Computational Biology

Computational Biology
Students will develop skills in preparing, analyzing, and interpreting biological data sets so they can make well-supported scientific conclusions and effectively evaluate scientific conclusions made by others. Students will become familiar with genomic, transcriptomic, phenotypic and environmental data and will apply appropriate computational tools for analyzing data and execute algorithms effectively.
 Hours3.0 Credit, 2.0 Lecture, 1.0 Lab
 PrerequisitesBIO 165 & C S 240
 ProgramsContaining BIO 365
Course Outcomes: 

Outcome 1: Prepare diverse types of biological research data for analysis.

Students will be able to classify and describe diverse types of biological research data including genomic, transcriptomic, phenotypic and environmental data. They will recognize data formats that are used to store such data, will be able to identify appropriate computational tools for preparing the data for analysis, and will be able to execute those tools effectively.

Outcome 2: Become proficient with quantitative methods for analyzing biological data.

Students will understand concepts and techniques for quantitatively evaluating biological data and discriminating which statistical test(s) and algorithms are most appropriate for analyzing the data. Using software that is widely used in biological research, they will be able to execute statistical tests and algorithms, create publication-quality graphics based on the data, and enable others to reproduce their analyses.

Outcome 3: Be able to critically evaluate prior biological research studies effectively.

Students will demonstrate proficiency in reading, interpreting, and discussing research studies that use bioinformatics techniques.