Data Science Master's

DATA 520. Databases. 3 Credits.

An introduction to the principles of Database design and management. Topics include query optimization, procedural extension of query languages, runtime error handling, normalization techniques, data warehousing, and NoSQL. Prerequisites: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

DATA 532. Applied Machine Learning. 3 Credits.

Implementation and application of common machine learning algorithms using a high-level programming language. Algorithms that employ supervised and unsupervised learning will be considered in the context of a variety of applications such as searching and ranking, text mining, and recommender systems. Prerequisites: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

DATA 530. Artificial Intelligence. 3 Credits.

This course provides a broad overview of the algorithms and applications of Artificial Intelligence. Topics include agent theory, problem-solving with search, constraint satisfaction, game theory, knowledge-based systems, reasoning, information retrieval, pathfinding and classification. Prerequisites: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

DATA 527. Predictive Modeling. 3 Credits.

The development of models that rely on statistical methods to predict future events based on past performance. Common modeling methods will be introduced, and procedures to determine the accuracy of models will be covered. Prerequisites: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

DATA 525. Data Engineering and Mining. 3 Credits.

This course studies theoretical and applied issues related to data engineering and mining. Data engineering is to identify, investigate, and analyze the underlying principles in the design and effective use of information systems; and data mining is to discover patterns in large data sets and transform the patterns into a comprehensible structure for further applications. The following topics are covered: data collection, data preparation, data indexing and storage, data processing and analysis, data classification and clustering, knowledge discovery, information retrieval, data visualization, data sharing, data applications, and some other special topics. Prerequisites: DATA 511, DATA 512, and DATA 513 or permission of the School of Electrical Engineering and Computer Science. On demand.

DATA 550. Data Security. 3 Credits.

Data is considered one of the most important assets for any organization. The course in data security covers security and privacy issues, models, policies, and techniques for protecting data against various types of threats. Prerequisites: DATA 511, DATA 512, and DATA 513, or permission of the School of Electrical Engineering and Computer Science. On demand.