Fall 2024
INF 385T Special Topics in Information Science: Foundations of Data Science
Mon
DESCRIPTION
This class explores various data science models, both traditional and the state of the art techniques. The course is designed to provide mathematical and computational basis such as Linear Algebra, Optimization techniques, and probabilistic modeling for different types of machine learning models. The goal of the class is provide a foundational basis for data science techniques. The class focuses on PSETs and a final data science project.
COURSE NOTES
The proliferation of open-source and proprietary data sciences packages has enabled the rise of low-code and no-code data science practices. This level of abstraction is undoubtedly helpful to users with limited mathematical knowledge. However, the data science packages are primarily computational packages that heavily rely on sophisticated algorithmic techniques based on different branches of mathematics, such as linear algebra, probabilistic theory, and convex optimization. To better understand data science and contribute to its research in theory and practice, it becomes imperative to know and understand these broad mathematical concepts in the context of various tools, techniques, and algorithmic designs. This course discusses and explores different data science techniques and their applications in light of the mathematical frameworks and Python libraries. In addition, the course examines essential survey papers that provide a comprehensive landscape of different topics.
PREREQUISITES
Graduate standing.
RESTRICTIONS
Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.