RRSCS Minor Program Courses [Click here for a printable list of courses]Capital University • Central State University • Columbus State Community College • Kent State University • Miami University • The Ohio State University • Ohio University • Owens Community College • Sinclair Community College • Stark State Community College • University of Cincinnati • Wittenberg University • Wright State University Capital UniversityCSAC 245 Introduction to Computational Science Prerequisites: None Description: An introduction to the problems and solution methodologies in computational science. Computational tools such as a computer algebra system, a high performance computing engine, visualization software and Internet resources will used to explore and solve mathematical problems drawn from various fields of science. Dr. Patrick Shields Central State UniversityTo be announced Columbus State Community CollegeMATH 299 Special Topics: Differential Equations and Discrete Dynamical Systems Prerequisites: Math 153 Calculus III and an introductory modeling course Instructor: Kent State UniversityBSCI-50/70195, BSCI 40195/ BTEC-40220 ST: Bioinformatics Prerequisites: Eighteen hours of biology and permission of instructor. Description: Learn to use GenBank, Ensembl and other genomic databases, Construct multiple sequence alignments, Phylogenetic tree reconstruction, Comparative and evolutionary genomics, Microarray data analysis, Protein structure prediction and much more… Instructor: Miami UniversityCSA 443 High Performance Computing and Parallel Programming Prerequisites: 1) CSA-278 (Computer Architecture) or equivalent 2) Knowledge of Java/C/C++ programming Description: An introduction to practical use of multi-processor workstations and supercomputing clusters, using parallel algorithms and concurrent data structures, for solving computational problems in a variety of science and engineering domains. The course builds on basic concepts of programming and problem solving. Instructor: The Ohio State UniversityChem 644 Computational Chemistry Prerequisites: Prerequisites: Chem 252 (Organic Chemistry II) Description: To provide a practical introduction to the theory and methods of molecular modeling and computational chemistry, focusing on its use for experimentalists. Hands-on experience will be obtained by all attendees in doing molecular mechanics, semi-empirical, ab initio quantum chemistry, density functional theory and modeling dynamic systems (molecular dynamics and kinetics).This is meant to be an introduction to molecular modeling for undergraduates, not a course on Quantum Mechanics. Instructor: CSE 694L Data and Information Visualization Description Prerequisites: The students will be best served if they possess some basic programming experience. Prior knowledge of C++, or Java, or MATLAB will help the students to gain much from the lectures and examples. Description: This course will provide a basic introduction to the science and the underlying technology of visualization. The following topics will be studied – the role of perception in visualization, the importance of good design practices, the construction of interactive tools for data and information visualization, and the application of visualization techniques on measured data from the medical and biological sciences and simulated data from the physical sciences and engineering. Scalar and vector data visualization techniques along methods for visualizing trees, clusters, and interconnection networks will be studied. Case studies and examples will be considered giving the course an application-focus. Hands-on programming experience and the design of interfaces will be stressed throughout the class and thereby providing the students a practical emphasis. Instructor: Ohio UniversityCS/PBIO 416/516 Problem Solving with Bioinformatics Tools Prerequisites: CS 361 (Data Structures) or PBIO 330/BIOS 325 (Genetics) Description: Computation has become integral and critical to research in the life sciences. Biotechnology researchers produce vast quantities of data that require detailed analysis. In addition, numerous biological data repositories offer an overwhelming amount of information. This course will provide an opportunity to learn about bioinformatics software tools that enable the efficient analysis of biological data. Students will acquire important skills that (1) are required by employers in the growing field of biotechnology, and (2) are necessary for successful research in the life sciences. The course will provide a unique learning environment. It will bring together students from the life sciences, computer science, engineering, mathematics, and other related fields. It will offer perspectives from faculty in the fields of biology and computer science. Classroom activities will focus on employing state-of-the-art bioinformatics tools to collaboratively solve a set of biological research problems. Students will become familiar with the capabilities of popular bioinformatics tools, and with the kind of information contained in popular biological databases. Participants will also gain insight into how bioinformatics tools and biological databases are used in multidisciplinary biological research and experimentation processes. Instructor: CS 412 Parallel Computing Prerequisites: Introductory "Algorithms and Data Structures" course Description: This course is a practical-oriented introduction to Parallel Computing. It aims to teach students an understanding of the complex interactions between software and hardware in parallel and distributed system. Upon completion of this course, the student should be familiar with fundamental aspects of parallel and distributed systems, taxonomies, performance measures, and theoretical limitations of parallel systems. Students will understand parallel programming languages and middleware, and will be able to design and implement efficient parallel applications on a variety of parallel architectures. The course will be accompanied by a number of programming projects and exercises including, but not limited to, bioinformatics applications and case studies. Instructor: Owens Community CollegeTo be announced Sinclair Community CollegePHY 212 Introduction to Modeling and Simulation Prerequisites: None Description: A variety of scientific problems will be analyzed by developing representative models, implementing the models, verifying and validating the model, reporting on the models in oral and written form, and by changing the models to reflect corrections, improvements and enhancements. Systems to be modeled include first and second order dynamic systems and random processes that utilize Monte Carlo simulations, random walk simulations and cellular automation simulations. Instructor: PHY 220 Introduction to Computational Physics Prerequisites: PHY 201 and MAT 201 or equivalent Description: Mathematical models of physical systems will be developed and simulations will be constructed using Matlab and Vensim. Explorations of complex systems will be conducted and results will be presented in oral and written form. Activities include the study of projectile motion, harmonic motion of mass-spring systems, LRC circuits, Fourier analysis of signals, modeling empirical data, assessment of numerical techniques, and the survey of Monte Carlo techniques in physics. Instructor: Stark State Community CollegeCST120 Computational Science Methods Prerequisites: None Description: Develop the necessary computational skills to model and simulate a broad set of deterministic and stochastic systems including the modeling of empirical data. Integrated problem solving methods found in modern research facilities and high technology workplaces will be utilized. Instructor: CST121 Introduction to Modeling and Simulation Prerequisites: None Description: A variety of scientific problems will be analyzed by developing representative models, implementing the models, verifying and validating the model, reporting on the models in oral and written form, and by changing the models to reflect corrections, improvements and enhancements. Systems to be modeled include first and second order dynamic systems and random processes that utilize Monte Carlo simulations, random walk simulations and cellular automation simulations. Instructor: University of Cincinnati20-CS-668 Parallel Computing Prerequisites: 20-CS-228 or Permission of Instructor Description: This class is designed as an introduction to the concepts and practice of Parallel Computing. In this class students will be introduced to some of these tools, techniques, and methods of analysis in parallel computing. We will do a number of programming projects during the term. This course is designed as a dual level/senior undergraduate level course covering the programming and algorithmic design issues arising in parallel computing. The course is designed to meet the competencies for Area 6 for the Minor Program in Computational Science of the Ralph Regula School of Computational Science. The following competencies are addressed in this course: Describe the fundamental concepts of parallel programming and related architectures. Demonstrate parallel programming concepts using MPI. Demonstrate knowledge of parallel scalability. Demonstrate knowledge of parallel programming libraries. Instructor: 15PHYS410 Computational Physics Prerequisites: 15MATH273 (Differential Equations) Description: A major portion of the course will be devoted to the numerical solution of partial differential equations with an emphasis on topics (such as the quantum mechanics of nano-structures) that are of current interest in physics and engineering. We will use Mathematica as our primary programming language although we will have to write a little FORTRAN or C if we run a problem on a cluster. Topics to be covered include: Numerical integration, Equation solving, Solving ordinary differential equations, Solution of boundary value and eigen value problems, Numerical solution of partial differential equations, Monte Carlo methods, An introduction to high performance computing (time permitting), Topics of interest to the class. Instructor: Wittenberg UniversityCOMP 345 Optimization Prerequisites: Calculus I and Introduction to programming. Familiarity with computational models and methods will be a benefit. Instructor: Wright State UniversityTo be announced
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