600

In this course, students will learn how to utilize advanced data mining techniques for use in improving decision making. The topics covered include generation of predictive models, optimal decision making, computational simulation systems, expert and recommendation systems.

The application of Data Science techniques is of increasing importance in computer security. Data mining and machine learning algorithms are now extensively employed in detecting cyber-attacks, developing authentication methods that distinguish legitimate from illegitimate users, and testing the strength of existing security technologies. In this course, students will learn how to use data mining techniques to solve real-world security problems, processing datasets, training models, and deploying solutions to strengthen a system’s defenses.

This course covers the theory and practice of data engineering. Students will learn about the data engineer role, data lineage, privacy and governance. Students will learn how to stitch together various technologies to securely serve the needs of downstream data consumers such as analysts, data scientists, and machine learning engineers. Students will be introduced to popular tools and techniques, such as Nifi, Spark, GraphQL, and ORC/Parquet, that are used by data engineers in the industry.