| Instructor: | Dr. Paul Regier | Time: | Tues/Thurs 9:30-10:55 a.m. |
| Email: | pregier@usao.edu | Place: | Austin Hall 213 |
| Office hours: | paulregier.com/office-hours |
This course introduces the principles and practice of data visualization and exploratory data analysis. Students will use Python to prepare real data, construct static and interactive visualizations, evaluate visual evidence, and communicate findings to an intended audience. Topics include visual encodings, distributions, relationships, multivariate displays, regression and classification graphics, time series, maps, accessibility, ethics, interactivity, dashboards, and data storytelling. (3 hours)
Prerequisite: MATH 2203 Elementary Statistics
If your computer cannot run the required environment, contact the instructor early so that an alternate campus or hosted arrangement can be identified. Do not wait until an assignment is due to report installation problems.
Students should be able to:
Prior experience with Python is helpful but not assumed to be uniform. The first part of the course provides focused practice with Python and pandas for data work.
By the end of this course, you will be able to:
This course meets in person on Tuesdays and Thursdays. Most class meetings will combine brief explanation or demonstration with guided work in a Jupyter notebook. Labs will normally begin in class, where collaboration and questions are encouraged, and conclude with work students complete independently.
Regular attendance and sustained participation are important because the labs build cumulatively. If you miss class, you remain responsible for the material, announcements, and deadlines. Contact the instructor and a classmate promptly to determine what you missed.
The working grading framework is:
| Component | Weight |
|---|---|
| Coursera and preparation assignments | 15% |
| Lab assignments | 40% |
| Two midterm exams | 30% (15% each) |
| Final project | 15% |
| Total | 100% |
After the final course grade is rounded to the nearest whole percent: \(A=90\text{-}100\%\), \(B=80\text{-}89\%\), \(C=70\text{-}79\%\), \(D=60\text{-}69\%\), and \(F<60\%\).
Coursera activities provide practice with Excel and Google Sheets, reinforce the Python visualization tools used in class, and introduce cloud-based tools. Deadlines and required components will be listed in Canvas. Complete assigned videos, readings, quizzes, and exercises by the posted deadline.
Labs are the primary vehicle for learning the course tools and will normally be assigned about once per week.
.ipynb file may also be required when stated in the
assignment.Lab procedures, including VS Code, kernels, notebook execution, and HTML export, will be covered during Week 1. If you miss those meetings, seek help promptly from a peer or during office hours.
Two midterm exams will use real data and course tools. Exam tasks may require you to prepare data, create or critique visualizations, interpret results, and explain design choices. Exams will be completed during the assigned class period and submitted through Canvas. Late or emailed exams will not be accepted except under an approved arrangement.
The final project will integrate data preparation, visualization, and communication. Students will create a coherent visual analysis or interactive data product, document their decisions, and present their work. Detailed requirements, milestones, and a rubric will be provided in Canvas.
Attendance will be recorded. Students are responsible for attending class, meeting deadlines, monitoring their progress, and seeking assistance when needed. No automatic number of free absences is promised. University-sponsored absences should be discussed with the instructor in advance whenever possible.
Discussion and peer assistance are valuable during guided lab work. Unless an assignment says otherwise, submitted code, visualizations, interpretations, and reflections must represent your own work.
Generative AI tools may be used for explanation or troubleshooting only when the assignment permits them. You remain responsible for understanding, checking, and being able to explain every part of submitted work. Do not submit AI-generated code, prose, analysis, or visualizations as your own. When an assignment permits substantive AI assistance, disclose the tool and how it was used. Assignment-specific instructions take precedence over this general rule.
Electronic communication should be clear, concise, and respectful. Before sending a message, check its tone, context, spelling, and required attachments.
The current USAO Syllabus Addendum supplements this syllabus and contains university information on academic honesty, attendance, Canvas, emergency notifications, counseling, disability services, copyright, and student support.
The Student Success Center, located in Nash Library 305, provides free tutoring, study groups, and academic mentoring. Bring specific questions, relevant instructions, and your current work when seeking assistance.
The Office of Accessible Education assists qualified students in arranging reasonable accommodations. Students who need accommodations should contact the office and notify the instructor as early as practical. Accommodations are not retroactive.
Academic dishonesty, including plagiarism, fabrication, cheating, forgery, unauthorized collaboration, copying code, or misrepresenting AI-generated work, violates the Academic Code of Conduct. Consequences will follow university policy and may include a zero on the assignment. Consult the current USAO Student Handbook and Syllabus Addendum for institutional policy.
Students are responsible for checking the official USAO Academic Calendar and the final-exam schedule for updates.
The instructor may make changes to course procedures, assignments, or the schedule when necessary, with appropriate notice through Canvas or email. Constructive feedback and corrections are welcome throughout the semester.