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.
Fall 2024 | ||||
Start/End Dates | Day(s) | Time | Building | Section |
09/24/24 - 12/14/24 | Sync Session M | 7 – 9:30 p.m. | 55 | |
Instructor | Course Location | Status | CAESAR Course ID | |
Miller, Thomas | Online | Open |