MSDA Study Plan
A Master of Science in Data Analysis (MSDA) program typically equips students with an in-depth understanding of data analysis, encompassing both theoretical foundations and practical applications within the discipline.
| Semester 1 | Credit Hours | Prerequisite | |
|---|---|---|---|
| MDA6000 | Statistics for Data Analytics | 3 | None |
| MDA6001 | Big Data management | 3 | None |
| MDA6002 | Research Methodology | 3 | None |
| Total | 9 | ||
| Semester 2 | Credit Hours | Prerequisite | |
| MDA6103 | Big data Analytics | 3 | MDA6000 |
| MDA6104 | Machine Learning & Data Mining | 3 | MDA6000 |
| MDA6105 | Big Data Visualization | 3 | MDA6001 |
| Total | 9 | ||
| Semester 3 | Credit Hours | Prerequisite | |
| MDA6106 | Deep Learning for Big Data | 3 | MDA6104 |
| MDA6999 | Master Thesis | 6 | Department Approval |
| *Elective (Any 1 out of three) | 3 | ||
| Total | 12 | ||
| Semester 4 | Credit Hours | Prerequisite | |
| MDA6999 | Master Thesis (continuation) | - | Department Approval |
| Total credits for MSDA | 30 | ||
| *Electives | |||
| Code | Course | Credits | Prerequisite |
| MDA6110 | Cloud Computing for Big Data | 3 | MDA6001 |
| MDA6111 | Big Data Analytics in Cybersecurity | 3 | MDA6001 |
| MDA6112 | Generative AI and Big Data | 3 | MDA6104 |