MA 41690 / ECE 39595: The Data Science Labs on Probability

An accessible HTML adaptation of the Spring 2026 syllabus, updated for the verified Fall 2026 cross-listed offering.

Before classes begin: teaching-assistant information and office hours will be added when confirmed. Brightspace remains the authoritative source for enrolled students.

Course information

Fall 2026 cross-listed meeting group
Sections	CRNs	Meeting time	Location	Teaching assistant
MA 41690-001 / ECE 39595-011	27370 / 29068	Wednesday, 3:00–5:50 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 introductory probability to data science and provide practice with Python, sensors, microprocessors, images, and video.

Prerequisite: MA 16290, MA 26190, MA 34990, or prior Python programming experience.
Corequisite: ECE 302, AAE 361, MA 416, STAT 416, or STAT 311.

Textbook and calendar

The Data Science Labs on Probability, Jupyter notebooks by Kyndyl King, Mireille Boutin, and Christopher Janjigian.

View the Fall 2026 MA 41690 / ECE 39595 calendar.

Learning objectives

By the end of the course, students will be able to:

Write Python code to collect and analyze sensor and microprocessor data.
Use sample spaces and conditional probability to describe probability as frequency.
Use probability mass functions to quantize images.
Use background subtraction to detect motion in video.
Create and analyze random-number generators with sensor data.
Use Monte Carlo methods to estimate pi.
Use a naive Bayes classifier to classify medical data.

Grading and assignments

The course grade is based on Jupyter notebook laboratory reports. Submission instructions and exact deadlines appear in Brightspace. 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. The lowest lab grade is dropped.

Letter-grade scale
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 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 41690 may be used to earn honors credit for ECE 302, AAE 361, MA 416, STAT 416, or STAT 311. A student must take the paired course during the same semester, submit the contract by the second Friday, and earn a passing grade in MA 41690. College of Science and College of Engineering procedures differ; consult Prof. Hood and Brightspace.

Laboratory sequence

Introduction.
Review of Python.
Probability as a frequency.
Image quantization.
Acquiring and manipulating video.
Motion detection.
Random-number generators.
Evaluating random-number generators.
Estimating pi with a random-number generator.
Naive Bayes classifiers.

The dated public calendar and Brightspace entries determine exact dates.

Hardware

Raspberry Pi Pico, PPG sensor, accelerometer, camera, and temperature sensor.

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/ma41690-fall-2026.html