Numerical Methods for Data Sci

3 Credits · Level: Graduate, Undergraduate

Prerequisites: MATH 5052 & MATH 5200 ; familiarity with programming and basic data science concepts would be beneficial but not mandatory. Description: This MS level course provides a deep dive into the numerical techniques and algorithms that form the foundation of modern data science. Students will explore the mathematical principles and computational methods essential for solving large-scale, data-driven problems in science, engineering, and beyond. Key topics include (1) numerical linear algebra and matrix computations, critical for handling high-dimensional datasets; (2) optimization techniques for model training and tuning; (3) numerical solutions to differential equations in predictive modeling; (4) probabilistic and statistical numerical methods for uncertainty quantification; (5) applications in machine learning, big data analytics, and scientific computing. Hands-on programming exercises using Python or R will be integrated throughout the course, allowing students to implement and experiment with algorithms on real-world datasets. By the end of the course, participants will gain a robust understanding of numerical methods, enabling them to tackle complex problems in data science with precision and efficiency.