DS 3010: Data Science III: Computational Methods
Introduces core methods in Data Science. It covers a broad range of methodologies for working with large and/or high-dimensional data sets to making informed decisions based on real-world data. Core topics include data collection through use cycle, data management of large-scale data, cloud computing, machine learning and deep learning. Students will acquire experience with big data problems through hands-on project susing real-world data sets.
Jun 6, 2025
CS 4432: Database Systems II
Concentrates on the study of the internals of database management systems. Topics include principles and theories of physical storage management, advanced query languages, query processing and optimization, index structures for relational databases, transaction processing, concurrency control, distributed databases, and database recovery, security, client server and transaction processing systems. Students may be expected to design and implement software components that make up modern database systems.
Jun 6, 2025
CS 4099: ST: Graph Machine Learning
An introduction to models, techniques & algorithms for analyzing and making predictions based on networked (graph) data. Prediction tasks include node classification, link prediction, graph classification, etc. Students will learn about theoretical foundations, building on linear algebra and probability concepts. Additionally, they will gain hands-on experience in working with real-world datasets, where nodes represent non-IID observations. Topics include, but are not limited to: node embeddings, graph neural networks and random graph models. Recommended Background: CS 4342 Machine Learning, MA 2071 Matrices and Linear Algebra I, MA 2621 Probability for Applications.
Jun 6, 2025