The course is intended for individuals looking to understand the basics of software engineering as they relate to building large software systems that leverage big data. You will be introduced to software engineering concepts necessary to build and scale large, data intensive, distributed systems. Starting with software engineering best practices and loosely coupled, highly cohesive data microservices, the course takes you through the evolution of a distributed system over time.
This course can be taken for academic credit as part of CU Boulder鈥檚 MS in Data Science or MS in Computer Science degrees offered on the 糖心vlog官网观看 platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on 糖心vlog官网观看 are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: /degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Transaction Processing, Continuous Integration, Service Level, Software Design, Data Architecture, Distributed Computing, Microservices, Software Architecture, Blockchain, System Monitoring, Data Structures, Software Engineering, Test Driven Development (TDD), Big Data, Maintainability, Database Systems
Reviews
3.5 (86 ratings)
5 stars
38.37%
4 stars
19.76%
3 stars
16.27%
2 stars
8.13%
1 star
17.44%
DR
Oct 18, 2023
Make sure you have a basic to intermediate understanding of Java to complete the Assignments. The instructions can be vague and implied given the experience you're supposed to already have with Java.
HC
Jul 3, 2024
I'd like the lecture notes to be well-organized, even outside of the lecture itself.
From the lesson
Fundamentals of Production Software
This week you will learn the fundamentals of monitoring software in production. You will learn how to create reliable background jobs, how to calculate and communicate service availability, and how to implement production metrics and monitoring.