Did You Know?

The amount of digital data being created globally is doubling every two years, and the majority of it is generated by consumers, according to IDC. Yet only about 0.5 percent of that data is ever analyzed.

Retailers exploiting data analytics at scale across their organizations could increase their operating margins by more than 60 percent according to the McKinsey Global Institute.

Gartner, Inc. states that investment in big data technologies continues to expand, finding that 73 percent of respondents have invested or plan to invest in big data in the next 24 months.

Graduate Certificate in Business Analytics

Offered by the GW School of Business and administered by the College of Professional Studies

Turn Data into Direction and Help your Organization Prosper

With a Graduate Certificate in Business Analytics designed specifically for working professionals, you can help your company or organization make the most of the information in its reach.

  • Explore and discover actionable business intelligence from large amounts of data
  • Create compelling, interactive, and automated scripts and programs that help you better understand your customers
  • Uncover relationships hidden in data sets and build predictive models
  • Monitor business processes, discover patterns and opportunities, and take data-driven action to defend against competitive threats
  • Present findings in innovative and dynamic visualizations that help to explain complex concepts in succinct ways
  • Hands-on experience with Python, R, SAS, and Google Analytics

Completion of this graduate certificate prepares you with the concepts, techniques, and skills necessary to apply data science skills in the business domain, and potentially moving forward into a full Master's degree in Business Analytics.

Course Schedule and Descriptions

The School of Business and the College of Professional Studies have collaborated to develop a program designed for ambitious professionals who are eager to dig into their data. Evening and Saturday classes at the Virginia Science and Technology Campus will help these individuals balance life, work, and school in order to complete this credential in less than a year, accessing the same high-quality curriculum as full-time students at the Foggy Bottom campus.

SEMESTER 1 - Fall 2018

DNSC 6206 (1.5 credits) DNSC 6203 (1.5 credits)
DNSC 6211 (3 credits)

SEMESTER 2 - Spring 2019

DNSC 6403 (1.5 credits) DNSC 6290 (1.5 credits)
DNSC 6279 (3 credits)

DNSC 6206 | Stochastic Foundations: Probability Models
Gain an understanding of key probability concepts, measures, models, and graphical representations. This course introduces the foundations of Probability, along with the commonly used Probability models (Binomial, Normal, and Poisson) in predictive analytics. Topics covered include probability laws, probability models for modeling dependence, univariate and bivariate models and their applications, conditional mean models including simple regression and extensions to probit and logit models. The course will primarily involve using R, and occasionally Microsoft Excel.

DNSC 6203 | Statistics for Analytics I
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.

DNSC 6211 | Programming for Analytics
This course emphasizes and focuses on concepts, techniques and tools that will prepare you to apply data science skills in the business domain. Accessing, preparation, handling, and processing data that differ in variety, volume, and velocity. The ability to handle and process data is a core capability in the context of any analytics position in the industry. Development of a theoretical grounding in emerging paradigms like schema-less data. The programming environments that will be typically employed include Python and R.

DNSC 6279 | Data Mining
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. This course provides an introduction to data mining concepts, methods, and tools with concrete examples from business applications. 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. Techniques covered include regression models, decision trees, neural networks, clustering, and association analysis.

DNSC 6403 | Visualization for Analytics
Data visualization platforms are essential tools for monitoring business processes, discovering patterns, and taking data-driven action to defend against competitive threats or to obtain opportunities. Move beyond traditional business graphics (i.e. bar and pie charts) and incorporate more dynamic and interactive graphic expressions when conveying conclusions drawn from more complex (i.e. thousands of dimensions or attributes) or larger (i.e. billions of rows) data sets. From pure data exploration to the delivery of valuable data-driven insights, visualization is an important application in the business analytics discipline. Go beyond the basic theory and into the hands-on approaches necessary to use data visualization software technology, such as SAS Visual Analytics, Visual Statistics, and Visual Data Mining & Machine Learning, in the context of analytical, exploratory and reporting capabilities.

DNSC 6290 | Digital Analytics
Digital Analytics has become an integral part of core business strategies and maintaining a competitive edge, especially when examining how the digital presence of any organization (websites, mobile applications, etc.) and today’s “connected consumers” interact. This class is designed for individuals who recognize the importance of digital data sources for analytics and data-driven organizations. Examine thought-leadership viewpoints and develop hands-on technical skills using Digital Analytic software technology in the context of analytical, exploratory and reporting capabilities. Learn by doing with a focus on data discovery and communicating insights.

Tuition and Fees

Graduate tuition and fees at The George Washington University are comparable to the national average for private universities. These costs, set by the GW Board of Trustees, generally increase from year to year and may vary by program and location.

Tuition is made up of many variables, so all calculations below are estimates based on current tuition rates and fee structures. Total tuition and fees will adjust depending on the courses taken and the rate at which you complete your coursework. Please use the following information as an approximate tuition amount based on current information and not your final investment which will appreciate over time.

Graduate Certificate Curriculum:
Two courses, 3 credits each
Four courses, 1.5 credits each
12 Credits      
Tuition (Summer 2017-Spring 2018 Terms):
12 credits @ $1,600/credit hour
Registration Fees:
2 registration sessions @ $35 each
Tuition + Fees
Other costs to consider:
Application fee
Matriculation fee (one time)
For a detailed list of fees please refer to The George Washington University Bulletin -- Graduate Programs
Admissions Requirements

Applications are being accepted for the Fall 2018 semester for the Graduate Certificate program in Business Analytics. It will be offered at the GW Virginia Science and Technology Campus in Ashburn, VA. 

When completing your application, you are not required to submit a GRE or GMAT score, though further details on other requirements are outlined here for Non-School of Business Student Applicants.


Program Representative:

Jim Miller
(571) 553-0142

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