Syllabus

The plan, the details, the logistics.

Author
Affiliation

Jo Hardin

Pomona College

Computational Statistics

Math 154, Fall 2024

Jo Hardin 2351 Estella jo.hardin@pomona.edu

Office Hours: Mon 2:30-3:30pm, Tues 9-10am & 1:30-2:30pm, Thurs 10am-noon

Mentorless Mentor Sessions:
Mon 8-10pm, Tues 8-10pm
Estella 2393
Mentor: you don’t have a mentor – work together!!!

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 Canvas webpage. Please provide me with feedback at any time!

Student Learning Outcomes.

By the end of the term, students will be able to:

  • work through the entire computational statistics flow chart as a data analyst.
  • use graphical representations of data to communicate ideas about the data.
  • identify, understand, and describe the uses and misuses of algorithms and data collection from an ethical lens with the purpose of preventing harms from data science.
  • critically evaluate analyses / graphics of data (typically big data or dynamic data).
  • communicate results effectively.

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:

  • If you have a name and/or set of pronouns that differ from those that appear in your official records, please let me know!
  • If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you. If you prefer to speak with someone outside of the course, the math liaisons, Dean of Students, or QSC staff are all excellent resources. I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it. As a participant in course discussions, you should also strive to honor the diversity of your classmates.

Technical Details

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:

  • Check-in quizzes on 9.11, 9.25, 10.9, 10.23, 11.6, and 11.20 (in class)
  • 10.16.24 Initial Project Proposal due (via Canvas by midnight)
  • 10.30.24 Final Project Proposal due (on GitHub by midnight)
  • 11.13.24 Project Update due (on GitHub by midnight)
  • 12.2.24 (4:15pm) Data Science Panel with industry data scientists
  • 12.6.24 (Friday) or 12.12.24 (Thursday) Group Presentations (both 2-5pm)
  • 12.12.24 Final write-up due (on GitHub by midnight)

Homework:

Weekly 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 Quarto and compiled to pdf.

  • Homework will be due on Tuesdays by 11:59pm to GitHub + Gradescope.
  • Non-homework activities (e.g., from the text) may be collected and added to your engagement grade.
  • HW should be turned in to your GitHub repository and posted to Gradescope by Tuesday at 11:59pm.
  • Always post both a PDF and a Quarto file to GitHub (and a pdf to Gradescope), unless otherwise requested.
  • HW is graded on a scale of 5/4/3/2/1. See below.
  • HW file should be in the format of: ma154-hw#-lname-fname.pdf
HW format
  • please be neat and organized which will help me, the grader, and you (in the future) to follow your work.
  • write your name on your assignment.
  • please include at least the number of the problem, or a summary of the question (which will also be helpful to you in the future to prepare for exams).
  • it is strongly recommended that you look through the questions as soon as you get the assignment. This will help you to start thinking how to solve them!
  • for R problems, it is required to use Quarto
    • in case of questions, or if you get stuck please don’t hesitate to email me (though I’m much less sympathetic to such questions if I receive emails within 24 hours of the due date for the assignment).
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. Project information is available here: Math 154 Semester Project

Computing:

  • GitHub will be used as a way to practice reproducible and collaborative science. There may be a slight learning curve, but knowing Git will be an extremely useful skill as you venture beyond this class.

  • R will be used for all homework assignments. R is freely available at http://www.r-project.org/ and is already installed on college computers. Additionally, you need to install R Studio in order to use Quarto, https://posit.co/downloads/. If you are not already familiar with R, please work through some of the materials provided ASAP.

  • You are welcome to use Pomona’s R Studio server at https://rstudio.campus.pomona.edu/ (or https://rstudio.pomona.edu if you are off campus). If you use the server, you can connect directly to your Git account without installing Git locally on your own computer. [If you are not a Pomona student, you will need to get an account from Pomona’s ITS. Go to ITS, tell them that you are taking a Pomona course, and ask for an account for using RStudio.]

Engagement:

  • This class will be interactive, and your engagement is expected (every day in class). Although notes will be posted, your engagement is an integral part of the in-class learning process.

  • In class: after answering one question, wait until 5 other people have spoken before answering another question. [Feel free to ask as many questions as often as you like!]

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:

  • An academic environment that values grades above learning.
  • Financial aid is critical for remaining in school that places undue pressure on maintaining a high GPA.
  • Navigating school, work, and/or family obligations that have diverted focus from class.
  • Challenges balancing coursework and mental health.
  • Balancing academic, family, peer, or personal issues.

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:

  • In projects and assignments prepared independently, students never represent the ideas or the language of others as their own.
  • Students do not destroy or alter either the work of other students or the educational resources and materials of the College.
  • Students neither give nor receive assistance in examinations.
  • Students do not take unfair advantage of fellow students by representing work completed for one course as original work for another or by deliberately disregarding course rules and regulations.
  • In laboratory or research projects involving the collection of data, students accurately report data observed and do not alter these data for any reason.

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:

  • 30% Homework
  • 30% Quizzes
  • 30% Group Project & Presentation
  • 10% Class engagement

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