Instructor:
Class Meetings:
Student Hours: This time is purposefully scheduled for you to drop in and ask any questions about the course. Please feel free to stop by. If the time doesn’t work for you, message me and we can try to arrange something.
Mentor Information: This course employs the use of a mentor to aid you in navigating the material. Our course mentors will assist us in class and host 1 – 2 mentor sessions throughout the week. Mentor sessions may review course content, provide practice problems, provide homework help, or aid in debugging code.
Mentor Info:
TBP
Mentor Sessions: TBP
Course Website: Course materials, assignments, and other resources will be made available through the course website at https://nfriedrichsen.github.io/STA230_F26/course_page.html. A link to the course website will also be sent to students by email at the beginning of the semester.
Course Description:
This course introduces core topics in data science using R programming. This includes introductions to getting and cleaning data, data management, exploratory data analysis, reproducible research, and data visualization. This course incorporates case studies from multiple disciplines and emphasizes the importance of properly communicating statistical ideas. Prerequisite: MAT-209 or STA-209. Suggested CSC-151 or computer programming experience.
Texts:
Much of the course material is inspired by or adapted from the following books. These texts may be useful as additional references, but students are not required to obtain a copy.
Additional readings, references, or online resources may be provided throughout the semester as needed.
After completing this course, students will be able to do the following:
A substantial portion of this course will involve “hands-on” practice working with data and writing code. Most class meetings will include a lab or other activity covering the concepts and techniques introduced in class. You are expected to work actively on the assigned material during class and ask questions when needed. Labs will generally be due at 11:59pm on the day following the activity.
Some class meetings may instead include short lectures, demonstrations, case studies, discussions, or other activities focusing on broader topics in data science.
Collaboration will often be permitted during lab sessions, although most labs will be completed and submitted individually. Some activities may involve assigned partners or groups; any different collaboration expectations will be stated with the activity.
Making mistakes, debugging code, and needing assistance are normal parts of learning data science. You are not expected to get everything correct on your first attempt, and you are encouraged to ask questions and experiment with different approaches as you work.
Labs will generally be graded on completion and good-faith effort rather than strict correctness, although submission and formatting requirements still apply (see below). Full credit requires a reasonable attempt at all portions of the lab using the concepts, methods, or techniques the activity is designed to practice. Reasonable mistakes will not generally reduce your grade, but responses that do not meaningfully engage with the intended methods may not receive credit. The instructor and course graders will use their discretion in determining whether a submission demonstrates sufficient effort and engagement.
Labs will be submitted through Gradescope and will typically be due at 11:59 p.m. the day after the activity. If you are absent, you are still responsible for completing and submitting any assigned work by the stated deadline.
Individual labs or activities may occasionally use different grading criteria. Any alternative requirements or grading criteria will be provided with the activity.
Homework consisting of longer programming exercises, analyses, or other assignments intended to be completed outside of class will be assigned roughly once per week. Assignments may be graded primarily for completion and good-faith effort or for accuracy and quality of work. Completion-based assignments emphasize meaningful practice and reasonable engagement with the assigned material, while accuracy-based assignments evaluate the quality and correctness of the submitted work. The manner of evaluation will be provided with each assignment.
You are encouraged to work with other students or visit the Math Lab or DASIL for help, but you should understand all work that you submit and your assignment should be entirely in your own words. If you engage in significant collaboration with classmates or tutors, you must acknowledge those individuals on your assignment. There is no penalty for appropriate collaboration.
Homework will be submitted through Gradescope and will typically be due one week after posting.
There will be 3–4 projects that will vary in workload and content. Specific requirements and grading criteria will be provided as the course progresses. Possible projects may include (but are not limited to) data visualization, cleaning a dataset, performing and communicating an analysis, or creating an interactive display.
Submitted work should be organized, readable, appropriately formatted, and follow the submission requirements provided with the assignment. This includes producing the requested file format; ensuring that code, figures, tables, and written responses are legible and appropriately presented; and correctly assigning pages to questions in Gradescope when required. Submissions that do not meet these standards may lose points, including assignments otherwise graded primarily for completion.
Unless otherwise stated, assignments requiring a rendered document should be submitted as a PDF. If an assignment cannot be rendered as a PDF but can be successfully rendered in another format, such as HTML, the alternative format may be accepted at the instructor’s discretion, possibly with a grading penalty. Submissions that cannot be rendered or otherwise reasonably evaluated may receive substantially reduced or no credit.
Students are expected to be present and ready to begin when class starts and to participate actively in assigned activities. This is an active, practice-oriented course where arriving late can mean missing instruction needed to complete the day’s work, while time spent catching up can interfere with other students’ learning. Students who arrive after the opening instructions or demonstration have begun will generally be recorded as a half-absence (2 late arrivals = 1 absence, round down); arriving more than 15 minutes late will count as 1 absence.
Students are permitted two absences during the semester without penalty. Each additional absence will result in a 1/3 letter-grade reduction to the final course grade (e.g., B+ to B). Exceptions may be granted for approved sports, extracurricular activities, or other extenuating circumstances, but students should not plan on receiving additional excused absences.
Class time should be used for course-related work. Working on assignments for other courses or otherwise using class time for unrelated activities may result in the loss of attendance credit for that class meeting.
Each student will have two extension tokens to use during the semester. One token extends the deadline of a lab or homework assignment by 48 hours, with no explanation or justification required. Outside of these tokens, no late work will be accepted.
To use a token, notify the instructor by email before the 48-hour extension period expires. Tokens may not be combined to extend the same assignment beyond 48 hours.
Extension tokens do not apply to projects. In rare cases, an assignment may disallow use of a token, but this will be communicated in the instructions.
Unused tokens have no effect on the final course grade and cannot be exchanged for additional credit.
You are welcome to email me whenever, though I rarely check 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, during office hours, or during mentor sessions. Please start your email subject line with “STA230”. If for some reason I do not respond to your email in a timely manner during the week, please send a follow-up email.
R will be the primary programming language used throughout this course. We will primarily work with R through RStudio, an integrated development environment (IDE) for writing and running R code, working with data, and creating reproducible documents.
Students should install R and RStudio on the computer they expect to use regularly for this course. Both are freely available and should be installed in the following order:
R from the
R Project for Statistical
Computing.RStudio Desktop from Posit.Additional R packages will be installed throughout the semester as needed.
Course materials, demonstrations, and software instructions will be designed around Windows. Students may use other operating systems, such as macOS, but differences in software installation, permissions, package behavior, and external connections may occasionally cause compatibility issues. Students choosing to use another operating system should be prepared to address operating-system-specific issues or use a Windows computer when necessary. Windows laptops are available from the College, and Windows desktop computers with R and RStudio installed are available in the classroom.
Grinnell also provides an online version of RStudio that may be useful as a backup. However, this environment may not contain all packages used in the course, and students should not rely on it as their primary computing environment.
You can expect to spend roughly 12 hours per week on this course, including 3 in-class hours and 9 out-of-class hours. This number is based on the Grinnell Guidelines for credit-hours. Some weeks will be more, some weeks will be significantly less. If you find that you are frequently spending significantly more than 9 hours working on material for this course outside of class each week, please let me know.
(Some wording will be updated here over the first weekend) Please do not cheat. You do not need to copy another’s work or use genAI to do well in this class. 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.