Computational Life Sciences (Graduate Certificate) (Certificate)
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.
At a Glance: program details
Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.
Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in the life or social sciences or related field from a regionally accredited institution.
Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program, or applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.
Applicants are required to submit:
- graduate admission application and application fee
- official transcripts
- professional resume
- written statement
- proof of English proficiency
Additional Application Information
- An applicant whose native language is not English must provide proof of English proficiency regardless of current residency.
- The professional resume should demonstrate the applicant's skills and previous training.
- The written statement asks about future career goals to better assist the student with selection of the most applicable set of courses in evolutionary medicine.
- International students who need an F1 or 1 J1 visa first need to apply to and be accepted into a graduate degree program prior to being considered for the certificate program. International students residing in the U.S. on other types of visas must adhere to all Graduate College policies and procedures regarding admission be considered for admission to this certificate program.
Elective courses (Choose 15 credits)
- BIO 543 Molecular Genetics and Genomics
- BIO 598 Genomic Analysis
- BIO 514 Statistical Models for Biology
- 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
Outside of School of Life Science
- 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
- CSE 598: BIO-INSPIRED COMPUTING
- 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)