Certificate in Bioinformatics

The Biological Sciences have become more and more data intensive. Many biological biochemistry experiments, including genomic sequencing, gene expression experiments, Nuclear Magnetic Resonance, Mass Spec, Etc., generate huge quantities of data. This certificate ensures that the student has the computational skills necessary to analyze and manipulates such large quantities of data.

Professional Certificate - Bioinformatics

College Humanities & Sciences

Catalog Year: 2016-2017

Degree Specific Credits: 12

Required Cumulative GPA: 2.0

Required Courses

Rule: 6 Credits

Show All Course Descriptions Course Credits
Show Description CSCI 135 - Fund of Computer Science I
Offered autumn and spring. Prereq., computer programming experience in a language such as BASIC, Pascal, C, etc. Fundamental computer science concepts using the high level structured programming language, Java.
3 Credits
Show Description CSCI 250 - Computer Mdlng/Science Majors
Offered autumn. Prereq., basic computer and spreadsheet literacy; coreq., M 162 or 171.  An introduction to programming in Python with an emphasis on problems arising in the sciences, including: function plotting, data fitting, file input/output, solving ordinary differential equations, matrix manipulation, and sensor networks. A student can take at most one of CSCI 172, CSCI 250, CRT 280, and CRT 281 for credit.
3 Credits
Show Description CSCI 451 - Computational Biology
Offered Autumn. Designed for attendance by both computer scientists and biologists. The course will explore the interdisciplinary nature at the juncture of the two fields. Students will be introduced to bioinformatics (emphasis: computational genomics), with exposure to fundamental problems, algorithms, and tools in the field. This includes a basic introduction to genomics, along with in-depth coverage of algorithms and methods relevant to modern computational genomics, including: biological sequence alignment, sequence database homology search, and phylogeny inference. The programming expectations are limited for a 400-level computer science course, but at least one semester of a programming-intensive course is required. Credit not allowed for CSCI 558 and this course
3 Credits
Minimum Required Grade: C- 6 Total Credits Required

Elective Courses

Rule: Student must complete one of the following courses

Note: BIOB 488 may fulfill this requirement if not taken as part of the core.

Show All Course Descriptions Course Credits
Show Description BCH 480 - Advanced Biochemistry I
Offered autumn. Prereq., CHMY 223. Primarily for science majors. The chemistry of biomolecules, with emphasis on the structure and function of proteins, carbohydrates, lipids and nucleic acids. The chemistry and regulation of the transfer and expression of genetic information, protein synthesis. Credit not allowed for both BCH 380 and 480-482.
3 Credits
Show Description BIOB 486 - Genomics
Offered autumn. Prereq., BIOB 272. Principles and mechanisms of genome biology of animals and microbes, including genome function, evolution, and basic molecular and computational methodology used in genome biology.
3 Credits
Show Description BIOB 488 - Programming for Biology
Offered spring. Prereq., BIOB 486 or A- or higher in BIOB 272. An introduction to computer programming using genomic and evolutionary examples. No prior programming experience expected or required.
3 Credits
Show Description CSCI 444 - Data Visualization
Offered intermittently. Prereq., M 171; programming experience; and junior, senior, or graduate status; or consent of instr. Visualization fundamentals and applications using special visualization software; formulation of 3-D empirical models; translation of 3-D models into graphical displays; time sequences and pseudo-animation; interactive versus presentation techniques; special techniques for video, CD and other media.
3 Credits
Show Description CSCI 448 - Pattern Recognition
Offered intermittently. Prereq., Junior or Senior status. Introduction to the framework of unsupervised learning techniques such as clustering (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation. Emphasis will be on the underlying algorithms and their implementation. Credit not allowed for both CSCI 448 and CSCI 548.
3 Credits
Minimum Required Grade: C- 6 Total Credits Required