TOOLS FOR DATA ANALYSIS

ABT 775

Course Description

Using a variety of existing and emerging bioinformatics tools and computational methods, emphasizes hands-on experiences analyzing and interpreting large data sets (e.g. genomic, proteomic, microbiomics, interactome, target discovery). Evaluate and adapt existing computational approaches for specific use in solving a problem in biotechnology.

Prerequisties

ABT 700 and ABT 715

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average

3.89

1.11% from Historical

Completion Rate

100%

No change from Historical

A Rate

77.78%

1.11% from Historical

Class Size

176.92% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

BIOINFORMATICS FOR BIOLOGISTS

ONCOLOGY 778

Become familiar with bioinformatics theory and principles. Provides real-world experience that can be applied to your own work. Provides a foundation of knowledge that can be used to critically evaluate existing bioinformatics tools that can be used in your work, and in the absence of an appropriate tool, identify the analyses that demand the development of novel tools.

APPLICATION OF BIOINFORMATICS

ABT 785

Exploration and application of existing bioinformatic tools. Implementation of pre-coded solutions to data acquisition, wrangling, analysis, visualization, and structural modeling problems. Complete a project that generates a multi-system workflow to solve bioinformatic problems.

BIOINFORMATIC INQUIRY

ABT 780

Advances the development of competencies promoting efficient analysis of biological data. Emphasizes matching a research problem with the most effective tools for its completion, balancing use of existing software and de novo software development. Advanced aspects of Python and R, algorithmics, machine learning, simulations, and effective communication of results are emphasized.

PYTHON FOR BIOINFORMATICS

ABT 730

Introduce diverse strategies for computational analysis of macromolecular data using Python including sequence alignment, genome annotation, data retrieval from databases, phylogenetic analysis, and molecular evolution. Experiential learning is emphasized; confidence in practical skills is developed through persistent application of course content to projects focused on current problems in bioinformatic research.

EXPERIMENTAL DESIGN AND ANALYSIS IN BIOTECHNOLOGY

ABT 720

Principles of descriptive and inferential statistics with applications in biotechnology including experimental design, quantitative data analysis, and bioinformatic evaluation of complex molecular and biological data sets.