Election Data Science Course
Fall Semester, 2018
Monday 8:30AM-11:30AM, 342 Dauer Hall
POS 6933 – Special Topics (19617)
POS 4931 – Special Topics (21703)
Every two years over a hundred million people across the United States participate in elections. We will examine the large administrative datasets that track these individuals’ activities: registering to vote, actual voting, and the contributions they make to federal candidates. We will read selected academic studies to understand what questions people ask of these data. We will learn the properties of these data, how to manage and analyze them, and how visualize findings, in order to do real-time analyses in the midst of the fall 2018 general election.
This is a combined graduate and undergraduate class. Students’ familiarity with programming and data management is desirable, but not a requirement. We will learn basic programming skills, so enthusiasm is a must. Any graduate student may enroll in POS 6933 (19617) without restriction. If you are an undergraduate interested in the class, please first contact Professor McDonald (firstname.lastname@example.org) to receive approval to enroll in POS 4931 (21703).
GMS 6805 – Introduction to Applied Ontology (Credits: 3)
Applied ontology is a sub-discipline of knowledge representation that develops resources to make the meaning of terms accessible to computers, to improve interoperability of data, and to support reasoning with digital knowledge bases. This course introduces students to the fundamentals of applied ontology and its role in biomedical informatics. Students will learn what ontologies are, how they differ from similar resources, and how to build, evaluate, and query ontologies.
Prerequisite: None, but some experience with symbolic logic will be an asset.
Class meeting times: Tuesdays, 1:55PM – 4:55PM in HPNP G-111.
Instructor: Amanda Hicks, Ph.D.
GMS 6234 – Introduction to Phylodynamics: A Practical Approach to Molecular Phylogenetics of Pathogens
Dr. Marco Salemi, Professor of Experimental Pathology, is offering the following course this fall: GMS 6234: Introduction to Phylodynamics: A Practical Approach to Molecular Phylogenetics of Pathogens. By the end of the course the students will have a solid understanding of the basic principles of molecular evolution, tree-building algorithms (distance, maximum likelihood and Bayesian based methods), molecular clocks theory, and coalescence theory. They will also be able to analyze real molecular sequence data (using HIV and V. cholerae data sets) to infer maximum likelihood (IQ-TREE) and Bayesian trees (BEAST), calibrate a molecular clock and use coalescence models to investigate the demographic history of microbial epidemics and the relationship between intra-host viral evolution and pathogenesis (BEAST).
Class meeting times: Tuesdays and Thursdays, September 19, 2017 through November 28, 2017, 1:00PM – 4:00PM (Periods 6-8) in CGRC 291. Final exam November 30, 2017.
For more details, visit https://informatics.institute.ufl.edu/wp-content/uploads/2017/08/Syllabus_salemi_course_Fall2017.pdf or contact Dr. Salemi at email@example.com.
To register for the course, please contact Linda Harlan at firstname.lastname@example.org.
MAP 6487 – Biomath Seminar 1
Dr. Calisitus Mgonghala, Assistant Professor of Math, is offering the following course this fall: MAP 6487 Biomath Seminar 1. The course will cover various aspects of infectious disease modeling, including, but not limited to deterministic, stochastic, meta-population and network models (with examples drawn from a variety of diseases), the basic reproduction number and disease control, associating disease models with data (alternative model fitting approaches), and local and global sensitivity analyses. The last part of the course will involve exploring feedbacks between infectious
For syllabus and more information, visit https://people.clas.ufl.edu/calistusnn/.
Contact: Calistus Ngonghala at email@example.com.