Description:
Equipped with an introductory understanding of Python and data science topics from Data Science I, Data Science II will expose learners to more advanced models, techniques, and other important big data topics. Common machine learning models will be covered comprehensively from their mathematical formulation (e.g., least squares equations for regression) and model training (e.g., stochastic gradient descent) to their practical implementation (e.g., regularized regression in Python) and evaluation on real datasets. More advanced Python topics (e.g., objects and classes) will also be covered for learners to understand how build reusable modules implementing custom data science methods.
Dr. Chris Brinton, Assistant Professor of Electrical and Computer Engineering at Purdue University.
Professor Brinton’s research is at the intersection of data science and networking. His group develops data-driven optimization methodologies for communication and social networks, with a particular emphasis on distributed edge intelligence. Contemporary network architectures we investigate include Fog computing systems, the Internet of Things (IoT), and social learning networks (SLN), and our foundational techniques include convex and non-convex optimization, machine learning, and signal processing.