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.
Upon successful completion of the program, students will meet the following learning outcomes:
- Demonstrate mastery of subject material.
- Conduct scholarly or professional activities in an ethical manner.
Minor Requirements
| Code | Title | Credits |
|---|---|---|
| Required Core | ||
| MB 599 | SELECTED TOPICS (Collaborative Problem-Solving in Biological Data Science) | 3 |
| Focal Areas | ||
| Select courses from at least two disciplinary focal areas: | 3-5 | |
MS - minimum 3 credits | ||
PhD - minimum 5 credits | ||
| Electives | ||
| Select remaining courses from the focal areas or other courses in consultation with the BLDS Director 1 | 9-12 | |
| Total for MS | 15 | |
| Total for PhD | 18 | |
- 1
Special topics (ST/) courses cover a range of computational skills (e.g., Genotyping By Sequencing; Intro to Unix; Python I & II; Command-Line Data Analysis; Env Sequence Analysis), offered in 1 or 2 credit modules. Each distinct topic can be taken once for credit
Focal Areas
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.
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 | 3 |
| BDS 570 | INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES | 3 |
| BDS 574 | INTRODUCTION TO GENOME BIOLOGY | 3 |
| BDS 575 | COMPARATIVE GENOMICS | 4 |
| BDS 577 | POPULATION GENOMICS | 3 |
| BDS 578 | FUNCTIONAL GENOMICS | 3 |
| IB 554 | EVOLUTIONARY GENOMICS | 3 |
| IB 592 | THEORETICAL ECOLOGY | 4 |
| IB 594 | COMMUNITY ECOLOGY | 5 |
| MB 668 | MICROBIAL BIOINFORMATICS AND GENOME EVOLUTION | 4 |
| MTH 527 | INTRODUCTION TO MATHEMATICAL BIOLOGY | 3 |
| MTH 528 | STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY | 3 |
| VMB 631 | MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS | 3 |
| VMB 670 | INTRODUCTION TO SYSTEMS BIOMEDICINE AND PHARMACOGENOMICS | 2 |
Mathematics Focal Area
| Code | Title | Credits |
|---|---|---|
| MTH 527 | INTRODUCTION TO MATHEMATICAL BIOLOGY | 3 |
| MTH 528 | STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY | 3 |
| MTH 563 | PROBABILITY I 1 | 3 |
| or ST 521 | INTRODUCTION TO MATHEMATICAL STATISTICS | |
| MTH 564 | PROBABILITY II 1 | 3 |
| or ST 522 | INTRODUCTION TO MATHEMATICAL STATISTICS | |
| VMB 631 | MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS | 3 |
- 1
The following sequences qualify for the Mathematics Focal Area: MTH 563–MTH 564, MTH 563–ST 522, ST 521–MTH 564. Only one pair of courses can be claimed for credit. ST 521–ST 522 does not qualify
Statistics Focal Area
| Code | Title | Credits |
|---|---|---|
| H 524 | INTRODUCTION TO BIOSTATISTICS | 4 |
| H 580 | LINEAR REGRESSION AND ANALYSIS OF TIME TO EVENT DATA | 4 |
| H 581 | GENERALIZED LINEAR MODELS AND CATEGORICAL DATA ANALYSIS | 4 |
| MTH 563 | PROBABILITY I 1 | 3 |
| or ST 521 | INTRODUCTION TO MATHEMATICAL STATISTICS | |
| Select one of the following: 1 | 3-12 | |
| PROBABILITY II | ||
| METHODS OF DATA ANALYSIS and METHODS OF DATA ANALYSIS and METHODS OF DATA ANALYSIS | ||
| INTRODUCTION TO MATHEMATICAL STATISTICS | ||
| ST 537 | DATA VISUALIZATION | 3 |
| ST 592 | STATISTICAL METHODS FOR GENOMICS RESEARCH | 3 |
| ST 599 | SPECIAL TOPICS (Introduction to Quantitative Genomics) | 3 |
- 1
The following sequences qualify for the Statistics Focal Area: MTH 563–MTH 564, MTH 563–(ST 511, ST 512, ST 513), MTH 563–ST 522, ST 521–MTH 564, ST 521–(ST 511, ST 512, ST 513), 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 | 4 |
| BB 585 | APPLIED BIOINFORMATICS | 3 |
| BDS 570 | INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES | 3 |
| BDS 572 | ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS | 3 |
| BDS 599 | SPECIAL TOPICS 1 | 1-2 |
| CS 519 | SELECTED TOPICS IN COMPUTER SCIENCE (Algorithms for Computational Biology) | 3 |
| or BB 599 | SPECIAL TOPICS | |
| CS 546/BDS 546 | NETWORKS IN COMPUTATIONAL BIOLOGY | 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 |
| IB 516 | ANALYTICAL WORKFLOWS | 4 |
| VMB 670 | INTRODUCTION TO SYSTEMS BIOMEDICINE AND PHARMACOGENOMICS | 2 |
- 1
Special topics (ST/) courses cover a range of computational skills (e.g., Genotyping By Sequencing; Intro to Unix; Python I & II; Command-Line Data Analysis; Env Sequence Analysis), offered in 1 or 2 credit modules. Each distinct topic can be taken once for credit