Computational Biology Option
This option is available within the Biological Data Sciences major at the following location:
- Corvallis
Students in the Computational Biology Option take courses in computer science, bioinformatics, and statistics.
Option Code: 918
Upon successful completion of the program, students will meet the following learning outcomes:
- Proficiently apply biological data sciences to scientific investigation.
- Critically evaluate data and design experiments to test hypotheses relevant to biological data sciences.
- Effectively communicate scientific data, methods, and ideas.
- Demonstrate awareness of professional and ethical practices of science.
Code | Title | Credits |
---|---|---|
Genome Biology | ||
BDS 474 | INTRODUCTION TO GENOME BIOLOGY | 3 |
Mathematics | ||
MTH 231 | ELEMENTS OF DISCRETE MATHEMATICS | 4 |
Computer Science | ||
CS 162 | INTRODUCTION TO COMPUTER SCIENCE II 1 | 4 |
CS 261 | DATA STRUCTURES | 4 |
Tracks | ||
Select one of the following tracks: | 19-30 | |
Track 1 - General Computational Biology (19-24 credits) | ||
Track 2 - Advanced Computational Biology (27-30 credits) | ||
Total Credits | 34-45 |
- 1
CS 162 has prerequisites that are not part of the option requirements
Track 1
Code | Title | Credits |
---|---|---|
General Computational Biology | ||
Biology/Biocomputing and Statistics | ||
Group A | ||
Select one course from the following: | 3-4 | |
APPLIED BIOINFORMATICS | ||
NETWORKS IN COMPUTATIONAL BIOLOGY | ||
ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS | ||
COMPARATIVE GENOMICS | ||
POPULATION GENOMICS | ||
FUNCTIONAL GENOMICS | ||
PHYLOGENETICS | ||
MICROBIAL GENOME EVOLUTION AND BIODIVERSITY | ||
Group B | ||
Select one course from the following: | 3-4 | |
METHODS OF DATA ANALYSIS | ||
DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS | ||
SAMPLING METHODS | ||
DATA VISUALIZATION | ||
Select one course from either Group A or B above | 3-4 | |
Advanced Computer Science | ||
Select three courses from the following: | 10-12 | |
ANALYSIS OF ALGORITHMS | ||
INTRODUCTION TO ARTIFICIAL INTELLIGENCE | ||
SOFTWARE ENGINEERING I | ||
SOFTWARE ENGINEERING II | ||
GRAPH THEORY WITH APPLICATIONS TO COMPUTER SCIENCE | ||
MACHINE LEARNING AND DATA MINING | ||
INTRODUCTION TO INFORMATION VISUALIZATION | ||
INTRODUCTION TO PARALLEL PROGRAMMING | ||
Total Credits | 19-24 |
Track 2
Code | Title | Credits |
---|---|---|
Advanced Computational Biology | ||
Mathematics | ||
MTH 254 | VECTOR CALCULUS I | 4 |
MTH 341 | LINEAR ALGEBRA I | 3 |
Machine Learning | ||
CS 331 | INTRODUCTION TO ARTIFICIAL INTELLIGENCE | 4 |
BDS 472 | ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS | 3 |
Biology/Biocomputing and Statistics | ||
Select one course from the following: | 3-4 | |
APPLIED BIOINFORMATICS | ||
NETWORKS IN COMPUTATIONAL BIOLOGY | ||
COMPARATIVE GENOMICS | ||
POPULATION GENOMICS | ||
FUNCTIONAL GENOMICS | ||
PHYLOGENETICS | ||
MICROBIAL GENOME EVOLUTION AND BIODIVERSITY | ||
METHODS OF DATA ANALYSIS | ||
DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS | ||
INTRODUCTION TO MATHEMATICAL STATISTICS | ||
INTRODUCTION TO MATHEMATICAL STATISTICS | ||
SAMPLING METHODS | ||
DATA VISUALIZATION | ||
Advanced Computer Science | ||
Select at least three courses from the following: | 10-12 | |
ANALYSIS OF ALGORITHMS | ||
SOFTWARE ENGINEERING I | ||
SOFTWARE ENGINEERING II | ||
GRAPH THEORY WITH APPLICATIONS TO COMPUTER SCIENCE | ||
MACHINE LEARNING AND DATA MINING | ||
INTRODUCTION TO INFORMATION VISUALIZATION | ||
INTRODUCTION TO PARALLEL PROGRAMMING | ||
Total Credits | 27-30 |
Option Code: 918