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Danny Thorne

Developing Computational Science Curriculum using the World Wide Web - 0 views

flux.aps.org/...SJ0203.html

computationalscience course curriculum

shared by Danny Thorne on 02 Mar 08 - Cached
  • Two new courses also involve substantive use of the World Wide Web in teaching the material of the course and in developing web tools which can be used to develop the course materials and their hypertext environment.
Danny Thorne

ANU - Bachelor of Computational Science (BComptlSci) - 0 views

bcomptlsci.anu.edu.au

computationalscience curriculum degree

shared by Danny Thorne on 02 Mar 08 - Cached
  • In many branches of science, computation is given equal weight to theory and experiment.
  • "I think the key thing that got me this job was my solid training in finite difference methods, Monte Carlo methods, PDEs etc etc... They've been looking for someone with my exact training for some time now!"
  • The degree program is built on a core of advanced mathematics and computer science courses linked with a specialist area of science. These core courses provide the training in the formulation, analysis, modelling and simulation of problems in science, engineering, commerce and industry. Typical areas of specialisation are physics, chemistry, biology, geology, geography, environmental sciences, applied mathematics, astrophysics, economics, finance and computer science. In this way the general mathematical and computing skills obtained from the core courses can be applied in a sophisticated manner in a specialisation area. Students will take two computational science extension activities which aim to introduce students to the application of computational science to scientific and industrial problems.
Danny Thorne

Computational Science in the Mathematics Curriculum - 0 views

www.maa.org/...launchings_07_05.html

computationalscience curriculum

shared by Danny Thorne on 02 Mar 08 - Cached
  • Computational science is the orphan of the sciences. Viewed as a collection of tools rather than as a discipline, it falls somewhere between Mathematics and Computer Science, usually fitting the self-image of neither discipline and therefore neglected. But it is of critical importance to many disciplines, among them Biology.
  • Biology is becoming much more mathematical, but this is not the mathematics that I was taught as an undergraduate. It is the mathematics of data analysis and modeling made possible by the computing power that is now available. It is rooted in computational science.
  • Most of these themes hold for all of the sciences as well as many of the social sciences.
  • ...2 more annotations...
  • “departments of mathematical sciences can and > should play a central role in their institutions’ undergraduate programs” > [3] > . > Such a role requires that our departments support computational science and find ways of engaging it in our curricula.
  • We are a joint department of Mathematics and Computer Science, and so the proper home for computational science is not at issue. Our first course in computational science, Introduction to Scientific Programming, is listed as Computer Science but is required of Mathematics majors. The mathematics curriculum has begun to draw on computational science to enrich and motivate its material, especially in calculus and statistics. It is not yet clear how far we will go in incorporating computational science into mathematics. It is not clear how far we should go. But we do recognize that if we are to live up to the vision of a Mathematics department that plays a central role in the undergraduate programs of our institution, then the role of computational science is something that we need to consider very seriously.
Danny Thorne

SC02.notes.pdf (application/pdf Object) - 0 views

www.cs.csustan.edu/...SC02.notes.pdf

computationalscience course

shared by Danny Thorne on 02 Mar 08 - No Cached
  • Danny Thorne on 02 Mar 08
     

Danny Thorne

University of Hawaii at Manoa Catalog - 0 views

www.catalog.hawaii.edu/...math.htm

computationalscience courselist curriculum

shared by Danny Thorne on 03 Mar 08 - Cached
  • MATH 304 Mathematical Modeling I: Deterministic Models (4) Deterministic mathematical modeling emphasizing models and tools used in the biological sciences. Topics include difference equations, qualitative behavior solutions of ODEs and reaction-diffusion equations. A computer lab is included. Pre: 216 or 242 or 252A, or consent. Fall only. MATH 305 Mathematical Modeling II: Probabilistic Models (4) Probabilistic mathematical modeling emphasizing models and tools used in the biological sciences. Topics include stochastic and Poisson processes, Markov models, estimation, Monte Carlo simulation and Ising models. A computer lab is included. Pre: 216 or 242 or 252A, or consent. Recommended: 304. Spring only.
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