Advisor and instructor quick guide
The Data Science Labs are interdisciplinary, one-credit companion courses that help Purdue students turn mathematics into working code, physical measurements, simulations, and data analysis.
Spring 2027 registration: use the current section and CRN table. Share that dated page instead of copying CRNs into email that may be reused in a later semester.
Match a student with a lab
| Lab | Take alongside | Coding preparation | What students build or analyze |
|---|---|---|---|
| MA 16290 | MA 16200 or MA 16600 | No previous programming course required | Sensors, data acquisition, Python foundations, measurement, and error |
| MA 26190 / ECE 29595 | MA 26100 | MA 16290 or prior Python experience | Color, images, video, vectors, and line integrals |
| MA 34990 / ECE 39595 | ECE 301, AAE 301, MA 428, or MA 34900 | Prior Data Science Lab or prior Python experience | Sound, transforms, filtering, and electronic instruments |
| MA 41690 / ECE 39595 | ECE 302, AAE 361, MA 416, STAT 416, or STAT 311 | Prior Data Science Lab or prior Python experience | Simulation, random processes, images, video, and classification |
Useful points to tell students
- Each lab is a focused, one-credit hands-on course connected to mathematics the student is already learning.
- Students use open online textbooks and can preview the materials before registering.
- Honors-contract option: eligible students may use a lab's interdisciplinary work to support an honors contract in the paired course; college procedures and approval requirements apply.
- The ECE and MA numbers are cross-listed versions of the same laboratory where shown.
Copy-and-share description
The Data Science Labs are interdisciplinary, one-credit Purdue laboratory courses that connect calculus, multivariable calculus, signals and systems, and probability to Python, sensors, microprocessors, simulations, and real data. Eligible students may also use a lab to support an honors contract in the paired course. Explore the projects and current sections at thedatasciencelabs.github.io.
Questions
For course fit, honors-contract, or registration questions not answered here, contact Prof. Kaitlyn Hood at kthood@purdue.edu.