Course Descriptions and Schedule
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
MSDS 436-DL : Analytics Systems Engineering
Description
This course introduces design principles and best practices for implementing large-scale systems for data ingestion, processing, storage, and analytics. Students learn about cloud-based computing, including infrastructure-, platform-, software-, and database-as-a-service systems for data science. They evaluate system performance and resource utilization in batch, interactive, and streaming environments. They create and run performance benchmarks comparing browser-based and desktop applications. The evaluate key-value stores, relational, document, graph, and graph-relational databases.
Recommended prior course: MSDS 430-DL Python for Data Science or MSDS 431-DL Data Engineering with Go.
Prerequisites: (1) MSDS 420-DL Database Systems or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.