Computational Life Sciences (Graduate Certificate)

Computational Life Sciences (Graduate Certificate)

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Computational approaches are transforming the life sciences. In silico approaches complement traditional bench-based approaches to gain novel insights across the spectrum of life sciences. Successful completion of this program will give you the knowledge and ability to apply computational approaches in the life sciences.

Degree Overview

Students in the computational life sciences graduate certificate program develop expertise in the understanding, interpretation and analysis of diverse data types generated from a range of life sciences disciplines, including ecology, botany, evolutionary biology, neuroscience, molecular and cellular biology, and animal behavior.

How to apply

The Plan of Study is the required curriculum to complete the program.

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Courses and electives

Approved Computational Life Sciences Graduate Certificate Electives.

  • BIO 543 Molecular Genetics and Genomics
  • BIO 598 Genomic Analysis
  • BIO 614 Biometry
  • BIO 549 Phylogenetic Biology and Analysis
  • BIO 545 Populations: Evolutionary Genetics
  • BIO 539 Computing for Research
  • EVO 598 Principles of Prog for Biologists
  • MCB 540 Functional Genomics
  • NEU 591 Data Analysis in Neuroscience
  • BIO 598 Software Carpentry
  • BIO 591 Ecological Modeling
  • NEU 591 Computation in Neuroscience
  • EVO 598 Spatial Analysis & Landscape Genet
  • EVO 598 Current Topics in Systematics
  • EVO 598 Discovering Biodiversity
  • EVO 598 Meta-Analysis in Ecology & Evol
  • EVO 598 Species, Traits, and Trees
  • EVO 598 Advanced Programming for Biology
  • EVO 598 Evolutionary Data Analysis
  • EVO 598 The Human Genome
  • BIO 598 Practical Applications in Computational Life Sciences
  • BIO 591 Computational Life Sciences Reading Group
  • GIS 494/598: GIS methods for Non-Majors
  • APM 533 (Mathematical Population Biology I): Class #81188
  • BME 598 Systems Biology of Disease
  • SOS 598 Research Data Management course
  • CSE 598/494: Algorithms in Computational Biology
  • ERM 494/598 Algal Bioprocess and Biosystems Engineering
  • CHM 598: Quantitative Foundation of Modern Biochemistry
  • CHM 494/598: Unraveling the Noise: Data Driven Models and Analysis
  • HCR562 Clinical Research Data Management & Technology (ocourse and icourse)