GGSB Computational Track Coursework


The Computational track curriculum trains students to address fundamental biological questions and to master the three skillsets that are essential to computational genomics research: probabilistic modeling, statistical inference, and computational algorithms & data structures. This curriculum is also unique in its focus on communication skills, both in terms of writing and speaking.  This emphasis emerges from a perspective that computational biologists need to clearly explain complex algorithms and results in order to both effectively share their research products and to collaborate with diversely trained colleagues.

Three [3] Required Courses in Computational Biology and Statistics:  

STAT 24400  Statistical Theory and Methods I (Autumn) AND HGEN 48600 Fundamentals of Computational Biology: Models and Inference (Winter) AND HGEN 48800 Fundamentals of Computational Biology: Algorithms and Applications

PLUS Two [2] Core Elective Courses Chosen from the Following List:    HGEN 47000 Human Genetics I (Autumn) OR MGCB 31400 Genetic Analysis of Model Organisms (Autumn) OR HGEN 47500 Genetic Mechanisms from Variation to Evolution (Autumn) OR HGEN 47100 Introductory Statistical Genetics (Winter) OR ECEV 35600 Principles of Population Genetics I (Winter) OR ECEV 31100 Evolution of Biological Molecules (Winter) OR BCMB 32200 Biophysics of Biomolecules (Spring) OR HGEN 46900 Human Variation and Disease (Spring) OR HGEN 47800 Quantitative Genetics for the 21st Century (Spring) OR HGEN 47300 Genomics and Systems Biology (Spring) OR MGCB 32000 Quantitative Analysis of Biological Dynamics (Spring)

PLUS Two [2] Additional Elective Courses Chosen From the Following List:

STAT 34300 Applied Linear Statistical Methods (Autumn) OR STAT 37790 Topics in Statistical Machine Learning (Autumn) OR ECEV 32000 Introduction to Scientific Computing for Biologists (Winter) OR STAT 30900 Mathematical Computation I: Matrix Computation (Autumn) OR BIOS 20186 Fundamentals of Cell and Molecular Biology (Spring) OR BIOS 20187 Fundamentals of Genetics (Winter) OR STAT 24500 Statistical Theory and Methods-2 (Winter) OR STAT 32950 Multivariate Statistical Analysis: Applications and Techniques (Winter) OR ECEV 42900 Theoretical Ecology (Winter) OR STAT 24610 Pattern Recognition (Spring) OR STAT 30210 Bayesian Analysis and Principles of Statistics (Spring) OR STAT 35500 Statistical Genetics (Spring) OR STAT 37710 Machine Learning (Spring) 

Note: Students may petition the GGSB Student Affairs/Curriculum Committee for approval of an elective course not listed above.

PLUS TWO LAB ROTATIONS:  BSDG 40100 Section 10 Non-Thesis Research (Autumn, Winter, Spring, Summer)

ADDITIONAL REQUIRED COURSES:  GENE 31900 Introduction to Research. “Allstars” (Autumn) AND BSDG 55100 Responsible, rigorous, and reproducible conduct of research: R3CR  (Winter)

For additional information please click here to view the Doctoral Training in Computational Genomics website.

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