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BIOL 432 Computation and Big Data in Biology Units: 3.00
Application of basic coding and analytical methods to obtain, organize, analyze, visualize, and interpret information from large, complex datasets (i.e. 'Big Data') in biology. Datasets may include climate/weather records, 'omics' data, specimen collections, long-term observational studies, journal articles, and other historical and online sources.
Learning Hours: 120 (36 Lecture, 12 Tutorial, 72 Private Study)
Requirements: Prerequisite BIOL 343/3.0 and a minimum GPA of 2.0 in the Biological Foundations List.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Analyze data commonly used in genomics (e.g. FASTA, FASTQ, SAM, BED, BAM) to answer biological questions.
- Apply regular expressions to manipulate biological data.
- Create publication-ready visualizations of biological data.
- Design and implement a strategy for project management in biological research, based on the philosophy that scientific research should be OPEN and REPRODUCIBLE.
- Write custom scripts to curate, merge, subset, reformat, and parse large biological datasets.
- Write programs for 'big data' in biology, using high-performance computing infrastructure maintained by Queen's Centre for Advanced Computing.