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ISC 1057 Computational Thinking |
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Student / TBD
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| This introductory course considers the question of how computers have come to imitate many kinds of human intelligence. The answer seems to involve our detecting patterns in nature, but also in being able to detect patterns in the very way we think. We will look at some popular computational methods that shape our lives, and try to explain the ideas that make them work. This course has been approved to satisfy the Liberal Studies Quantitative/Logical Thinking requirement. |
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ISC 2310 Introduction to Computational Thinking in Data Science with Python |
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Janet Peterson
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| This course investigates strategies behind popular computational methods used in data science. In addition, many of the algorithms are implemented using the programming language Python. No prior programming experience is required so the course presents the basics of the Python language as well as how to leverage Python’s libraries to solve real-world problems in data science. Prerequisite: MAC 1105 or equivalent. |
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ISC 3313 Introduction to Scientific Computing with C++ |
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M W F 1:20-2:10 (152 DSL)
TBD
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| This course introduces the student to the science of computations. Topics cover algorithms for standard problems in computational science, as well as the basics of an object-oriented programming language, to facilitate the student’s implementation of algorithms. The computer language will be Java. Prerequisite: MAC 2311. |
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ISC 4220C Continuous Algorithms for Science Applications |
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M W F 9:20-10:10 (152 DSL), T 3:05-5:35 (Lab, 152 DSL)
Sachin Shanbhag
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| Basic computational algorithms including interpolation, approximation, integration, differentiation, and linear systems solution presented in the context of science problems. The lab component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results. Corequisite: ISC 3222; Prerequisite: MAC 2312. |
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ISC 4245C / CAP 5771 Data Mining |
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M W 12:00-1:15 (499 DSL)
Anke Meyer-Baese
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| This course enables students to study concepts and techniques of data mining, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining. Prerequisites: COP 3330, ISC 3222, ISC 3313 or ISC 4304, or instructor permission. |
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ISC 4302 / ISC 5307 Scientific Visualization |
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M W F 12:20-1:10, 152 DSL
Xiaoqiang Wang
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| This course enables students to study concepts and techniques of data mining, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining. Prerequisites: COP 3330, ISC 3222, ISC 3313 or ISC 4304, or instructor permission. |
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ISC 4304C Programming for Science Applications |
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T R 9:45-11:00 (152 DSL), F 3:05-5:35 (Lab, 152 DSL)
Peter Beerli
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| Provides knowledge of a scripting language that serves as a front end to popular packages and frameworks, along with a compiled language such as C++. Topics include the practical use of an object-oriented scripting and compiled language for scientific programming applications. There is a laboratory component for the course; concepts learned are illustrated in several science applications. Prerequisites: MAC 2312, COP 3014 or ISC 3313. |
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ISC 4420 / ISC 5425 Introduction to Bioinformatics |
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M W F 1:20-2:10 (499 DSL)
Alan Lemmon
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| Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. Designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Lab exercises reinforce the classroom learning. |
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ISC 4933 / ISC 5227 Survey of Numerical Partial Differential Equations |
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T R 11:35-12:50 (152 DSL)
Tomasz Plewa
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| This course provides an overview of the most common methods used for numerical partial differential equations. These include techniques such as finite differences, finite volumes, finite elements, discontinuous Galerkin, boundary integral methods, and pseudo-spectral methods. |
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ISC 4933 / ISC 5318 High-Performance Computing |
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T R 9:45-11:00 (499 DSL)
Xiaoqiang Wang
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| Introduces high-performance computing, which refers to the use of parallel supercomputers, computer clusters, as well as software and hardware to speed up computations. Students learn to write faster code that is highly optimized for modern multi-core processors and clusters, using modern software development tools and performance analyzers, specialized algorithms, parallelization strategies, and advanced parallel programming constructs. Prerequisite: ISC 5305 or equivalent or instructor permission. |
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ISC 4943 Practicum in Computational Science |
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M W 10:40-11:55 (499 DSL)
Anke Meyer-Baese
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| This practicum allows students to work individually with a faculty member throughout the semester and meet with the course instructor periodically to provide progress reports. Written reports and an oral presentation of work are required. May be repeated to a maximum of six semester hours, with a maximum of only three semester hour credits allowed to be applied to the Computational Science degree. |
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ISC 5316 Applied Computational Science II |
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T R 1:20-2:10 (422 DSL), R 3:05-5:35 (Lab, 152 DSL)
Tomasz Plewa
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| Provides students with high performance computational tools to investigate problems in science and engineering with an emphasis on combining them to accomplish more complex tasks. Topics include numerical methods for partial differential equations, optimization, statistics, and Markov chain Monte Carlo methods. Prerequisite: ISC 5315. |
| File | Description | File size |
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 spring2022.xlsx | 13 kB | |
 2022spring-all.pdf | 3418 kB |
