Biological Data Sciences Graduate Minor
This program is available at the following location:
- Corvallis
The graduate minor in Biological Data Sciences will familiarize MS and PhD graduate students in the life sciences with research concepts and methodologies in quantitative sciences, and those in the quantitative sciences with research concepts and methodologies in life sciences. The disciplinary learning goals of the minor are by nature foundational. Thus, for example, students with advanced expertise in life sciences would receive foundational training in computer science, statistics and/or mathematics. Students with advanced expertise in computer science would receive foundational training in life science, statistics and, if needed, mathematics. A capstone collaborative problem-solving course will be required by all students. Students may complete all the course work in a single year (encouraged), or may choose spread the courses out over several years.
The minor is open to both MS and PhD students.
PhD students must complete at least 18 credits for the minor and MS students must complete 15 credits.
Students must select courses from at least two disciplinary focal areas outside their undergraduate and graduate majors. For example a life sciences student might take courses in mathematics and computer science, while a statistics student might take courses in computer science and life sciences. In each focal area, PhD students must take at least 5 credits and MS students at least 3 credits. Some courses span more than one focal area; these courses may not be counted towards two focal areas simultaneously.
Some courses that are electives in an MS or PhD major may also be counted towards the BLDS minor. For example, a PhD student in Molecular and Cellular Biology (MCB) may select MCB 576 as an elective for their MCB requirements, and also as computer science credit for the BLDS minor.
Required by All Students:
| Code | Title | Credits |
|---|---|---|
| BOT 599 | SPECIAL TOPICS (Collaborative Problem-Solving in Biological Data Science) | 3 |
Students who do not complete an ethics and professionalism class as part of their PhD major must take MCB 557 or an equivalent course.
Students are recommended to choose their courses from the following lists, depending on their prior preparation as an undergraduate. Equivalent or more advanced courses may be substituted after consultation with the BLDS director. Some courses require prerequisites. Some courses span more than one focal area; such courses can be counted towards one or other of those focal areas, but not both.
Life Sciences Focal Area
| Code | Title | Credits |
|---|---|---|
| BB 585 | APPLIED BIOINFORMATICS 1 | 3 |
| BOT 599 | SPECIAL TOPICS (Introduction to Genome Biology) 2 | 3 |
| BDS 575 | COMPARATIVE GENOMICS | 4 |
| IB 592 | THEORETICAL ECOLOGY | 4 |
| IB 594 | COMMUNITY ECOLOGY | 5 |
| MB 668 | MICROBIAL BIOINFORMATICS AND GENOME EVOLUTION 2 | 4 |
| MTH 527 | INTRODUCTION TO MATHEMATICAL BIOLOGY | 3 |
| MTH 528 | STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY | 3 |
| VMB 631 | MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS 2 | 3 |
| VMB 670 | 2 | 2 |
| Total Credits | 34 | |
- 1
Recommended prerequisites may be waived with instructor approval
- 2
No prerequisites
Mathematics Focal Area
| Code | Title | Credits |
|---|---|---|
| MTH 527 | INTRODUCTION TO MATHEMATICAL BIOLOGY | 3 |
| MTH 528 | STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY | 3 |
| Select one of the following: | 3-4 | |
| PROBABILITY I 1 | ||
| INTRODUCTION TO MATHEMATICAL STATISTICS 2 | ||
| Select one of the following: | 3-4 | |
| PROBABILITY II 1 | ||
| INTRODUCTION TO MATHEMATICAL STATISTICS 2 | ||
| VMB 631 | MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS 3 | 3 |
| Total Credits | 15-17 | |
- 1
Recommended prerequisites may be waived with instructor approval
- 2
The following sequences qualify for Mathematics Focal Area credit: MTH 563–MTH 564, MTH 564–ST 521, ST 521–MTH 564. ST 521–ST 522 does not qualify. Only one pair of courses can be claimed for credit.
- 3
No prerequisites
Statistics Focal Area
| Code | Title | Credits |
|---|---|---|
| H 524 | INTRODUCTION TO BIOSTATISTICS 1 | 4 |
| H 566 | 2 | 3 |
| H 580 | LINEAR REGRESSION AND ANALYSIS OF TIME TO EVENT DATA | 4 |
| H 581 | GENERALIZED LINEAR MODELS AND CATEGORICAL DATA ANALYSIS | 4 |
| MCB 599 | SPECIAL TOPICS (Data Programming in RI and II) 1 | 2 |
| Select one of the following: | 3-4 | |
| PROBABILITY I 2,3 | ||
| INTRODUCTION TO MATHEMATICAL STATISTICS 4 | ||
| Select one of the following: | 3-12 | |
| PROBABILITY II 2,3 | ||
| INTRODUCTION TO MATHEMATICAL STATISTICS 4 | ||
| METHODS OF DATA ANALYSIS and METHODS OF DATA ANALYSIS and METHODS OF DATA ANALYSIS 4 | ||
| ST 537 | DATA VISUALIZATION (Via Ecampus only) | 3 |
| ST 592 | STATISTICAL METHODS FOR GENOMICS RESEARCH 2 | 3 |
| ST 599 | SPECIAL TOPICS (Introduction to Quantitative Genomics) 1 | 3 |
| Total Credits | 32-42 | |
- 1
- No prerequisites
- 2
Recommended prerequisites may be waived with instructor approval
- 3
The following sequences qualify for Mathematics Focal Area credit: MTH 563–MTH 564, MTH 564–ST 521, ST 521–MTH 564. ST 521–ST 522 does not qualify. Only one pair of courses can be claimed for credit.
- 4
The following sequences qualify for Statistics Focal Area credit: ST 511–ST 513, MTH 563–MTH 564, MTH 564–ST 521, ST 521–MTH 564, or ST 521–ST 522. Only one of these sequences can be claimed for Statistics focal area credit.
Computer Science Focal Area
| Code | Title | Credits |
|---|---|---|
| AI 534 | MACHINE LEARNING 2 | 4 |
| BB 585 | APPLIED BIOINFORMATICS 1 | 3 |
| CS 519 | SELECTED TOPICS IN COMPUTER SCIENCE (Algorithms for Computational Biology) 1 | 3 |
| or BB 599 | SPECIAL TOPICS | |
| CS 546 | NETWORKS IN COMPUTATIONAL BIOLOGY 1 | 3 |
| ECE 560 | STOCHASTIC SIGNALS AND SYSTEMS | 4 |
| ECE 564 | DIGITAL SIGNAL PROCESSING | 4 |
| FW 599 | SPECIAL TOPICS IN FISHERIES AND WILDLIFE (Machine Learning Topics in Species Distribution Modeling) | 3 |
| MCB 599 | SPECIAL TOPICS (Introduction to Linux and the Command Line) 2 | 2 |
| MCB 599 | SPECIAL TOPICS (Introduction to Python I and II) 1 | 2 |
| MCB 599 | SPECIAL TOPICS (Data Programming in R I and II) 1 | 2 |
| MCB 599 | SPECIAL TOPICS (Simulating Natural Systems) 1 | 1 |
| MCB 576/BOT 576 | INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES 1 | 3 |
| VMB 670 | 2 | 2 |
| Total Credits | 36 | |
- 1
Recommended prerequisites may be waived with instructor approval
- 2
- No prerequisites