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

The current degree requirements are below. Remember you are required to fulfill the requirements from the academic year you were admitted. Please refer to the handbook from your year of admission as needed.

16 credit hours

Required Core (1 credit hour)

BIO 511: Big Data in Context: Ethics, Policy, History, and Philosophy (1) OR
BIO 610 Introduction to Responsible Conduct of Research (RCR) in Life Sciences (1) 

Electives (15 credit hours)

BIO 543 Molecular Genetics and Genomics (3)

BIO 514 Statistical Models for Biology (4)

BIO 549 Phylogenetic Biology and Analysis (3)

BIO 545 Populations: Evolutionary Genetics (3)

BIO 539 Computing for Research (3)

MCB 540 Functional Genomics (3)

NEU 591 Data Analysis in Neuroscience (3)

BIO 598 Software Carpentry (3)

BIO 579 Data Analysis and Visualization in R (3)

BIO 591 Selected Topics in Ecological Modeling (3)

BIO 544 Discovering Biodiversity (3)

EVO 598 Meta-Analysis in Ecology & Evolution (3)

EVO 598 Advanced Programming for Biology (3)

EVO 598 Evolutionary Data Analysis (3)

EVO 598 The Human Genome (3)

BIO 591 Dynamic Modeling of Social & Ecological Systems (3)

BIO 591 Programming for Biologists (3)

BIO 598 Bioscience Data Carpentry in R (3)

BIO 598 Genomics Research Experience (3)

BIO 598 Medical Genetics and Genomics (3)

EVO 601 Principles of Evolution (3)

BIO 598 Microbiome Data Science (3)

BIO 598 Statistical Programming for the Life Sciences (3)

BIO 591 Molecular Evolution (3)

BIO 591 Computational Life Sciences Reading Group (1)

Non-SOLS Elective Options

BMI 517 Adv Biostats Biomed Research

BMI 603 Health Informatics Database 

BMI 515 App Biostats Med & Informatics

HCD 501 Biostatistics & Data Management 

APM 533 Mathematical Population Biology I 

BME  598 Systems Biology of Disease

BMI 555 Statistics Learning for Data Mining  

BMI 598 Biostatistics with Computational Applications 

CHM 598 Quantitative Foundation of Modern Biochemistry 

CHM 598 Unraveling the Noise: Data Driven Models and Analysis

CSE 598 Algorithms in Computational Biology

CSE 598 Bio-Inspired Computing

ERM 598 Algal Bioprocess and Biosystems Engineering

GIS 598 GIS methods for Non-Majors

HCR 562 Clinical Research Data Management & Technology

SOS 598 Research Data Management course

Additional Curriculum Information
Elective coursework is selected from a restricted list in consultation with the academic unit.

The final requirement to complete the certificate and graduate is to write a short overview including a page written summary about how you have applied computational approaches to a biological problem. This can be any project you completed as part of a course or as part of a lab you were participating in. Please email this summary to Program Directors, Dr. Snyder-Mackler and Dr. Kenneth Buetow.