Data-driven decision making is critical in today’s business and economic environment. At the unique intersection of federal government agencies, Fortune 500 businesses, and advanced tech-savvy companies, the greater Washington, D.C. region is a true epicenter of data analytics. The graduate certificate in business analytics at the GW School of Business prepares students to succeed in this era of “Big Data,” and graduates are trained to harness the power of data through descriptive, predictive and prescriptive methodologies.
Weeknight and Saturday Options
Students admitted to the graduate certificate in business analytics have the option of enrolling in one of two sections: weeknight classes on GW’s Foggy Bottom campus or evening and Saturday classes at our Virginia Science and Technology Campus (VSTC) in Ashburn, Virginia.
Please note, each section has its own course offerings and schedule, and the evening and Saturday classes in Ashburn, VA will only be offered if ten or more students express interest in that section. Please contact us for more information if you are interested in evening and Saturday classes.
- Completion of all required and elective credits from the course options listed below
- Completion of the certificate with a cumulative grade point average of 3.0 or above
In addition, students should be aware of the following course application limitations:
- Courses taken by non-degree students are not eligible to be applied towards certificates.
- Students may only apply a course to two program requirements (for example, MBAD 6224 may be applied to both the MBA and the graduate certificate, but that course cannot subsequently be applied to any other degree or certificate requirements).
- Students who choose to enroll in two graduate certificates may only "double count" up to six credits between the two certificates; all remaining requirements (typically, 18 credits for two certificates) must be unique to each certificate.
Successful applicants to the graduate certificate in business analytics should have a quantitative background*, including:
Applicants should have taken and earned a grade of B or higher in an undergraduate or graduate statistics course in the past five years, and be able to demonstrate regular use of statistics in a current or past professional role or be able to demonstrate in another way an adequate understanding of statistics.
- Higher Level Mathematics (Calculus and Linear Algebra)
Applicants need to be able to demonstrate regular use of mathematical principles and methodologies. Applicants should have taken and obtained a grade of B or higher in undergraduate- or graduate-level calculus classes, including Calculus I and Calculus II, and had academic exposure to topics in Linear Algebra and Finite Math.
- Computer Programming
Applicants need to be able to understand basic computer programming and software principles and demonstrate the ability to learn Python and R programming.
*Applicants may satisfy the above prerequisites through an online offering such as Coursera.
Required Courses • 9 Credits
Statistics • 3 Credits
- MBAD 6224 | Decision Making and Data Analysis (3 cr.)* OR
- DNSC 6202 | Mathematics and Statistics for Management (3 cr.)* OR
- DNSC 6203 | Statistics for Analytics (1.5 cr.) AND
- DNSC 6206 | Stochastic Fndn: Prob. Models (1.5 cr.)
Students may choose to complete either MBAD 6224, DNSC 6202, or the combination of DNSC 6203 and DNSC 6206. Students who opt to complete MBAD 6224 or DNSC 6202 must attain a grade of B+ or better. Students who do not attain a B+ or better will be required to complete DNSC 6203 and DNSC 6206.
Additional Required Courses • 6 Credits
- DNSC 6211 | Programming for Analytics
- DNSC 6279 | Data Mining
Electives • 3 Credits
- DNSC 6209 | Forecasting for Analytics
- DNSC 6214 | Pricing and Revenue Management
- DNSC 6215 | Social Network Analytics
- DNSC 6225 | Business Process Simulation
- DNSC 6251 | Optimization Models for Decision Making
- DNSC 6252 | Risk Analysis for Decision Making
- DNSC 6403 | Visualization for Analytics
- DNSC 6404 | Sports Analytics
- DNSC 6290 | Special Topics: Big Data
- DNSC 6290 | Special Topics: Digital Analytics
- DNSC 6290 | Special Topics: Supply Chain Analytics
- DNSC 6290 | Social Network Analysis for Managers
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