Data Science

INFO 523: Data Mining and Discovery

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns.

INFO 529: Applied Cyberinfrastructure Concepts

Students will learn from experts from projects that have developed widely adopted foundational Cyber infrastructure resources, followed by hands-on laboratory exercises focused around those resources. Students will use these resources and gain practical experience from laboratory exercises for a final project using a data set and meeting requirements provided by domain scientists. Students will be provided access to computer resources at: UA campus clusters, iPlant Collaborative and at NSF XSEDE.

INFO 521: Introduction to Machine Learning

Machine learning describes the development of algorithms, which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself.

INFO 515: Organization of Information

Introduction to the theories and practices used in the organization of information. Overview of national and international standards and practices for access to information in collections.

INFO 514: Computational Social Science

This course will guide students through advanced applications of computational methods for social science research.  Students will be encouraged to consider social problems from across sectors, including health science, environmental policy, education, and business.

INFO 510: Bayesian Modeling and Inference

Bayesian modeling and inference is a powerful modern approach to representing the statistics of the world, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data.

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