-
The course is intended for individuals who want to build a production-quality software system that leverages big data.
You will apply the basics of software engineering and architecture to create a production-ready distributed system that handles big data. You will build data intensive, distributed system, composed of loosely coupled, highly cohesive applications.
Applications of Software Architecture for Big Data can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Overview
Syllabus
-
- Project Overview
- In this module, we will introduce a project where you can apply some of the concepts from Fundamentals of Software Architecture for Big Data. You will learn about the expectations for the project as well as how to establish features for the project.
- MVP & Development Environment
- In this module you will learn about the concept of a Minimum Viable Product (MVP), how to incrementally add features to the MVP. Additionally, we will show you how to get going with a development environment and set up appropriate tests.
- Affixing Features
- This module builds upon an MVP created in the previous module. Here we show you how to create a database, populate the database as well as analyze the data in the database. The module ends by elaborating on testing.
- Scaling your MVP & Wrapping Up
- Here we add more features to the project inclusive of collaborative messaging. We end things off by building a simple health check for production monitoring and discussing acceptance testing.
