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From CDS101
1. Introduction
Introduction to Computational and Data Sciences - CDS 101
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Computational Science is an emerging field involving applications of sophisticated computational techniques to build models and solve problems related to science and engineering. It complements existing theoretical and experimental approaches and may be thought of as a new mode of scientific inquiry. Students with a wide interest in computers and sciences will benefit from the BS degree in Computational and Data Sciences. In particular, students who are interested in the sciences but do not want to go deep into one particular program, will find this program useful. The students in this program will be exposed to a wide range of computational science applications, and will learn computational science tools, high-performance computing, applied and computational methods, modeling and simulation, visualization tools. The students graduated from this program will acquire interdisciplinary knowledge and apply scientific principles in solving real-world problems. As a result, they will be better prepared for future employment in industry, research, and academia.
Recently emerged but very well-funded interdisciplinary areas of chemical, physical and biological sciences (such as biotechnology, nanotechnology, molecular electronics, photonics in nanoscale systems, and energetics of DNA/protein binding) require highly-qualified professionals with strong computational skills in order to work closely with experimentalists in solving modern scientific or engineering problems. Students graduating with a traditional discipline-based bachelor’s degree in biology, chemistry, mathematics, or physics generally do not have the required computational background necessary to participate as members of interdisciplinary scientific research teams. The BS program in CDS will provide students with a variety of opportunities to become research professionals possessing interdisciplinary knowledge, including science, mathematics, and strong computational skills. Graduates of the proposed program will be able to perform data analysis, optimization, and computational simulation for solving problems in science and engineering.
Wallin's talk from Supercomputing 09 on GMU's CDS program
2. Syllabus
3. Homework
3.1. Homework #1 Due Tuesday September 8 - 1:30
3.2. Homework #2 Due Tuesday September 15 - 1:30
3.3. Homework #3 Due Tuesday September 22 - 1:30
3.4. Homework #4 Due Thursday October 1 - 1:30
3.5. Homework #5 Due Thursday October 8 - 1:30
3.6. Homework #6 Due Tuesday November 3 - 1:30
3.7. Homework #7 Due Tuesday November 10 - 1:30
3.8. Homework #8 Due Tuesday December 8 - 1:30
3.9. Water Temperature Excel Spreadsheet From October 23, 2009
4. Lectures
4.1. Lecture 1 The Computational and Data Sciences
4.2. Lecture 2 The Scientific Method
4.3. Lecture 3 How Computers Work
4.4. Lecture 4 How Computers Work
4.5. Lecture 5 Introduction to Octave
4.6. Lecture 6 Introduction to Octave
4.7. Lecture 7 Sensors and Scientific Measurement
4.8. Lecture 8 Signal to Noise Ratios
4.9. Lecture 9 Signal Processing
4.10. Lecture 10 Overview of Scientific Data
4.11. Lecture 11 Scientific Databases
4.12. Lecture 12 Computer Models - Using mathematics and algorithms to represent reality
4.13. Lecture 13 Models using Simple Differential Equations
4.14. Lecture 14 ODE Solvers and Coupled Differential Equations
4.15. Lecture 15 Verification and Validation
4.16. Lecture 16 The Predator-Prey Problem
4.17. Lecture 17 Scientific Visualization and Optical Perception
4.18. Lecture 18 Introduction to High Performance Computing
4.19. Lecture 19 Parallel Computing
5. Study Material
6. Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. 0737091.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation."
