Computational Biology and Bioinformatics Certificate - Curriculum
The online Graduate Certificate in Data Science in Computational Biology and Bioinformatics consists of 18 credit hours and can be completed in as little as 12 months.*
Students without undergraduate coursework in Data Science or a related field can still be accepted, but may need take up to two courses (6 credit hours) to provide the required foundation for them to pursue advanced study.**
BIOL-50900 Introduction to Computational Biology (3)
This course will cover the computational techniques used to access, analyze, and interpret the biological information in common types of biological databases and the biological questions that can be addressed by such methods, applicable to the study of the context of genes within the same genome and across different genomes, the study of molecular sequence data for the purpose of inferring the function, interactions, evolution and structure of biological molecules, and the study of annotation and ontology.
BIOL-51000 Data Systems in the Life Sciences (3)
This is a continuation of BIOL-50900. Students will examine how bioinformatics, statistics and computation are being used to support the discovery of new biomedical knowledge and learn the basics of computational methods used to analyze molecular sequences and structures.
CPSC-51000 Introduction to Data Mining and Analytics (3)
Overview of the field of data mining and analytics; large-scale file systems and Map-Reduce, measures of similarity, link analysis, frequent item sets, clustering, e-advertising as an application, recommendation systems.
CPSC-51100 Statistical Programming (3)
Programming structures and algorithms for large-scale statistical data processing and visualization. Students will use commonly available data analysis software packages to apply concepts and skills to large data sets and will also develop their own code using an object-oriented programming language.
CPSC-53000 Data Visualization (3)
The theory and practice of visualizing large, complicated data sets to clarify areas of emphasis. Human factors best practices will be presented. Programming with advanced visualization frameworks and practices will be demonstrated and used in group programming projects.