CSD 438 Big Data Application Development

5 credits

Students are introduced to techniques and tools used to manage, process, and interact with massive datasets. The course explores non-relational (NoSQL) data storage for big data applications. The course covers topics such as distributed data storage, MapReduce, key value stores, stream processing, data mining, and basic statistical techniques to perform data analytics.

Prerequisites: CSD 331, CSD 425, and admission to the BAS IT:CSD program

Course Outcomes
Upon successful completion of this course students will be able to:

Explain the fundamentals of big data and analytics
Implement data pipelines to integrate data from a variety of sources
Use publish-subscribe and message queue models to acquire data
Run batch analytics using the MapReduce programming model
Perform real-time analysis using Apache Spark and Storm
Implement reporting APIs and dashboards

Program Outcomes
This course teaches to the following program outcomes:

demonstrate competency in software engineering, software testing principles, and quality assurance techniques
implement program management concepts
identify, evaluate, and apply efficient algorithms and technologies required for developing software system
demonstrate ability to understand and integrate contributions to the architecture design of a large software system
be prepared to obtain an entry-level position at a software development company

Total Hours: 60 Theory (Lecture) Hours: 40 Guided Practice (Lab or Clinical) Hours: 20