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Computational Science Certificate
Program type:
Certification
Format:
On Campus
Est. time to complete:
2-3 semesters
Credit Hours:
15
Discover the latest techniques and use technology to manage data for research and
solve complex problems in your area of expertise.
The Computational Science certificate provides students a broad knowledge base in
problem-solving using contemporary computational methods and tools, as well as specialized
experience in modeling and solving complex problems in a particular focus area. Computational
and data-intensive methods have become an essential aspect of all scientific disciplines.
This certificate provides students with competitive skills whether they intend to
pursue graduate education or a career in the industry.
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Request More InfoWhy Earn a Computational Science Certificate?
The University of North Texas offers upper-division undergraduate academic certificates to meet workforce needs or to provide students with life/career skills and knowledge and to allow for specialization in academic disciplines.
The computational science certificate will teach you the technology and programming solutions to solve problems in your focus area. Students can specialize in math, physics, biology, or chemistry and graduate career-ready with an in-demand skill set.
Computational Science Certificate Courses You Could Take
Data Analytics and Computational Statistics 1 (3 hrs)
Provides an overview of quantitative methods essential for analyzing data, with an
emphasis on science and industry applications. Topics include identification of appropriate
metrics and measurement methods, descriptive and inferential statistics, experimental
design, parametric and non-parametric tests, simulation, and linear and logistic regression,
categorical data analysis, and select unsupervised learning techniques.
Principles of Data Visualization for Large Data (3 hrs)
Principles and methods for effective visualization and communication of large data
sets. Standard and open source data visualization packages are used to develop presentations
that convey findings, answer science and industry questions, drive decisions, and
provide persuasive evidence supported by data.
Fourier Analysis (3 hrs)
Application-oriented introduction to Fourier analysis, including Fourier series, Fourier
transforms, discrete Fourier transforms, wavelets, orthogonal polynomials and the
Fast Fourier Transform (FFT) algorithm. The theoretical portions of the course emphasize
interconnections and operator algebraic formalism.
Introduction to Computational Chemistry (3 hrs)
Introduction to the use of modern computational methodologies for the study of physical
properties and chemical reactions of importance in chemistry, biochemistry, molecular
biology and environmental sciences.
Biocomputing (3 hrs)
Introduction to computational problems inspired by the life sciences and overview
of available tools. Methods to compute sequence alignments, regulatory motifs, phylogenetic
trees and restriction maps.
Physics, Computation and Software Applications (3 hrs)
A basic survey of selected topics at the intersection of computer science, engineering
and physics. Student will learn computer programming for applications in physics as
well as the physics underlying computation and its physical implementation. Topics
include automated control in experimental physics, symbolic computation/analysis,
simulation of physical phenomena; physical basis of contemporary computers and computation;
physical constraints with respect to size, speed, energy and architecture; classical
and quantum computation and implementations.
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