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BIOL 6990C
Foundations of Quantitative Reasoning

Welcome to the Foundations of Quantitative Reasoning course. This is a graduate course, though motivated undergraduates can also enroll, but should first reach out to Dr. Nunez. This course has no specific prerequisites, yet some experience programming in Bash, R, SLiM, msprime, and using Github will go a long way. This course is designed to provide students with a solid background in evolutionary and statistical genomics and commonly used quantitative tools.


  1. Understand basic concepts in evolutionary, ecological, and statistical genomics.

  2. Understand and master the concept of tidy data.

  3. Compare and contrast different modeling and data analysis philosophies.

  4. Understand basic tools for the analysis of large datasets.

  5. Understand the fundamental models of evolution: stochastic and deterministic. 

  6. Learn how to acquire, assess, and process large genomic datasets in a repeatable and scalable way.

  7. Learn how to simulate genomic data in order to test null hypotheses of evolution.

  8. Understand and apply forward- and backward-in-time simulation engines (we will focus in forward models).

  9. Propose, test, and assess the hypothesis of natural selection vs. neutral evolution in structured populations.

  10. Perform ecological association analysis with genomic and environmental data.

  11. Understand and apply various tools to understand evolution.

  12. Describe how humans affect ecological and evolutionary dynamics.

  13. Read, digest, and discuss primary literature.

  14. Draft, edit, and discuss scientific proposals.

Our class is ambitious and has many key goals to cover in one semester. However, there will be lots of time to ask questions and to have discussions. The format of the class will change from day to day, but will include a combination of lecturing, problem-solving activities, discussions, and working in small groups.

You should be able to complete most assignments within the allotted class-time, but plan to spend a couple of hours each week outside of class reading papers and finishing assignments. The more time and effort you put in, the more you will get out of this course.

Any and all of the content of this syllabus is subject to change as we go through the course. The material will be tailored to fit the needs of the class. Changes will be announced in-class and electronically.

Topic 1:

Fundamentals of "Big Data" Analysis

Topic 2:

Fundamentals of Evolutionary Analysis

Topic 3:

Fundamentals of Simulations

Topic 4

Advanced Statistical Genetics

Topic 5

Fundamentals of Multispecies Analyses

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Topic 6:

Advanced Evolutionary models

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