense acquisition. INSIGHT 18(2), 45 (2015) DOI: 10.1002/inst.12022
Gabor, T., Belzner, L., Kiermeier, M., Beck, M.T., Neitz, A.: A simulation-based architecture for smart cyber-physical systems. In: 2016 IEEE International Conference on Autonomic Computing (ICAC). IEEE, pp. 374–379 (2016)
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, New York (2017)
Tkatchenko, A., Scheffler, M.: Accurate molecular van der Waals interactions from ground-state electron density and free-atom reference data. Phys. Rev. Lett. 102(7), 073005 (2009) DOI: 10.1103/PhysRevLett.102.073005
Montavon, G., Hansen, K., Fazli, S., Rupp, M., Biegler, F., Ziehe, A., Tkatchenko, A., Lilienfeld, A.V., Müller, K.R.: Learning invariant representations of molecules for atomization energy prediction. In: Advances in Neural Information Processing Systems, pp. 440–448 (2012)
Rupp, M., Tkatchenko, A., Müller, K.R., Von Lilienfeld, O.A.: Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108(5), 058301 (2012) DOI: 10.1103/PhysRevLett.108.058301
Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.R., Von Lilienfeld, O.A.: Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15(9), 095003 (2013) DOI: 10.1088/1367-2630/15/9/095003
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., Von Lilienfeld, O.A., Müller, K.R., Tkatchenko, A.: Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 6(12), 2326 (2015) DOI: 10.1021/acs.jpclett.5b00831
Kerfriden, P., Schmidt, K.M., Rabczuk, T., Bordas, S.P.A.: Statistical extraction of process zones and representative subspaces in fracture of random composites. Int. J. Multiscale Comput. Eng. 11(3), 253 (2013) DOI: 10.1615/IntJMultCompEng.2013005939
Mueller, T., Kusne, A.G., Ramprasad, R.: Machine learning in materials science: recent progress and emerging applications. Rev. Comput. Chem. 29, 186 (2016)
Antony, P., Manujesh, P., Jnanesh, N.: Data mining and machine learning approaches on engineering materials—a review. In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, pp. 69–73 (2016)
Viti, F., Verbeke, W., Tampère, C.: Sensor locations for reliable travel time prediction and dynamic management of traffic networks. Transp. Res. Rec. J. Transp. Res. Board 2049, 103 (2008) DOI: 10.3141/2049-12
Fonzone, A., Schmöcker, J.D., Viti, F.: New services, new travelers, old models? Directions to pioneer public transport models in the era of big data. J. Intell. Transp. Syst. 20(4), 311–315 (2016). 10.1080/15472450.2016.1190553 DOI: 10.1080/15472450.2016.1190553