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BCB Students are required to take 12 credit hours of electives to complete their PhD requirements. In particular,

  1. The courses should be relevant to your research (if in doubt, you should verify with the program director);
  2. At least 9 of the 12 credits should have a strong quantitative or computational component. This applies to all students who entered their 2nd PhD year (ie, their first BCB year) in fall 2020 or later. Relaxations to this rule (eg, 6 of the 12 credits) may be considered for students with a primarily mathematical/computational background, and subject to approval of BCB leadership.
  3. These can include courses at other universities (eg, NC State, Duke, etc) provided they covered by UNC’s Interinstitutional Program.

[Note: Students are welcome to take courses that do not meet the above criteria, albeit on the understanding those courses will not count toward the BCB elective requirement.]

Finding the right electives can sometimes be confusing and time consuming.For this reason, students are welcome to postpone taking electives until their second semester (ie, spring of their first year) to give them more time to weigh options and get information from other students on what courses are useful.

Below is a list of elective options as suggested by BCB students.  This list should not be considered as a list of the only available possible electives, but rather a list of options other BCB students found beneficial to their research and career at UNC. For specific feedback/comments on these courses (when available) please contact BCB Student Services Manager John Cornett.

Notes: Electives marked with a “*” are highly recommended.

 

Curriculum in Bioinformatics and Computational Biology

BCB 718 – Computational Modeling Laboratory

BCB 723 – Topics in Statistical Genetics and Genomics

BCB 730 – Fundamentals of Quantitative Image Analysis for Light Microscopy

BCB 645 / GNET 645 – Quantitative Genetics of Complex Traits

BCB 725 – Statistical Genetics

BCB 784 / BIOS 784 – Introduction to Computational Biology

BCB 785 / BIOS 785 – Statistical Methods for Gene Expression

 

Biochemistry

BIOC 643 – Cell Biology I (“Supercell”)

 

Biology

BIOL 565 – Conservation Biology

 

Biostatistics

BIOS 600 – Principles of Statistical Inference

BIOS 611 – Intro To Data Science

BIOS 660 – Probability and Statistical Inference I

BIOS 661 – Probability and Statistical Inference II

BIOS 662 – Intermediate Statistical Methods

BIOS 663 – Intermediate Linear Models

BIOS 735 – Statistical Computing

BIOS 779 – Bayesian Statistics

BIOS 781 – Statistical Methods in Human Genetics

BIOS 782 – Statistical Methods in Genetic Association Studies

BIOS 784 / BCB 784 – Introduction to Computational Biology

BIOS 785 / BCB 785 – Statistical Methods for Gene Expression

 

Computer Science

COMP 401 – Foundations of Programming

COMP 550 – Algorithms & Analysis

COMP 555 – Bioalgorithms*

COMP 590 – Artificial Intelligence

COMP 790 – Machine Learning*

COMP 790 – Language and Vision

COMP 790 – Computational Genetics

 

Curriculum in Genetics and Molecular Biology

GNET 631 – Advanced Molecular Biology I

GMB Modules:

– GNET 645 / BCB 645 – Quantitative Genetics of Complex Traits

– GNET 742 – Introduction to Unix and Perl programming for biomedical researchers

– GNET 743 – Introduction to data analysis and statistics with R

– GNET 749 – PRACTICAL RNASeq

 

Mathematics

MATH 547 – Linear Algebra with Applications

 

Statistics and Operation Research

STOR 614 – Linear Programming

STOR665 – Applied Statistics II

STOR 757 – Bayesian Statistics and Generalized Linear Models

STOR 891- Object Oriented Data Analysis

 

Microbiology and Immunology

MCRO 630 – Virology

 

Neurobiology

NBIO 729 – Neural Information Processing

 

Pathology

PATH 725 – Cancer Pathobiology

 

Pharmacology

PHCO 742 – Contemporary Topics in Cell Cycle

 

Inter-institutional

DUKE 750 – Bayesian Statistical Methods