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 DatesDay(s)TimeBuildingSection
09/24/24 - 12/14/24Sync Session M
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Miller, Thomas
Online
Open
^ Back to top ^