Syllabus

The plan, the details, and the logistics.

Jo Hardin https://m154-comp-stats.netlify.app/ (Pomona College)https://hardin47.netlify.app/

Computational Statistics

Math 154, Fall 2021

Jo Hardin 2351 Estella

Office Hours: Wed 9-11am, Th 1:30-3pm, or by appointment

Mentor Sessions:
Sun 9-10pm, Wed 8-10pm
Estella 1021 (Emmy Noether Room)
Mentor: JJ Shankar

Monsters going through the phases of writing code.

Figure 1: Artwork by @allison_horst.

The course

Computational Statistics will be an introduction to statistical methods that rely heavily on the use of computers. The course will generally have three parts. The first section will include communicating and working with data in a modern era. This includes data wrangling, data visualization, data ethics, and collaborative research (via GitHub). The second part of the course will focus on traditional statistical inference done through computational methods (e.g., permutation tests and bootstrapping). The last part of the course will focus on machine learning ideas such as classification, clustering, and dimension reduction techniques. Some of the methods were invented before the ubiquitous use of personal computers, but only because the calculus used to solve the problem was relatively straightforward (or because the method wasn’t actually every used). Some of the methods have been developed quite recently.

Anonymous Feedback As someone who is constantly learning and growing in many ways, I welcome your feedback about the course, the classroom dynamics, or anything else you’d like me to know. There is a link to an anonymous feedback form on the landing page of our Sakai webpage. Please provide me with feedback at any time!

Course Goals:

Diversity and Inclusion Statement

(adapted from Monica Linden, Brown University):

In an ideal world, science would be objective. However, much of science is subjective and is historically built on a small subset of privileged voices. In this class, we will make an effort to recognize how science (and statistics!) has played a role in both understanding diversity as well as in promoting systems of power and privilege. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was written, even though the material is primarily of a scientific nature. Integrating a diverse set of experiences is important for a more comprehensive understanding of science. I would like to discuss issues of diversity in statistics as part of the course from time to time.

Please contact me if you have any suggestions to improve the quality of the course materials.

Furthermore, I would like to create a learning environment for my students that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, etc.) To help accomplish this:

Texts:

All resources are freely available. They also all have print versions which can be purchased. Using the online version of the books is expected.

First section of the course (data science, data viz, wrangling, simulating, permutation tests, bootstrapping, ethics)

Second section of the course (bootstrapping, knn, trees, bagging, random forests, support vector machines, clustering)

Important Dates:

Git resources

R resources

Homework:

Homework will be assigned from the text(s) with some additional problems. One homework grade will be dropped. Homework will be done using the statistical software package R (in the RStudio IDE) and posted to GitHub + Gradescope. All homework must be done in RMarkdown and compiled to pdf.

HW format
HW Grading

Homework assignments will be graded out of 5 points, which are based on a combination of accuracy and effort. Below are rough guidelines for grading.

[5] All problems completed with detailed solutions provided and 75% or more of the problems are fully correct. Additionally, there are no extraneous messages, warnings, or printed lists of numbers.
[4] All problems completed with detailed solutions and 50-75% correct; OR close to all problems completed and 75%-100% correct. Or all problems are completed and there are extraneous messages, warnings, or printed lists of numbers.
[3] Close to all problems completed with less than 75% correct.
[2] More than half but fewer than all problems completed and > 75% correct.
[1] More than half but fewer than all problems completed and < 75% correct; OR less than half of problems completed.
[0] No work submitted, OR half or less than half of the problems submitted and without any detail/work shown to explain the solutions. You will get a zero if your file is not compiled and submitted on GitHub.

Projects:

There will be a semester long group project. Your task is to use data to tell us something interesting. This project is deliberately open-ended to allow you to fully explore your creativity. There are three main rules that must be followed: (1) data centered, (2) tell us something, (3) do something new. The project information is available here: Math 154 Semester Project

Computing:

Participation:

Academic Honesty:

You are on your honor to present only your work as part of your course assessments. Below, I’ve provided Pomona’s academic honesty policy. But before the policy, I’ve given some thoughts on cheating which I have taken from Nick Ball’s CHEM 147 Collective (thank you, Prof Ball!). Prof Ball gives us all something to think about when we are learning in a classroom as well as on our journey to become scientists and professionals:

There are many known reasons why we may feel the need to “cheat” on problem sets or exams:

Being accused of cheating – whether it has occurred or not – can be devastating for students. The college requires me to respond to potential academic dishonesty with a process that is very long and damaging. As your instructor, I care about you and want to offer alternatives to prevent us from having to go through this process. If you find yourself in a situation where “cheating” seems like the only option:

Please come talk to me. We will figure this out together.

Pomona College is an academic community, all of whose members are expected to abide by ethical standards both in their conduct and in their exercise of responsibilities toward other members of the community. The college expects students to understand and adhere to basic standards of honesty and academic integrity. These standards include, but are not limited to, the following:

Covid Safety Awareness.

The faculty at Pomona College knows that person-to-person interaction provides the best liberal arts education. The best learning occurs in small communities. This year we are gathering in person for what we do best: create, generate, and share knowledge. During the past academic year, we built community remotely, and this year we will build on the pedagogical improvements we acquired last year. For example, we might meet on zoom from time to time, or hold discussions online on Sakai Discussions Board.

Our health, both mental and physical, is paramount. We must consider the health of others inside and outside the classroom. All Claremont Colleges students have signed agreements regulating on-campus behavior during the pandemic; in the classroom, we will uphold these agreements. We need to take care of each other for this course to be successful. I ask you therefore to adhere to the following principles:

The pandemic is fast-moving, and we might have to adjust the principles as the semester evolves. I am always happy to receive your feedback to make this course work.

Let’s care for each other, show empathy, and be supportive. While there will likely be some community transmission and breakthrough infections, together, we can minimize their effect on our community and on your learning.

Advice:

Please email and / or set up a time to talk if you have any questions about or difficulty with the material, the computing, or the course. Talk to me as soon as possible if you find yourself struggling. The material will build on itself, so it will be much easier to catch up if the concepts get clarified earlier rather than later. This semester is going to be fun. Let’s do it.

Grading:

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/hardin47/m154-comp-stats, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

© Jo Hardin, 2021 -- jo.hardin@pomona.edu -- https://hardin47.netlify.app/

Math 154 -- Computational Statistics -- Pomona College, Claremont, CA