Denison - DePauw - Furman - Harvey Mudd - Middlebury - Rhodes - Scripps - Vassar
June 4 - 5, 2009 at Denison University
This workshop was sponsored by a Mellon Faculty Career Enhancement grant.
The central objective of the workshop was to develop a deeper understanding among science faculty members of the uses of mathematics and computer science in the natural sciences and an equally crucial appreciation among computer science faculty for work in the natural sciences and the broad utility of computer science across a significant portion of most undergraduate curricula. We are not solely interested in what computing skills our introductory computer science courses should provide for science students; we are interested in a broader view. We want to identify concepts that are important for students of both computer science and the natural sciences, encouraging a two-way exchange of these ideas.
Computation for Scientists
May 31 - June 5, 2010 at Denison University
We hosted 2 short courses for science faculty to learn more about computation in the framework of scientific teaching and research, and better position them for future work in their increasingly computational disciplines. Ideally, each participant will leave their short course having developed a module that can be used in one of their courses or their research and shared with others.
This proposal is a concrete outcome of the Mellon FCE workshop, "Computing and Mathematics Across the Sciences," held at Denison in June, 2009. Another goal of this project is to continue the fruitful conversations about interdisciplinary computation begun at that workshop.
Programming in R
Instructors: Daniel Kaplan (Math and Computer Science, Macalester College) and Matthew Landis (Biology, Middlebury College)
Programming in Python
Instructors: Elizabeth Sweedyk (Computer Science, Harvey Mudd College) and David Goodwin (Geosciences, Denison University)
This project was supported by grants from the Mellon Faculty Career Enhancement Initiative and the GLCA New Directions Initiative.
Resources
Books and Reports
Introduction to Scientific Computation and Programming
Introduction to Statistical Modeling
Discovering Genomics, Proteomics, and Bioinformatics
Introductory Statistics with R
Python Programming in Context
Practical Programming: An Introduction to Computer Science Using Python
BIO 210
Math & BIO 2010: Linking Undergraduate Disciplines
Curriculum Renewal Across the First Two Years (CRAFTY)
Distinctively American: The Residential Liberal Arts College
Academic Programs
Computational Science at Wittenberg
Genomics at Davidson
Integrated Quantitative Science at Richmond
Computational Studies at Capital University
Integrated Science at Princeton
Interdisciplinary Computer Science at Virginia
Genomics at Wheaton
Courses
Math 155: Introduction to Statistical Modeling (Macalester)
Math/CS 365: Scientific Computation (Macalester)
COS 323: Computing for the Physical & Social Sciences (Princeton)
CS 112: Computation for the Sciences (Wellesley)
STAT 598Z: Concepts in Computing with Data (Purdue)
Software
R Project
Stella
Python
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