Academic Plan of Study
Data Analytics Engineering program in GMU provides vary concentrations and covers many topics in the field. I choose an individual academic plan, because I would like to get a general understanding about data- driven techniques and methodologies, then focus on deeply explore 3 or 4 aspects or techniques, such as R, Python, Hadoop, etc. I’m plan to get B+ up of each course, so can scarify some courses’ prerequisites. Except study from the grogram, I’m trained related technical knowledge and skills in last and this Summer. Besides, I’m looking for a Data Analyst internship in CA this Summer. That would help me practice and enhance my data analysis knowledge.
Below table shows my academic plan of study in the program. The courses in 2017 Fall could be changed in future.
Degree Requirements (30 credits)
| Course/Credits | Semester |
| OR531 Analytics and Decision Analysis / 3 | 2016 Spring |
| AIT580 Analytics: Big Data to Information / 3 | 2016 Fall |
| STAT515 Applied Statistics and Visualization for Analytics / 3 | 2016 Fall |
| AIT 582 Applications of Metadata in Complex Big Data Problems / 3 | 2017 Spring |
| CS 504 Principles of Data Management and Mining / 3 | 2017 Spring |
| SYST 568 Applied Predictive Analytics / 3 | 2017 Spring |
| SYST 573 Decision and Risk Analysis (3 credits)/ 3 | 2017 Fall |
| STAT 663 Statistical Graphics and Data Exploration/ 3 | 2017 Fall |
| SYST 664 Bayesian Inference and Decision Theory/ 3 | 2017 Fall |
| DAEN 690 Data Analytics Project / 3 | 2017 Fall |