Fall 2026 · 1 credit
MA 16290: The Data Science Labs on Calculus
This syllabus is an accessible HTML adaptation of the Spring 2026 syllabus, updated for the verified Fall 2026 offerings and working laboratory schedule.
Course information
| Section | CRN | Meeting time | Location | Teaching assistant |
|---|---|---|---|---|
| 001 | 25495 | Monday, 6:00–8:50 PM | Brown Hall 215 | To be announced |
| 002 | 22275 | Thursday, 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. The laboratories introduce applications of one-variable calculus to data science problems through short projects. Students learn Python and Jupyter notebooks and use sensors and microprocessors to acquire data.
- Prerequisite: Differential Calculus (Calculus I) with a grade of C or better.
- Corequisite: Integral Calculus (Calculus II).
Textbook and calendar
The Data Science Labs on Differential and Integral Calculus, Jupyter notebooks by A. Bradford and M. Boutin with Kyndyl King and Naazneen Rana.
View the Fall 2026 MA 16290 calendar. Brightspace and Gradescope dates take precedence if the working public calendar changes.
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 the first derivative test to build a device that measures heart rate from a PPG sensor.
- Analyze PPG data using means, standard deviations, and polynomial fitting.
- Use trigonometry to build an inclinometer with an accelerometer.
- Analyze inclinometer measurement error with limits, integrals, and derivatives.
- Use finite differences and interpolation to build a distance-measurement tool with an ultrasonic sensor.
Grading and assignments
A Jupyter notebook lab report is created for each laboratory. Unless the section instructions say otherwise, submit the report by the deadline listed for your section in Gradescope. The course grade is based on the laboratory reports.
After grading, a lab may be corrected and resubmitted for full credit at most twice. Students may submit no more than one regrade request per week. The teaching assistant will provide the section's regrade procedure.
| 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.
- A student who misses a lab during the first two weeks is automatically excused but must attend one announced make-up session for each missed laboratory.
- After the first two weeks, request an excused absence promptly and provide documentation. A make-up is permitted only with explicit approval.
- For an exam conflict, contact the exam administrator as early as possible. Scheduled classes take precedence over exams under Purdue policy; the coordinator can provide documentation.
- Arrive on time. Instructions are often given at the beginning of the session, and late arrival may result in a report penalty.
- Students may leave after completing and submitting the required work.
Academic integrity, artificial intelligence, and equipment
Individual work
Students may discuss approaches, but every submitted report and all submitted code must be the student's 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.
Artificial intelligence and large language models
Writing and understanding Python code is a central learning objective. Unless the teaching assistant explicitly authorizes a particular use, submitted work must reflect the student's own understanding and must not be produced by AI or a large language model. Unauthorized use is handled under the academic-integrity policy.
Equipment
Handle sensors, microprocessors, and other laboratory equipment carefully. Intentional theft or damage is treated as an academic and conduct matter.
Honors contracts
MA 16290 may be used to earn honors credit for MA 16200 or MA 16600. A student must take the paired course during the same semester, submit the honors contract by the second Friday of the semester, and earn a passing grade in MA 16290. Detailed submission instructions are posted in Brightspace; Prof. Hood approves the contracts.
Laboratory sequence
The dated public calendar and section-specific Brightspace/Gradescope entries determine the exact meeting and submission dates.
- Lab 0: syllabus, Jupyter notebooks, Markdown, and LaTeX.
- Lab 1a: first half of the introduction to Python, data visualization, functions, sampling, and aliasing.
- Lab 1b: second half of the two-week Lab 1 sequence.
- Lab 2: determine heart rate from PPG data by finding local maxima.
- Lab 3: estimate blood flow with functions and polynomial fitting.
- Lab 4a: namespaces, modules, and objects in preparation for an inclinometer.
- Lab 4b: build the inclinometer.
- Lab 4c: measure heights with the inclinometer.
- Lab 4d: analyze measurement accuracy and error propagation.
- Lab 5a: floating-point numbers and numerical differentiation.
- Lab 5b: build a distance-measurement tool with an ultrasonic sensor.
Fall 2026 change: Lab 4e is not included.
Programming and hardware
Programming topics
Primitive and structured data types, expressions, conditionals, loops, functions, methods, file input/output, namespaces, modules, objects, classes, inheritance, REPL systems, plotting, data cleaning, Boolean indexing, and numerical computation.
Hardware
Raspberry Pi Pico, PPG sensor, accelerometer, ultrasonic distance sensor, OLED display, and tactile switch.
Accessibility
Purdue University strives to make learning experiences accessible to all participants. Students who anticipate or experience disability-related barriers should contact the Disability Resource Center at drc@purdue.edu or 765-494-1247.
If the DRC has approved accommodations, send the Course Accommodation Letter to the teaching assistant and contact them promptly to discuss implementation. Read the DRC Course Accommodation Letter instructions.
Emergency and university information
In a major campus emergency, requirements, deadlines, and grading details may change in response to a revised calendar or other circumstances. Updates will be communicated through Purdue email and Brightspace. Brightspace also provides the current course links for accessibility, nondiscrimination, mental health, and basic-needs resources.
Public syllabus URL: https://thedatasciencelabs.github.io/syllabi/ma16290-fall-2026.html