Fall 2026 · 1 credit
MA 26190 / ECE 29595: The Data Science Labs on Multivariable Calculus
An accessible HTML adaptation of the Spring 2026 syllabus, updated for the verified Fall 2026 cross-listed sections.
Course information
| Sections | CRNs | Meeting time | Location | Teaching assistant |
|---|---|---|---|---|
| MA 26190-001 / ECE 29595-007 | 27353 / 29059 | Tuesday, 3:00–5:50 PM | Brown Hall 215 | To be announced |
| MA 26190-003 / ECE 29595-006 | 27356 / 29058 | Tuesday, 6:00–8:50 PM | Brown Hall 215 | To be announced |
Coordinator
- Name: Prof. Kaitlyn Hood
- Email: kthood@purdue.edu
- Office hours: To be announced
- Role: course administration, course structure, and honors contracts
Course description
This one-credit course consists of weekly face-to-face computer laboratories. Projects apply multivariable calculus to data science and provide practice with Python, Jupyter notebooks, sensors, and microprocessors.
- Prerequisite: MA 16290 with a grade of C or better, or prior Python programming experience.
- Corequisite: Multivariable Calculus (Calculus III).
Textbook and calendar
The Data Science Labs on Multivariable Calculus, by Kyndyl King and Mireille Boutin with Alden Bradford and Julia Long.
Learning objectives
By the end of the course, students will be able to:
- Write Python code to collect and analyze data from sensors and microprocessors.
- Use vectors to describe color and create colors with LEDs.
- Use Euclidean distance to detect color with a color sensor.
- Detect image edges using second-derivative ideas.
- Build a motion detector using vector fields and polar coordinates.
- Build a planimeter using Green's theorem and the fundamental theorem of line integrals.
Grading and assignments
A Jupyter notebook report is created for each laboratory and submitted by the section-specific Gradescope deadline. The course grade is based on the laboratory reports. A graded lab may be corrected and resubmitted for full credit at most twice; students may submit no more than one regrade request per week.
| Letter | Percentage | Letter | Percentage |
|---|---|---|---|
| A+ | 97% or higher | C+ | 77–79.9% |
| A | 93–96.9% | C | 73–76.9% |
| A− | 90–92.9% | C− | 70–72.9% |
| B+ | 87–89.9% | D+ | 67–69.9% |
| B | 83–86.9% | D | 63–66.9% |
| B− | 80–82.9% | D− / F | D−: 60–62.9%; F: below 60% |
Attendance
Attendance is mandatory unless the teaching assistant explicitly excuses an absence.
- First-two-week absences are automatically excused but require one announced make-up session per missed lab.
- Later excused-absence requests should be submitted promptly with documentation; make-ups require explicit approval.
- For an exam conflict, contact the exam administrator early. Scheduled classes take precedence under Purdue policy.
- Arrive on time. Late arrival may result in a report penalty. Students may leave after submitting the required work.
Academic integrity, artificial intelligence, and equipment
Students may discuss approaches, but submitted reports and code must be their own work. Do not share final code or solutions or post them publicly. Academic-integrity incidents may result in a failing course grade and referral to the Office of the Dean of Students.
Writing and understanding Python is a central objective. Unless a use is explicitly authorized, submitted work must not be produced by AI or a large language model and must reflect the student's own understanding.
Handle sensors, microprocessors, and other laboratory equipment carefully. Intentional theft or damage is treated as an academic and conduct matter.
Honors contracts
MA 26190 may be used to earn honors credit for MA 26100. A student must take MA 26100 during the same semester, submit the contract by the second Friday, and earn a passing grade in MA 26190. Brightspace contains the submission instructions; Prof. Hood approves the contracts.
Laboratory sequence
- Lab 0: course introduction, breadboards, Jupyter, and LaTeX.
- Lab 1: color as a vector.
- Lab 2: creating color with LEDs.
- Lab 3: human perception of color.
- Lab 4: color quantization.
- Lab 5: color detection.
- Lab 6: edge detection in images.
- Lab 7: acquiring and manipulating video.
- Lab 8: optical flow.
- Lab 9: motion detection.
- Lab 10: building a planimeter.
The dated public calendar and section-specific Brightspace/Gradescope entries determine exact dates.
Hardware
Raspberry Pi Pico, NeoPixels, RGB color sensor, Arducam, potentiometer, OLED display, and tactile switch.
Accessibility
Purdue strives to make learning experiences accessible. Students who anticipate or experience disability-related barriers should contact the Disability Resource Center at drc@purdue.edu or 765-494-1247. If accommodations are approved, send the Course Accommodation Letter to the teaching assistant and discuss implementation promptly. Read the Course Accommodation Letter instructions.
Emergency and university information
In a major campus emergency, requirements, deadlines, and grading details may change. Updates will be sent through Purdue email and Brightspace. Brightspace provides the current accessibility, nondiscrimination, mental-health, and basic-needs links.
Public syllabus URL: https://thedatasciencelabs.github.io/syllabi/ma26190-fall-2026.html