Syllabus - STA 230 (Spring 2026)

Last updated: Thu Jan 22 2026, 08:06

Course Information

Instructor:

  • Collin Nolte, Noyce 2216, noltecollin@grinnell.edu

Class Meetings:

  • STA-230-02 Noyce 2401, TR 8:30-9:50AM

Office Hours:

Calendly

Class Mentors

Joyce Gill ()

  • Mentor sessions Tuesday 7-8pm in Noyce 2401

Classroom Technician

Anker Roy ()

  • Office hours Monday 1-2 and Wednesday 12-1 in Noyce 2246

Course Description:

This courses introduces core topics in data science in the context of R programming. These include data visualization, data wrangling, exploratory data analysis, reproducible research, and a collection of additional topics. This course incorporates case studies from multiple disciplines and emphasizes the importance of properly communication statistical and scientific ideas.

Texts:

We will use texts from a variety of sources, though the most prominent are given below

All other readings will be postd on the course website.

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Aims and Objectives

This course aims to develop in students informed critical and theoretical perspectives on data collection, manipulation, production, and the use of algorithmic techniques to process and analyze data.

Learning Objectives

After completing this course, students should be able to:

  • Apply methods of exploration, visualization, and statistical analysis to data in order to illustrate key findings and make justifiable inferences using statistical software
  • Use the R programming environment to manage, process, and format unstructured data, and to generate meaningful data visualizations
  • Clearly and concisely communicate the findings of data-driven analyses to statistical and non-statistical audiences.

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Policies

Email

You are welcome to email me whenever, though I rarely have access to my email on evenings or weekends. I’m generally happy to answer questions this way, but all R code troubleshooting must be done in class or during office hours.

Class Sessions

A core component of our class meetings will be working through hands-on labs in a paired programming environment. These pairs will be assigned during the first half of the semester. After the first project, you will have the freedom to choose your partner or work independently near someone that you can occasionally consult with. During labs it is essential that you and your partner(s) work together, making certain that each of you understand your work equally well.

Most labs will begin with a brief “preamble” section that we will go through together as class. The purpose of this section is to introduce the topic of the lab and ensure a smooth start to each class meeting.

Attendance

Because this course involves some amount of group work, absences impact not only yourself but also your classmates. As such, class attendance is absolutely necessary. You are permitted two missed classes this semester without penalty. Every absence following this will result in a 1/3 drop in letter grade (e.g., B+ to B). Being more than 10 minutes late will count as an unexcused absence. Exceptions granted for sports and extra curricular activities.

Late Work

I do not accept any late work; however, there will be a two-day grace period following the due date of any assignment during which the assignment or lab may be submitted without applied penalty. The caveat is that if the grader has already started grading, the assignment can no longer be submitted.

Software

Software is increasingly an essential component of statistics and will play an role in this course. We will primarily use R, an open-source statistical software program.

You are welcome to use your own personal laptop or a Grinnell College laptop during the course. R is freely available and you can download it and it’s UI companion, R Studio, here (note: R must be downloaded and installed before R Studio):

  1. Download R from http://www.r-project.org/
  2. Download R Studio from http://www.rstudio.com/

You may also work on a classroom computer, all of which will have R and R Studio pre-installed.

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Grading

I will be employing the following scale for this course:
Grade A A- B+ B B- C D
Range 94-100 90-93 87-89 83-86 80-82 70-79 60-69

Generally speaking, grades will not be curved unless there is a serious miscalibration in difficulty on my part.

A breakdown of how grades will be distributed during the course is as follows:

Engagement, Participation, and Labs - 40%

Participation in a lab-heavy course is absolutely critical. During labs you are expected to help your partner(s) learn the material (which goes beyond simply answering the lab questions), and they are expected to help you. Additionally, you will be expected to participate in class discussions, either with in-class exercises, or asking and answering questions. This may also take the form of Peer Evaluation Forms.

The remaining 10% of this section will be credited towards labs assignments. Labs will typically be done in class and with a group, and it is expected that you will contribute fairly and fully towards the completion of these labs, which requires that you attend regularly.

Individual Homework - 30%

Homework will be assigned at most once per week. Absolutely no late homework will be accepted. Homework will be due on Gradescope, submitted as a pdf.

A critical portion of this class will be centered around organization and presentation of submission documents. Homework or labs that are poorly formatted or fail to assign pages on gradescope will be docked points.

I encourage you to work with other students or visit the Math Lab or DASIL for help on homework questions, but you should clearly understand all your answers and your assignment should be entirely in your own words. The homework is intended primarily as an exercise to practice and develop your understanding and to assist in identifying weaknesses: you do only yourself a disservice if you submit work that you do not fully understand.

If you engage in significant collaboration with classmates or tutors, you must explicitly acknowledge that person(s) on the top of your assignment (again, you are encouraged to collaborate, and I want to emphasize that there is no penalty for doing so).

Projects and Midterms - 30%

The remaining portion of the grade will be assigned based on projects and midterms. More information on what these will look like will be offered in advance.

Boilerplate

Academic Honesty

Please do not cheat. The policy at Grinnell College removes ALL discretion that I may exercise; any work that is suspected of violating the academic honest policy will be submitted to the Committee on Academic Standing.

In virtually all cases, unless otherwise specified, the use of generative AI is strictly prohibited.

At Grinnell College you are part of a conversation among scholars, professors, and students, one that helps sustain both the intellectual community here and the larger world of thinkers, researchers, and writers. The tests you take, the research you do, the writing you submit-all these are ways you participate in this conversation.

The College presumes that your work for any course is your own contribution to that scholarly conversation, and it expects you to take responsibility for that contribution. That is, you should strive to present ideas and data fairly and accurately, indicate what is your own work, and acknowledge what you have derived from others. This care permits other members of the community to trace the evolution of ideas and check claims for accuracy.

Failure to live up to this expectation constitutes academic dishonesty. Academic dishonesty is misrepresenting someone else’s intellectual effort as your own. Within the context of a course, it also can include misrepresenting your own work as produced for that class when in fact it was produced for some other purpose. A complete list of dishonest behaviors, as defined by Grinnell College, can be found here.

Inclusive Classroom

Grinnell College makes reasonable accommodations for students with documented disabilities. To receive accommodations, students must provide documentation to the Coordinator for Disability Resources, information can be found here. If you plan on using accommodations in this course, you should speak with me as early as possible in the semester so that we can discuss ways to ensure your full participation in the course.

Religious Holidays

Grinnell College encourages students who plan to observe holy days that coincide with class meetings or assignment due dates to consult with your instructor in the first three weeks of classes so that you may reach a mutual understanding of how you can meet the terms of your religious observance, and the requirements of the course.

Title IX and Pregnancy

Grinnell College is committed to compliance with Title IX and to supporting the academic success of pregnant and parenting students and students with pregnancy related conditions. If you are a pregnant student, have pregnancy related conditions, or are a parenting student (child under one-year needs documented medical care) who wishes to request reasonable related supportive measures from the College under Title IX, please email the Title IX Coordinator at . The Title IX Coordinator will work with Disability Resources and your professors to provide reasonable supportive measures in support of your education while pregnant or as a parent under Title IX.

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