MSDS 436-DL Analytics Systems Engineering.

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. They 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.

Students benefit by taking the Go Learning Studio and MSDS 431-DL Data Engineering with Go prior to taking this course.

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