Foundations of Quantitative Reasoning
University of Vermont
Foundations of Quantitative Reasoning (Spring 2024);
LIVING/LEARN D, room D107; T-Th 10:05am - 11:20 am
Visit the Class GitHub
Foundations of Quantitative Reasoning (FQR) is a graduate-level class designed to provide graduate students with the knowledge and competencies needed to tackle complex problems in biology using evolutionary first principles. As part of this process, students will work to develop a comprehensive analysis toolbox to conduct highly reproducible quantitative research using high-performance computation (HPC) environments. This will be pivotal to ensure success in the student’s graduate careers. This course is open to all interested graduate students, and may also be taken by select, highly advanced, undergraduates with permission from the instructor.
Understand a "common language" and work in "common languages”
Students will be able to understand the basic concepts of evolutionary biology. Moreover, these concepts will serve as a common language to collaborate across disciplines.
Students will be able to design and deploy analytical workflows using the most widely used programming languages in our field (Unix, R, Python, and SLiM which is like R), in an HPC environment using reproducible principles.
Develop testable hypotheses informed by “first principles” and simulation
students will be able to scale up the first principles of evolution in order to create testable hypotheses using simulation data to inform their work.
Integrate hypotheses into a research project with defined goals.
Students will be able to craft small written documents that summarize the proposed work.
Students will be able to give presentations on proposals and receive feedback from peers.
Details of the course
BIOL 6990C (CRN 15309) is an advanced course that will focus on tackling a wide gamut of theoretical, analytical, and operational issues in quantitative research using first principles (i.e., fundamentals) of evolutionary theory. Accordingly, most analyses will be done using simulated data. This choice of simulated data is deliberate and key to the goals of the class since students will focus on solving generalizable issues in data analysis and hypothesis testing without the need to worry about the idiosyncratic issues of real datasets (i.e., the output of sequencing platforms like Illumina, singular, Oxford nanopore, PacBio, etc.). Yet, the expectation of the course is that, having mastered the fundamentals of analyses using simulations, students will be well-equipped to extract the real signal from the noise inherent in their future analyses.
Some thoughts to share about the class:
Is this class a “data science” class?
This class uses “big data” to achieve its goals and uses principles of data science, as such it may be thought as a “data science class”. Yet, we are also interested in the biological interpretation of said “big data”. Accordingly, our class will have a very heavy emphasis on understanding evolutionary theory to “derive meaning” from “the data”.
What are the expectations of the class?
As a graduate class, the expectation is that all students taking the course are deeply invested in their own professional development, and are fully self-motivated.
I have never done coding before, how should I prepare?
Do not worry, the expectation of the class is that students have little coding experience as they enter the class. The class will provide you with all the tools needed to achieve competence in HPC environments and workflows. It is key, however, that you remain self-motivated and, should roadblocks arise, contact Dr. Nunez ASAP, to avoid falling behind in the course.
I have ample experience coding, how can I get value out of this class?
On occasion, students taking the class will have ample coding experience and may be puzzled as to “how to extract value from class time”. I submit to these folks that they may get great value from the course using a four-prong approach:
Polish your knowledge. While you may have ample coding experience, this class will offer you the opportunity to polish your knowledge and expand your toolbox using software that you may not have used before, or tricks that other folks have developed over years of practice.
Become a peer mentor. You can use your knowledge to help others in the class. Knowing how to teach complex skills to others is a true sign of mastery over a topic. You can practice your mentoring skills by helping other folks master HPC skills. If this is something that interests you talk to Dr. Nunez about being placed in a group of beginners. Also, and most crucially, be kind to others in the class.
Bring your own data. It is possible to plug your own data into the class in order to advance research goals. Talk to Dr. Nunez about this early on during the class.
This is a great opportunity to read papers and practice your presentation and proposal writing skills.
It is my goal for this class to provide value to all students regardless of starting skill level.