This graduate certificate is targeted at upgrading the skills of current practitioners. The federal statistical system is a complex data collection and analysis system that requires a wide variety of multidisciplinary skills for its maintenance. The Federal Statistics Systems Certificate is intended to respond to the need for broad training in statistics, survey methods, and data analysis, including graphics and data visualization. The program is extremely flexible and can be tailored to the needs of students within the federal statistical sector. It is also intended to be responsive to the needs of those in state and local governments, and those in the private sector involved in the collection, interpretation, or statistical analysis of federal data.
Admissions
Applicants should have an undergraduate degree from an accredited institution, with a minimum overall GPA of at least 3.00 (on a 4.00 scale).
Policies
For policies governing all graduate certificates, see AP.6.8 Requirements for Graduate Certificates.
Formerly: EC-CERG-ASTA
Certificate Requirements
Total credits: 12
This certificate may be pursued on a part-time basis only.
Some courses may have prerequisites beyond minimal admission requirements for which students must qualify or seek a waiver from the course instructor.
Core Courses
| Code | Title | Credits |
|---|---|---|
| STAT 515 | Applied Statistics and Visualization for Analytics | 3 |
| STAT 554 | Applied Statistics I | 3 |
| Total Credits | 6 | |
General Certificate (no concentration)
| Code | Title | Credits |
|---|---|---|
| Select 6 credits from the following: | 6 | |
| Biostatistical Methods | ||
| Applied Statistical Learning | ||
| Survey Sampling I | ||
| Case Studies in Data Analysis | ||
| Applied Statistics II | ||
| Statistical Graphics and Data Visualization | ||
| Total Credits | 6 | |
Concentration in Modern AI (MOAI)
| Code | Title | Credits |
|---|---|---|
| Required Course: | 3 | |
| Foundations and Practice of Machine Learning for Artificial Intelligence | ||
| Select one from the following: | 3 | |
| Planning and Decision Making for Intelligent Agents | ||
| Foundations and Practice of Deep Learning for Artificial Intelligence | ||
| Applied Statistical Learning | ||
| Case Studies in Data Analysis | ||
| Statistical Graphics and Data Visualization | ||
| Total Credits | 6 | |