DSBA 6190: Cloud Computing for Data Analysis
University of North Carolina, Charlotte
Introduction to the basic principles of cloud computing for data analysis and data-intensive applications. Covers a broad range of technologies and solutions used in designing cloud-based data platform architectures and in performing analysis on massive data sets. Focuses on the scalable deployment of cloud resources and the integration between individual services. Topics covered will include the deployment and use of data architectures with data lakes and data warehouses, containerized applications, distributed computing using cluster technologies such as Apache Spark, machine learning, and deep learning using scalable/GPU-based infrastructure. Course may be taught on Microsoft Azure, Amazon Web Services, or another large enterprise cloud provider, depending on the instructor.
Average GPA: 3.91
Grade distribution records: 449 students across 17 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 405 | 90.2% |
| B | 32 | 7.1% |
| C | 4 | 0.9% |
| W | 7 | 1.6% |
Based on 449 student grade records across 17 terms and 6 professors.
Instructors
- Robert Fox 177 students, Average GPA 3.89
- Colby Ford 154 students, Average GPA 3.93
- Angelina Tzacheva 45 students, Average GPA 3.93
- Noah Gift 38 students, Average GPA 4.00
- Sabyasachi Gupta 25 students, Average GPA 4.00
- Srinivas Akella 10 students, Average GPA 3.33