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  <meta name="description" content="Course information, textbooks, schedules, and syllabi for Purdue's Data Science Labs.">
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          <p class="site-title"><a href="/">The Data Science Labs</a></p>
          <p class="tagline">Hands-on data science and scientific computing alongside Purdue mathematics courses</p>
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        <ul><li><a href="/courses/">Courses</a></li><li><a href="/semesters/fall-2026/">Fall 2026</a></li><li><a href="/resources/">Resources</a></li><li><a href="/faq/">FAQ</a></li><li><a href="/archive/">Archive</a></li></ul>
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      <p class="eyebrow">Purdue University</p>
      <h1 id="welcome-heading">Mathematics you can measure, code, and explore</h1>
      <p class="lede">The Data Science Labs are one-credit companion courses that connect calculus, multivariable calculus, signals and systems, and probability to Python, Jupyter notebooks, sensors, microprocessors, and real data.</p>
      <div class="actions"><a class="button" href="/semesters/fall-2026/">View Fall 2026 offerings</a><a class="button secondary" href="/semesters/fall-2026/calendar.html">View the Fall calendar</a></div>
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      <h2 id="course-heading">Course sequence</h2>
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        <article class="card course-card"><a class="card-image-link" href="/courses/ma16290.html"><img class="card-image sensor" src="/assets/images/ma16290-ultrasonic-sensor.webp" width="862" height="709" loading="lazy" alt="Ultrasonic distance sensor, Raspberry Pi Pico, LEDs, and wiring on a breadboard"></a><div class="card-body"><h3><a href="/courses/ma16290.html">MA 16290: Calculus</a></h3><p>Build Python foundations and collect data with sensors while applying differential and integral calculus.</p><p><a href="https://thedatasciencelabs.github.io/DSLab_Calculus/">Open textbook</a></p></div></article>
        <article class="card course-card"><a class="card-image-link" href="/courses/ma26190.html"><img class="card-image" src="/assets/images/ma26190-lab-experiments.webp" width="621" height="457" loading="lazy" alt="Colorful individually controlled LEDs connected to a Raspberry Pi Pico on a breadboard"></a><div class="card-body"><h3><a href="/courses/ma26190.html">MA 26190 / ECE 29595: Multivariable Calculus</a></h3><p>Use vectors, color, images, video, and line integrals in computational laboratory projects.</p><p><a href="https://thedatasciencelabs.github.io/DataLab_Multivariate_Calculus/intro.html">Open textbook</a></p></div></article>
        <article class="card course-card"><a class="card-image-link" href="/courses/ma34990.html"><img class="card-image diagram" src="/assets/images/ma34990-fourier-instrument.webp" width="1843" height="899" loading="lazy" alt="Electronic theremin circuit labeled with Raspberry Pi Pico, audio amplifier, speaker, tuner, and ultrasonic sensor"></a><div class="card-body"><h3><a href="/courses/ma34990.html">MA 34990 / ECE 39595: Signals and Systems</a></h3><p>Explore sound, transforms, frequency analysis, and signal processing with Python and hardware.</p><p><a href="https://thedatasciencelabs.github.io/DSLab_Fourier/">Open textbook</a></p></div></article>
        <article class="card course-card"><a class="card-image-link" href="/courses/ma41690.html"><img class="card-image simulation" src="/assets/images/ma41690-probability-simulation.webp" width="1019" height="867" loading="lazy" alt="Monte Carlo simulation with random points inside and outside a circle used to estimate pi"></a><div class="card-body"><h3><a href="/courses/ma41690.html">MA 41690 / ECE 39595: Probability</a></h3><p>Study random phenomena, image and video data, simulation, and classification through laboratory work.</p><p><a href="https://thedatasciencelabs.github.io/DSLab_Probability/">Open textbook</a></p></div></article>
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      <h2 id="how-heading">Build it, measure it, model it</h2>
      <p>Every lab follows the same hands-on arc:</p>
      <ol class="process-list">
        <li><strong>Build with hardware</strong>Connect sensors, microprocessors, LEDs, speakers, and other components.</li>
        <li><strong>Collect real data</strong>Use Python and Jupyter notebooks to measure physical or simulated systems.</li>
        <li><strong>Apply the mathematics</strong>Use ideas from the companion course to analyze, explain, and improve the result.</li>
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      <h2 id="benefits-heading">Why add a Data Science Lab?</h2>
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        <article class="card"><h3>One credit</h3><p>Each course is a focused laboratory companion to a mathematics or engineering course.</p></article>
        <article class="card"><h3>Hands-on projects</h3><p>Students leave with working code, experience with physical systems, and examples they can discuss in future courses and interviews.</p></article>
        <article class="card"><h3>Honors pathway</h3><p>Eligible students can use a lab to support an honors contract in the paired course; see the current syllabus for details.</p></article>
        <article class="card"><h3>Open materials</h3><p>The laboratory textbooks are published online and can be explored before registration without a Purdue login.</p></article>
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      <h2 id="next-heading">Ready to share or register?</h2>
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        <div class="audience-panel"><h3>Students</h3><p>Find the lab that matches your mathematics or engineering course, then review the Fall 2026 sections and CRNs.</p><p><a class="button" href="/semesters/fall-2026/">Find a Fall 2026 section</a></p></div>
        <div class="audience-panel"><h3>Advisors and instructors</h3><p>Use the concise advisor guide to match students with a lab and share accurate registration information.</p><p><a class="button" href="/resources/advisors.html">Open the advisor guide</a></p></div>
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    <div class="notice info"><strong>Enrolled students:</strong> Brightspace and Gradescope remain the authoritative sources for assignments, submissions, grades, and deadline changes.</div>
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