The BLDS capstone course, Collaborative Problem-Solving in Biological Data Science (CRN 58415), will be offered Spring '19 term.

Course Requirements

The minor is open to both M.S. and Ph.D. students. Ph.D students must complete at least 18 credit hours for the minor and M.S. students must complete 15 credit hours.

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. 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 M.S. or Ph.D. major may also be counted towards the BLDS minor. For example, a Ph.D. 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.

Students enrolling in the minor must complete an ethics and professionalism class as part of their Ph.D. major, for example MCB 557: Scientific Skills and Ethics (3). This course does not count towards the unit credits for the minor.

Required by all students

MB 599: Collaborative problem-solving in biological data science (3)*

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 advisor. Some courses require pre-requisites. 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 585: Applied Bioinformatics (3) §
BI 592. Theoretical Ecology (4) †
BIOE 511: Cellular and Molecular Bioengineering (3)* †
BIOE 512: Modeling of Physiological Systems (4)* §
BOT 599. Special Topics [Introduction to Genome Biology] (3) †
BOT 575: Comparative Genomics (4)
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 651. Selected Topics [Cancers Systems Biology] (3) †
VMB 670. Introduction to Systems Biology (2) †
Z 594. Community Ecology (5) †

Mathematics Focal Area

MTH 527. Introduction to Mathematical Biology (3)
MTH 528. Stochastic Elements in Mathematical Biology (3)
MTH 563. Probability I (3) §
   or  ST 521. Introduction to Mathematical Statistics I (4) ‡
MTH 564. Probability II (3) §
   or ST 522. Introduction to Mathematical Statistics II (4) ‡
VMB 631. Mathematical Modeling of Biological Systems (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. 

Statistics Focal Area

CHE 599 Introduction to Data Sciences (3) † (Cory Simon CBEE Fall 2019 Syllabus )
H 524. Introduction to Biostatistics (4) †
H 566. Data Mining in Public Health (3) §
H 580. Linear Regression and Analysis of Time to Event Data (4)
H 581. Generalized Linear Models and Categorical Data Analysis (4)
ST 599. Special Topics [Data Programming in R I and II] (2) †
MTH 563. Probability (3) §‡
    or ST 521. Introduction to Mathematical Statistics I (4) #
MTH 564. Probability (3) §‡
    or ST 522. Introduction to Mathematical Statistics II (4) #
    or ST 511-513. Methods of Data Analysis (4,4,4) #
ST 515. Design and Analysis of Planned Experiments (3). PREREQS: ST 352 or (ST 411 or ST 511)
ST 591. Introduction to Quantitative Genomics (3) †
ST 592. Statistical Methods for Genomics Research (3) §
ST 516  Foundations of Data Analytics (4) (E-campus only) PREREQ: ST351 #
ST 517  Data Analytics I (4) (E-campus only) PREREQ: ST516 #
ST 518  Data Analytics II (4) (E-campus only) PREREQ: ST517 #
ST 599. Special Topics [Topics in Data Visualization] (3)

 # The following sequences qualify for Statistics Focal Area credit: ST 511-513, 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

BB 585. Applied bioinformatics (3) §
CHE 599 Introduction to Data Sciences (3) † (Cory Simon CBEE Fall 2019 Syllabus )
CS 519 Selected Topics/Computer Sci [Algorithms for Computational Biology] (3) §
   or BB 599. Special Topics [Algorithms for Computational Biology] (3) §
CS 534. Machine Learning (4) †
CS 546. Networks in Computational Biology (3) §
ECE 560. Stochastic Signals and Systems (4)
ECE 564. Digital Signal Processing (4)
FW 599. Special topics [Machine Learning Topics in Species Distribution Modeling] (3)
BDS 599. Special Topics [Introduction to Linux and the command line] (2) †
BDS 599. Special Topics [Introduction to Python I and II] (2) §
ST 599. Special Topics [Data Programming in R I and II] (2) §
BDS 599. Special Topics [Deep Learning] (1) § (coming soon)
BOT 576. Introduction to Computing in the Life Sciences] (3) §
OC 512, Basic MatLab for Environmental Scientists and Engineers (2) †
VMB 670. Introduction to Systems Biology (2) †

* Future planned course
† No prerequisites
§ Recommended prerequisites may be waived with instructor approval
Note: All of the 599 classes here represent classes that are in transition to becoming regular offerings.

For more information contact Brett Tyler