Puneet Agarwal

Puneet Agarwal

Pronouns: He/him/his

Assistant Professor, Department of Industrial & Manufacturing Engg. @ Cal Poly SLO

Teaching Philosophy

While teaching, I strongly rely on student-centered and problem-based strategies.

  • Student Feedback: I regularly encourage students in providing feedback so that I can leverage their expectations, needs, and interests in making necessary improvements and/or adjustments to the learning environment, course materials, and teaching style.
  • Learn by Doing: Instead of directly presenting raw technical concepts, I use real-world problems and examples to promote student learning of concepts and principles.
  • Effective Communication: I put in my best efforts to provide personalized attention using proper communication channels to help students overcome their struggles and master the course materials.
  • Prompt Response: I also respond to all emails from students promptly so that they can have their questions quickly answered and thus, have sufficient time to complete their tasks efficiently.
  • Grading and Feedback: I prioritize grading and returning assignments promptly with detailed comments so that students have enough time to review their work in preparation for future assignments and exams.

These efforts help students to feel more comfortable in initiating further contacts and generating a positive learning and feedback cycle.

Courses Taught @ Cal Poly - SLO

  • IME 212: Introduction to Enterprise Analytics
    • Term: Spring 2022
    • Course Description: Using Big Data for decision-making in Industrial Engineering. Data-driven solution design. Data acquisition and database queries. Cleaning, understanding, preprocessing, describing, and visualizing data. Analysis for insight. Storytelling and ethical considerations.
    • Syllabus: PDF (subject to change)
  • IME 372: Applications of Enterprise Analytics
    • Term: Winter 2022, Spring 2022
    • Course Description: Applications of Big Data Analytics to facilitate enterprise decision-making. Clustering, classification, and prediction. Applications of regression-based prediction and decision making. Over-fitting and regularization. Supervised and unsupervised learning. Machine learning, neural networks, and Bayesian analysis.
    • Syllabus: PDF (subject to change)