Office: FA 220E
Office hours: Mondays at 1pm in FA 220e
Lecture & Discussion: M, W at noon in FA 109 (50
Lab: Tuesday at 3pm in KRC 105 (2 hours 45 mins)
Modern computers have reduced many of the barriers to advanced data analysis, and powerful (and free!) computational tools are now available that enable “ordinary” researchers like you and me to answer questions that would have been considered intractable in previous generations! Armed with basic concepts of probability and statistics, a little programming chops - and a deep understanding of the natural world- ecologists and natural resource professionals can get more out of their data than ever before.
In this course, we embrace computer algorithms (and largely avoid closed-form mathematical solutions)!. This can be time-consuming, but it allows us to focus on the ecological system first and foremost, instead of being constrained by the statistical tools available to us.
By the end of this course, students should have the ability to (1) simulate data under biologically realistic scenarios, (2) make inferences from data using maximum likelihood estimation (MLE) and Bayesian techniques, (3) assess model adequacy, goodness-of-fit and predictive performance, and (4) understand where and when to use a wide variety of additional advanced data analysis methods. The goal is for students to emerge from this course as creative data analysts with the tools and intuition needed to draw inferences from a wide variety of data types.
The course motto: Be Dangerous! It is safer to use standard analytical tools (e.g., using ‘canned’ stats functions in R) because these methods have been rigorously tested over the years. When we build our own algorithms, we can be entering uncharted territory. And exploring these territories can be dangerous… and your inner voices (and other people’s voices) might tell you not to go there, just play it safe. Don’t listen to those voices! In this class, you are allowed - and encouraged - to live dangerously!
Our general focus will be on model-based inference, including regression-based approaches, hierarchical/mixed models, and multi-model inference. Additional student-led modules will cover other advanced analysis topics (we’ll discuss this more in class). In all cases, we focus on the concepts and implementation – we leave the nitty-gritty stats questions to statisticians.
Each student will be responsible for leading discussions, demonstrations and a hands-on 1 hour “mini-lab” on an advanced topic that builds on the main concepts in the course (working in groups). The laboratory portion of the class will provide students the opportunity to try out some of the data analysis methods we discuss in lecture. The first 4 labs will be led by the instructor, and the remainder (second half of the semester) will be student-led.
Students will be able to:
Curious scientific mind, broad research interests, and willingness to engage with equations and computer code. Students are expected to already have a basic foundation in statistical concepts and methods, obtained through other coursework. If this is not the case, they should be prepared to work harder to develop the necessary prerequisite knowledge. Datacamp is an excellent platform for learning R, and this class is part of the ‘Datacamp for the classroom’ program, so class participants can take Datacamp courses for free during the semester!
We will use the book, Ecological Models and Data in R, by Ben Bolker, as a general class reference.
Additional readings may be assigned -please check the course schedule (which is ever- evolving!).
Other related books you may find useful include “Statistical Rethinking” by Richard McElreath, and the textbooks on hierarchical models by Kery and Royle.
Student-led lectures and mini-labs: Each student
will work with a small group (2-3) to lead a 50 minute
lecture/demonstration and a 1-hour hands-on ‘mini-lab’ activity that
introduces a relevant data analysis method (for this course, must be
likelihood-based, and implemented in R/JAGS/stan). The lecture component
will provide an overview of the method along with some published
real-world applications of the method. The mini-lab will consist of a
worked example (clear, concise, informative tutorial) and additional
activities that provide classmates with an opportunity to test their
knowledge of the method. Presenters are encouraged to work with the
instructor (and other faculty, graduate students!) to develop their
lectures and mini-lab activities.
Class Participation: Students are expected to actively participate in the classroom. Don’t be afraid to ask questions- fear of embarrassment can be a major impediment to learning. This a safe space for making mistakes- this is part of being dangerous!
Laboratory Reports: Students will submit (1) an R script (‘.R’ file); here, a set of R functions, each of which performs a specific assigned task, and (2) a brief written report (in Word or Google Docs, submitted via WebCampus) succinctly answering any questions, and stating any questions or points of confusion with the lab exercises. While students are encouraged to work on the labs in small groups, all lab submissions must be made individually.
NOTE: the course schedule is subject to change, so please check back frequently!
If you miss a class meeting or lab period, it is your responsibility to talk to one of your classmates about what you missed. If you miss a lab meeting, you are still responsible for completing the lab activities and write-up by the stated due date. Please let your instructor know in advance if you need to miss class or lab.
Any student with a disability needing academic adjustments or accommodations is requested to speak with the instructor or the Disability Resource Center (Pennington Achievement Center Suite 230) as soon as possible to arrange for appropriate accommodations. This course may leverage 3rd party web/multimedia content, if you experience any issues accessing this content, please notify your instructor.
Surreptitious or covert video-taping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy. This class may be videotaped or audio recorded only with the written permission of the instructor. In order to accommodate students with disabilities, some students may have been given permission to record class lectures and discussions. Therefore, students should understand that their comments during class may be recorded.
The University of Nevada, Reno is committed to providing a safe learning and work environment for all. If you believe you have experienced discrimination, sexual harassment, sexual assault, domestic/dating violence, or stalking, whether on or off campus, or need information related to immigration concerns, please contact the University’s Equal Opportunity & Title IX office at 775-784-1547. Resources and interim measures are available to assist you. For more information, please visit the Equal Opportunity and Title IX page.
The University Academic Standards Policy defines academic dishonesty, and mandates specific sanctions for violations. See the University Academic Standards policy: UAM 6,502
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Pursuant to Nevada law, NSHE employees, students and members of the public are no longer required to wear face coverings while inside NSHE buildings irrespective of vaccination status.
Disinfecting supplies are provided for you to disinfect your learning space. You may also use your own disinfecting supplies.
Students testing positive for COVID 19 or exhibiting COVID 19 symptoms regardless of vaccination status will not be allowed to attend in-person instructional activities and must leave the venue immediately. Students should contact the Student Health Center or their health care provider to receive care and who can provide the latest direction on quarantine and self-isolation. Contact your instructor immediately to make instructional and learning arrangements.
For students who are required to quarantine or self-isolate due to 1) COVID 19 infection or 2) exposure while not vaccinated, instructors must provide opportunities to make-up missed course work, including assignments, quizzes or exams. In courses with mandatory attendance policies, instructors must not penalize students for missing classes while quarantined.
In accordance with section 6,502 of the University Administrative Manual, a student may receive academic and disciplinary sanctions for failure to comply with policy, including this syllabus, for failure to comply with the directions of a University Official, for disruptive behavior in the classroom, or any other prohibited action. “Disruptive behavior” is defined in part as behavior, including but not limited to failure to follow course, laboratory or safety rules, or endangering the health of others. A student may be dropped from class at any time for misconduct or disruptive behavior in the classroom upon recommendation of the instructor and with approval of the college dean. A student may also receive disciplinary sanctions through the Office of Student Conduct for misconduct or disruptive behavior, including endangering the health of others, in the classroom. The student shall not receive a refund for course fees or tuition.