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Issue No.02 - March-April (2012 vol.14)
pp: 84-89
ABSTRACT
<p>A research course for juniors and seniors has been designed to offer students a chance to work with the tools of computational science for large, data-intensive computations on publicly available datasets.</p>
INDEX TERMS
Environmental data mining, linear model, fitting, smoothing, scientific computing
CITATION
Jessica Howard, Omar Padron, Patricia Morreale, David Joiner, "Applications of Computational Science: Data-Intensive Computing for Student Projects", Computing in Science & Engineering, vol.14, no. 2, pp. 84-89, March-April 2012, doi:10.1109/MCSE.2012.18
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7. B.S. Everitt, Cambridge Dictionary of Statistics, 2nd ed., Cambridge Univ. Press, 2002.
8. J.W. Eaton, GNU Octave Manual, Network Theory, 2002.
9. R Development Core Team, "R: A Language and Environment for Statistical Computing," R Foundation for Statistical Computing, 2008; www.R-project.org.
10. R.H. Landau, M.J. Paez, and CC. Bordeianu, A Survey of Computational Physics, Princeton Univ. Press, 2008.
11. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, "Optimization by Simulated Annealing," Science, vol. 220, no. 4598, 1983, pp. 671–680.
12. A. Robbins, The GNU Awk User's Guide, Free Software Foundation, 2011; www.gnu.org/software/gawk/manualgawk.html .
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