SAS Joint Certificate Program


The Master of Science in Business Analytics (MSBA) program is proud to announce the SAS Joint Certificate in Business Analytics. Endorsed by SAS®, the 15-credit certificate will be available to MSBA students this fall. Participants will analyze real-world data using a variety of analytics, from simple descriptive methods to more sophisticated predictive and prescriptive approaches. 


The certificate program is a result of a confluence of factors: the urgent need for more trained STEM workers in the U.S., the D.C. metro region’s reputation as a focus for business analytics, the growing importance of being able to harness the power of “big data,” and the attractive and lucrative career paths for students with STEM training.

What students at the GW School of Business (GWSB) are learning could land them the hottest jobs today. GWSB is embedding SAS Analytics technology, coursework and faculty into programs to cultivate high-demand analytics talent. A massive study of 54 million employee profiles recently examined which career skills translated into salary bumps — and knowledge of SAS was number one on the list.


The certificate is available only to students enrolled in the MSBA program, effective fall 2016.


To receive the SAS Certificate in Business Analytics, students must complete 15 credits from the following courses:

DNSC 6231 | Consulting for Analytics | 1.5 Credits

An introduction to business analytic concepts, methods, and tools with concrete examples from industry applications; Big Data and the opportunities it has created for businesses to store, organize, and analyze vast amounts of information.

Prerequisites: Completion of a basic course in statistics prior to enrollment is recommended.


DNSC 6203 | Statistics for Analytics I | 1.5 Credits

The foundations of statistical methodologies used in business analytics; statistical inference and probability models; methods of estimation, hypothesis testing, contingency table analysis, analysis of regression models and logit and probit analysis. Restricted to students in the master of science in business analytics program; departmental approval may be substituted.

Prerequisites: DNSC 6206


DNSC 6206 | Stochastic Foundations: Probability Models | 1.5 Credits

A description for this course is not currently available. Please check back soon.

University Bulletin | DNSC 6206


DNSC 6213 | Statistics for Analytics II | 1.5 Credits

Statistical methodologies for business analytics in real world scenarios; introduction of high-level analytical techniques such as hierarchical linear modeling and mixed-effects modeling. Restricted to students in the MS in business analytics and graduate certificate in business analytics programs or with departmental permission.

Prerequisites: DNSC 6203


DNSC 6216 | Business Analytics Skills Workshops | 1.5 Credits

A series of workshops covering project management techniques for analytics projects, team dynamics skills, communicating quantitative information, and ethics, security, and privacy policies in analytics.

Prerequisites: n/a


DNSC 6217 | Business Analytics Practicum | 1.5 Credits

Working in small teams, students apply their analytics skills to projects sponsored by public or private institutions. Each team is advised by a faculty member, and the practicum sponsor designates a mentor to provide guidance to the team for the duration of the project.

Prerequisites: MSBA Degree Candidacy


DNSC 6219 | Time Series Forecasting for Analytics | 3 Credits

Predictive analysis and blackbox models for time series and econometric forecasting; identifying hidden patterns and structures in the univariate and multivariate time series data and exploiting these for forecasting; use of SAS to apply different forecasting models and methodologies to real life time-series data. Restricted to students in the master of science in business analytics degree program; program approval may be substituted.



DNSC 6279 | Data Mining | 3 Credits

How organizations make better use of the increasing amounts of data they collect and how they convert data into information that is useful for managerial decision making. Examination of several data mining and data analysis methods and tools for exploring and analyzing data sets. State-of-the-art software tools for developing novel applications.

Prerequisites: n/a

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