|name: Henry Scharf||office: virtual (zoom)|
|email: email@example.com||office hours: T/Th 10:00-11:00am or by appt.|
|(please let me know you’re coming)|
|course number: STAT 580||semester: Fall 2020|
|meeting times: T/Th 11:00am–12:15pm||room: virtual (zoom)|
|prerequisites: Statistics 551B. Some programming experience, particularly with R, will be helpful.|
In a world in which the price of calculation continues to decrease rapidly, but the price of theorem proving continues to hold steady or increase, elementary economics indicates that we ought to spend a larger and larger fraction of our time on calculation. –John Tukey, Sunset Salvo (1986)
This course will focus on the computational aspects of many common statistical modeling techniques including random number generation, integration, uncertainty quantification, and optimization. The course will offer frequent opportunities to practice implementing different algorithms and procedures using the R statistical programming environment.
Students who succeed in this course will have a collection of potentially useful statistical tools at their disposal that they can appropriately apply to a wide variety of problems. Just as important, they will also be able to determine when certain statistical tools are not appropriate, or when a better alternative exists. The focus of the course will be on both holistic, general understanding of methodology, as well as specific implementation using the R statistical programming environment and relevant packages.
Students will be able to:
In-class quizzes (15%): A very short quiz will be given during the first 5-10 minutes of each class meeting. The lowest two scores will be dropped. All quizzes will contribute equally.
Homework (25%): Homework assignments will be given every 2-3 weeks in the form of an Rlab (see schedule). Each assignment will begin in class (virtually), and students will have 8 days to submit their solutions online.
Independent research project (IRP) (25%): Each student will prepare a short tutorial in the form of a pre-recorded presentation or written vignette on a topic of their choosing relevant to statistical computing. Tutorials should be aimed at an audience of peers (i.e., classmates). Pair-based IRPs may be allowed; talk to me first.
Final project: R package (35%): A live or pre-recorded presentation + documented R package, including a vignette. This will be a group effort, and your grade will be based on several deliverables building up to the final presentation + R package. Groups will be made up of 4-5 students chosen randomly by me.
I try to grade assignments as quickly as I can because I think it is most useful for students to receive feedback as soon as possible. Grades and feedback will be posted on Canvas. If you have a question about grades or notice an inaccuracy, please let me know right away.
Late Policy: Brief extensions will be granted for assignments when a reasonable request is made at least 48 hours before the due date. If no arrangements have been made in advance, a late penalty of 20% of the total assignment grade per day will be assessed.
Student privacy: I will not post grades or leave graded assignments in public places. Students will be notified at the time of an assignment if copies of student work will be retained beyond the end of the semester or used as examples for future students or the wider public. Students maintain intellectual property rights to work products they create as part of this course unless they are formally notified otherwise.
Academic Honesty: The University adheres to a strict policy prohibiting cheating and plagiarism. The California State University system requires instructors to report all instances of academic misconduct to the Center for Student Rights and Responsibilities. Academic dishonesty will result in disciplinary review by the University and may lead to probation, suspension, or expulsion.
Statistical Computing with R [Rizzo] by Maria L. Rizzo
Available through SDSU bookstore, or online.
Computational Statistics by Geof Givens and Jennifer Hoeting
Slightly more advanced level than Rizzo’s text written by two of Henry’s previous professors.
Available here, subject to change.
Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging to keep students going (e.g., quizzes), but for the most part your success will be a product of your own internal desire to actually learn this stuff. For my part as the instructor, this means I have planned zero in-class exams, and will try to keep things as immediately relevant for you, the students, as possible. I will try to be responsive to all your requests throughout the semester. If you find something boring/useless, I’ll try to take it out. If you want me to go into more depth on a particular topic, I’ll try to make time to do that.
For your part as a student, this means you will have to manage your own time carefully, and do whatever you must to make assignments/projects relevant for you. When there is an opportunity, find data sets you care about and want to analyze. Focus on methods you want to be able to take with you throughout your career. A fully engaged student will probably find that she is frequently searching online for more information about something we discussed in class. He may find himself listening to unassigned podcasts and reading blogs written by experts. From time to time, they may bump up against a problem to which the collective response of humanity is “We don’t know how to do that…yet.”
I am committed to ensuring each student’s access to all course materials, time and attention from me as the instructor both in and out of class, and fair opportunities to demonstrate mastery of the course content. Please contact me if you require any special assistance or accommodations. To avoid any delay in the receipt of your accommodations, you should contact Student Ability Success Center as soon as possible. Please note that accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from Student Ability Success Center.
I encourage students to reach out by email anytime they need help or have a question. I endeavor to respond to all emails within 24 hours during the work week. Generally I will not be able to respond during the weekend. For questions that require a longer response than a few sentences, please visit me during office hours or schedule a meeting with me. For questions that can be easily answered through a straightforward search online, you may receive a terse reply inviting you to find the answer on your own. (For example: STUDENT: When are your office hours again? ME: You can figure that out without my help. I believe in you!)
Religious observances: In accordance with the University Policy File, please notify me about planned absences for religious observances by the end of the second week of classes.
Medical-related absences: Please contact me if you need to miss class, etc. due to an illness, injury or emergency. For the purposes of addressing university policy, documentation may be requested.
SDSU Economic Crisis Response Team: If you or a friend are experiencing food or housing insecurity, or any unforeseen financial crisis, visit sdsu.edu/ecrt, email firstname.lastname@example.org, or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.
The Art of R Programming by Norman Matloff
R for Data Science by Hadley Wickham and Garrett Grolemund. Especially for those who live in, or are curious about, the tinyverse.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. This site also has links to videos/tutorials prepared by the authors and other experts.
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman, & James Franklin
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!