Electives
BCB Students are required to take 12 credit hours of electives to complete their PhD requirements. In particular,
- The courses should be relevant to your research (if in doubt, you should verify with the program director);
- 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.
- These can include courses at other universities (eg, NC State, Duke, etc) provided they covered by UNC’s Interinstitutional Program.
- In most cases, transfer credits cannot be used toward the elective requirement.
[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 that other BCB students (at the time of this writing) 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.
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 602 – Bayesian Statistical Methods