Biological Data Sciences Graduate Minor

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. With approval by the director of the minor, students may receive credit for courses taken for their major.

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 Introduction to Computing in the Life Sciences" as an elective for their MCB requirements, and also as computer science credit for the BLDS minor.

Required by All Students:

BOT 599SPECIAL 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 SCIENTIFIC SKILLS AND ETHICS 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

BB 585APPLIED BIOINFORMATICS 13
BOT 599SPECIAL TOPICS (Introduction to Genome Biology) 23
BOT 575/MCB 575COMPARATIVE GENOMICS4
IB 592THEORETICAL ECOLOGY4
IB 594COMMUNITY ECOLOGY5
MB 668MICROBIAL BIOINFORMATICS AND GENOME EVOLUTION 24
MTH 527INTRODUCTION TO MATHEMATICAL BIOLOGY3
MTH 528STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY3
VMB 631MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS 23
VMB 670INTRODUCTION TO SYSTEMS BIOLOGY 22
Total Hours34

Mathematics Focal Area

MTH 527INTRODUCTION TO MATHEMATICAL BIOLOGY3
MTH 528STOCHASTIC ELEMENTS IN MATHEMATICAL BIOLOGY3
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 631MATHEMATICAL MODELING OF BIOLOGICAL SYSTEMS 33
Total Hours15-17

Statistics Focal Area

H 524INTRODUCTION TO BIOSTATISTICS 14
H 566DATA MINING IN PUBLIC HEALTH 23
H 580LINEAR REGRESSION AND ANALYSIS OF TIME TO EVENT DATA4
H 581GENERALIZED LINEAR MODELS AND CATEGORICAL DATA ANALYSIS4
MCB 599SPECIAL TOPICS (Data Programming in RI and II) 12
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 537DATA VISUALIZATION (Via Ecampus only)3
ST 592STATISTICAL METHODS FOR GENOMICS RESEARCH 23
ST 599SPECIAL TOPICS (Introduction to Quantitative Genomics) 13
Total Hours32-42

Computer Science Focal Area

BB 585APPLIED BIOINFORMATICS 13
CS 519SELECTED TOPICS IN COMPUTER SCIENCE (Algorithms for Computational Biology) 13
or BB 599 SPECIAL TOPICS
CS 534MACHINE LEARNING 24
CS 546NETWORKS IN COMPUTATIONAL BIOLOGY 13
ECE 560STOCHASTIC SIGNALS AND SYSTEMS4
ECE 564DIGITAL SIGNAL PROCESSING4
FW 599SPECIAL TOPICS IN FISHERIES AND WILDLIFE (Machine Learning Topics in Species Distribution Modeling)3
MCB 599SPECIAL TOPICS (Introduction to Linux and the Command Line) 22
MCB 599SPECIAL TOPICS (Introduction to Python I and II) 12
MCB 599SPECIAL TOPICS (Data Programming in R I and II) 12
MCB 599SPECIAL TOPICS (Simulating Natural Systems) 11
MCB 576/BOT 576INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES 13
VMB 670INTRODUCTION TO SYSTEMS BIOLOGY 22
Total Hours36
Note: All of the 599 classes here represent classes that are in transition to becoming regular offerings.
Minor Code: 1375