GGSB Computational Track Coursework


Computational, mathematical, and statistical tools are essential to research in the biological sciences. The University of Chicago has had a long tradition of excellence in these areas, and to continue that tradition, GGSB has developed a focused curriculum to train students in these areas.  

There are four suggested specializations for this track: 1) Population Genetics & Evolution, 2) Statistcial Genetics, 3) Computational Genomics, and 4) Computational Cell Biology.  GGSB encourages students to explore other areas of interest as well.  

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.

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

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

Statistical Theory and Methods I AND Fundamentals of Computational Biology: Models and Inference AND Fundamentals of Computational Biology: Algorithms and Applications

AND Three [3] Core Elective Courses Chosen from the Following List:

Human Genetics I  OR Genetic Analysis of Model Organisms OR Introductory Statistical Genetics OR Principles of Population Genetics I  OR Evolution of Biological Molecules OR Biophysics of Biomolecules OR Human Variation and Disease OR Genomics and Systems Biology OR Quantitative Analysis of Biological Dynamics

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

Fundamentals of Cell and Molecular Biology OR Applied Linear Statistical Methods OR Topics in Statistical Machine Learning OR Computational Systems Biology OR Introduction to Scientific Computing for Biologists OR Mathematical Computation I – Matrix Computation OR Fundamentals of Genetics OR Statistical Theory and Methods II OR Multivariate Statistical Analysis: Applications and Techniques OR Theoretical Ecology OR Pattern Recognition OR Bayesian Analysis and Principles of Statistics OR Statistical Genetics OR Machine Learning  

Click below for a list of GGSB course descriptions and student tracks: