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