400

This course covers the study of systems for storing and processing large datasets. Covered concepts include standard architectures for Big Data, use of common software frameworks, and applications to batch and real-type systems. Students will work on projects using Big Data technologies such as Hadoop, MapReduce, Hive, Spark or NoSQL databases.

This course studies programs that use experience for improving their performance at solving a variety of tasks such as classification, regression, or clustering. Topics include supervised and unsupervised learning, reinforcement learning, parametric and non-parametric methods, ensemble learning and introduction to computational learning theory. Students will learn how to evaluate the performance of machine learning methods and how to utilize the techniques in various applications.

An introduction to the concepts, techniques, and systems of data warehousing and data mining, including (1) design and implementation of data warehouse and on-line analytical processing (OLAP) systems, and (2) data mining concepts, methods, systems, implementations, and applications.

In this course, students will work in teams to develop a data-driven solution for a real-world problem using data science methods, will document their work in a scholarly report, and present their methodology and results to faculty and peers. Students will identify appropriate project topics with help of the faculty, research appropriate current methods and technologies, then apply them to find a solution. The results will be presented in a form of a technical report and an oral presentation. Additionally, this course will cover topics in professional ethics, intellectual properties, privacy and professional communication.