Syllabus

Table of contents

1. Overview
2. Learning outcomes
3. Course materials
4. Getting help
5. Course policies
   1. Communication
   2. Participation and illness
   3. Extensions
   4. Accessibility
   5. Mental health
   6. AI policy, use of outside resources, and collaboration
   7. Academic integrity
   8. Professionalism
6. Course components
   1. Class participation
   2. Homework
   3. Project
   4. Exams
7. Grading
   1. Late work
   2. Regrade requests

Note: this syllabus may change in response to changing public health circumstances or university protocols.

Overview

Welcome to STA 279! This course is an introduction to statistical computing. Our overall goal will be to get more familiar with some of the important computational tools for statistics and data science. We will build on STA 112 and learn more about R as we cover topics in simulation, data wrangling, and text data. You will also be introduced to Python and SQL, and you will use Git and GitHub for version control. Throughout the semester you will work with real data and a variety of statistical problems, and we will emphasize reproducibility and thoughtful coding.

Prerequisites: The only prerequisite for this course is STA 112. No other coding experience is assumed. Please note that if you do have extensive coding experience, much of the material in STA 279 will already be familiar to you.

Time: MWF 1:00 – 1:50

Location: Worrell Professional Center 0172

Professor: Ciaran Evans

Office: Manchester 329

Email: evansc@wfu.edu (please allow 24 hours for email responses, and longer on weekends)

Learning outcomes

By the end of this course, you will be able to:

• Clean, transform, and wrangle data in a variety of formats, to both explore characteristics of the data and prepare the data for future analysis and modeling
• Write functions to accomplish key steps in a data processing workflow
• Employ iteration to efficiently apply data processing steps to many inputs
• Query large relational databases (using SQL)
• Scrape, process, and clean data from the web
• Write clear, readable, idiomatic code
• Identify and implement all necessary data processing steps, from data import to modeling, to tackle challenging, open-ended questions
• Communicate data-driven decisions

Course materials

Laptops: You will need a laptop for this class, and must bring it every day.

Textbook: There is no single textbook for this course. However, we will draw from the following texts, which are free at the links provided. Reading suggestions from these texts will be included in homework assignments.

• Modern Data Science with R (3rd edition)
• Advanced R (2nd edition)
• R for Data Science (1st edition)
• R for Data Science (2nd edition)
• Python for Data Analysis (3rd edition)

Software: We will primarily use the statistical software R, through the interface RStudio for working with data and statistical modeling. We will also use Python and SQL. Download instructions for all software will be provided on the course website.

Getting help

If you have any questions about the course (or statistics in general!), please don’t hesitate to ask! I am available during office hours, by appointment, or via email. If you’re emailing about a coding issue, please include a minimum working example (everything I need to reproduce the issue you encountered).

Keep in mind that debugging software issues can take time, so make sure to start the assignments early in case you run into problems.

Office hours: Drop-in office hours will be held in Manchester 329 at the following times:

• Monday 2 - 3:30pm (this is open to students from all my classes)
• Wednesday 2 - 3pm (this is for 279 only)
• Thursday 1 - 2pm (this is for 279 only)

Study sessions: There will also be a TA-led study session 8-9pm on Tuesdays in Manchester 020

Course policies

Communication

While course materials will be posted on the course website, I will send messages and announcements through Canvas. Please make sure your Canvas account is set up so that you receive emails when I send these messages.

Participation and illness

Attendance is important, and you are expected to participate actively in class activities and discussions during lecture. However, your health, and the health of your peers, is crucial. If you are ill, please do not come to class or office hours. All class materials will be posted online, and I can meet with you one-on-one when you have recovered. If you need office hours when you are ill, I am happy to communicate via email or Zoom. Extensions on coursework may be granted on an individual basis under extenuating circumstances.

Extensions

You have a bank of 5 extension days, which you may use over the course of the semester. You may use either 1 or 2 extension days for a give homework or project (making the assignment due either 24 or 48 hours after the original due date). If you plan to use an extension, you must email me before the assignment is due.

Extensions in extenuating circumstances, such as family emergencies, will be handled separately and on an individual basis.

Accessibility

If you require accommodations due to a disability or other learning differences, contact the Center for Learning, Access, and Student Success at 336-758-5929 or class@wfu.edu as soon as possible to better ensure that such accommodations are implemented in a timely fashion. Please feel free to contact me, and I will be happy to discuss any necessary accommodations. I always like to know how to help my students feel comfortable and successful in our course.

Scent-free zone: The 3rd floor of Manchester is a scent-free zone. Please refrain from wearing perfume, cologne, scented lotion, body spray, and all other scented products if visiting the third floor.

Mental health

All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. The University Counseling Center is here to help: call 336-758-5273 or visit their website at https://counselingcenter.wfu.edu/.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night: Counseling Center: 336-758-5273

If the situation is life threatening, call the police: 911 or 336-758-5911 (campus police)

AI policy, use of outside resources, and collaboration

Coding and data science do not exist in a vacuum, and practicing statisticians and data scientists rarely find themselves in a situation in which it is impossible to use any outside resources. Unless otherwise stated, on homework assignments and class activities you are therefore free (and encouraged!) to:

• use AI tools (e.g. ChatGPT, Gemini, Claude, etc.)
• use online resources such as freely available textbooks (both those for our class and others you find helpful), vignettes, lecture notes, and examples
• use online resources for sharing code, such as StackOverflow
• collaborate with other students

However, please be cautious and avoid over-reliance on these resources. Your use of these resources should assist your own work, not replace it. In STA 279, any homework you are given is designed to help you practice and learn the material, as well as to prepare you for exams. You will do a disservice to your preparation for future study and employment, as well as to your own exam grades, if you rely too heavily on AI or on others.

Here are a few examples of good questions you might ask Google, an AI tool, or a classmate:

• “What does this error message mean?”
• “What input types does this function accept?”
• “How do I use a map variant to iterate a function with multiple arguments?”
• “What does it mean for an R function to support tidy evaluation?”
• “Explain what this chunk of code is doing”

And here are a few examples of questions you should generally avoid:

• “Write a solution to this homework problem”
• “Fix this code”

Class rules for AI, outside resources, and collaboration:

• These resources may be used, unless otherwise stated, on homework and class activities
• These resources may not be used on exams
• Rules for projects will be specified in the project descriptions
• If you have obtained code from the internet or an AI tool, you must explicitly cite where you have obtained the code (both code you used directly and code used as an inspiration). Any reused code that is not explicitly cited will be treated as plagiarism and may result in a grade of 0.
  • If the code was obtained from a site like StackOverflow, provide a URL to the post
  • If the code was generated with help from an AI tool, provide the name of the tool and the queries you used
• While you are encouraged to collaborate with other students on homework assignments, you may not copy code and/or answers directly from another student. If the code was written with help from another student, acknowledge your collaborator(s) at the top of the assignment. Failure to acknowledge collaborators will be treated as plagiarism and may result in a grade of 0. If you copy someone else’s work, both parties may receive a grade of 0.
• Rather than copying someone else’s work, ask for help. You are not alone in this course!

Academic integrity

I expect and require that students conduct themselves in a manner according to the Wake Forest standard for academic integrity. Cheating or academic dishonesty of any kind will not be tolerated. For other information on these matters, please consult the Code of Conduct. For Academic issues please see the College Judicial System.

Professionalism

Laptops will be used regularly in class, and you must bring one each day. You may also use laptops or tablets to take notes. Please refrain from using your cellphone, laptop, or tablet for anything other than coursework during class.

Course components

Class participation

In general, I expect you to come to class and participate in the lecture and activities. Your class participation grade will consist of class activities and attendance at departmental seminars.

Class activities: This course includes regular, short activities during class time to help your learning. Participation in class activities is expected, and you will submit your work on these activities at the end of class. Most class periods will have at least one activity; some class periods will have multiple activities. Class activities will be graded for effort, not for completeness or correctness.

If you are unable to attend class due to illness or extenuating circumstances, please let me know. Class activities may be made up at a later date at the instructor’s discretion. Persistent absences, however, will not be permitted except in special circumstances such as a medical or family emergency.

Department seminars: Throughout the semester, the department will host visiting speakers who will give talks on a range of statistical topics. You will attend these seminars and submit a short (one or two paragraph) reflection on the talk and how it relates to material you have learned in your statistics courses. If you cannot attend the seminars due to a conflict, I will provide alternative talks available on YouTube. If you are simultaneously enrolled in another of my courses, the same seminar attendance and reflection may count for both courses.

Reflections on department seminars will be due the day after the seminar (except in extenuating circumstances). Note: it is ok if you don’t understand everything in a seminar talk! The goal is to be exposed to a broad range of topics in statistics and data science, and make connections with material you have seen in your statistics courses.

Seminar dates: (additional seminars may be added later)

• September 11 (11am): Robert Langefeld
• September 30 (11am): Michael Hudgens
• October 7 (11am): Nate Josephs
• October 28 (11am): Fan Li
• November 11 (11am): Jessica Hullman

Homework

Homework will be assigned most weeks, and will be posted on the course website.

You are welcomed, and encouraged, to work with each other on homework assignments, but you must turn in your own work. If you copy someone else’s work, both parties may receive a 0 for the assignment grade, and may be reported to the honor council. If you work with someone else, you must write the name of your collaborator(s) on your homework.

Submission instructions and due dates will be provided on each assignment. Grading will be based on both completeness and accuracy.

Project

Statistics and data science in the real world often involves implementing complex methods and working with challenging, messy datasets. Projects provide an opportunity to develop these skills, and apply the tools you have learned in class and practiced on homework assignments.

There will be one project in this course, due towards the end of the semester. Instructions and grading rubrics will be provided on the course website.

Exams

We will have two in-class exams and one final exam.

Exam 1: October 6 (tentative), in class

Exam 2: November 14 (tentative), in class

Final exam: December 10, 2pm

Grading

I will use the standard grading scale (above a 93 is an A, above a 90 is an A-, above an 87 is a B+, etc.)

Late work

• 30 minutes – 24 hours late (after extensions): lose 20% of points
• 24 – 48 hours late (after extensions): lose 40% of points.
• more than 48 hours late (after extensions): no credit

If you know you cannot turn in assignment (for instance, if you are ill or there is a family emergency), let me know before the assignment is due, and we will work something out. There will be no grade changes after our last day of class.

Regrade requests

If you believe an error has been made in grading your work, you must email me within one week of receiving the graded homework, exam, or project.