Data Mining (INFSCI 2160)
This course will introduce the core data mining concepts and practical skills for applying data mining techniques to solve real-world problems. Topics cover major data mining problems as different types of computational tasks (prediction, classification, clustering, etc.) and the algorithms appropriate for addressing these tasks, as well as systematic evaluation and model assessment. Students are expected to design and implement data mining applications using real-world datasets, and to leverage cloud-computing services to build big data analytics projects.
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Information Visualization (INFSCI 2415; cross-listed with LIS 2690)
Visualization is a way to explore, present and express meaning in data, so there is no visualization without data. This course aims to investigate what data presents and how to present data. It will introduce concepts, methods and procedures of data visualization, with emphasis on the creative process of organizing, visualizing, communicating and interacting information. Students are expected to design and implement visualization systems using real-world datasets, and evaluate the systems in practical scenarios.
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Seminar: Social Networks and Graph Analysis (INFSCI 3350)
Social networks (friendship, co-authorship), information networks (the Web) and communication networks (emails, tweets and retweets) play a central role in the transmission of information and are critical to the trade of many goods and services. This doctoral seminar course will examine modern techniques for capturing, modeling and understanding the structure and dynamics of real-world networks. It covers topics such as: theoretical and empirical background on social and economic networks, concepts used to describe and measure networks, with a particular emphasis on graph-based data mining techniques, such as network dynamics, information diffusion, community detection, link prediction, etc. Substantial reading and project will be required. (Prerequisites: Data mining, machine learning or relevant course are suggested. Basic courses in Linear Algebra, Probability and Statistics are required.)

Seminar: Data Science (INFSCI 3250)
This seminar course will discuss advance techniques and key ideas in state-of-the-art data science research, covering topics including data mining, network science, computational social science, business intelligence, health informatics, etc. Substantial reading and project will be required. (Prerequisite: data mining or relevant course; registration requires instructor's permission.)

Tutorials & Workshop