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Business Analytics Graduate Certificate

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From Silicon Valley to Capitol Hill, most major decisions are driven by one thing: Data. To stay ahead of the curve and succeed in this era of Big Data, you need the advanced analytical techniques that will help your organization make the most of the information within its reach.

Through collaboration with the GW School of Business, our Graduate Certificate in Business Analytics is a hands-on program designed specifically for working professionals to learn how to utilize actionable intelligence from large amounts of data. In less than a year, you can earn your certificate by working with top experts on Python, R, SAS and Google Analytics, finding relationships hidden in data sets, creating dynamic visualizations and more. Plus, students enrolled in our flexible part-time program can still access the same high-quality curriculum as full-time students, potentially moving towards a Master’s degree.

 

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""At a Glance

A laptop with a line graph on it

The program prepares you to succeed in the era of “Big Data” by equipping you with advanced analytical techniques needed to extract meaningful insights from large data sets. 

A line graph

You will gain hands-on experience with Python, R, SAS, and Google Analytics.

A graduation cap

You can use this certificate to advance to a Master’s Degree in Business Analytics.

Less than one year

You can complete this program in less than one year.

""Where You’ll Study

Where you'll study
Evening and Saturday classes at the Virginia Science and Technology Campus in Ashburn, Virginia

Our part-time program offers evening and Saturday classes at the Virginia Science and Technology Campus, providing you the opportunity to balance life, work and school.

""What You'll Study

A professor lectures to students in a boardroom setting

 

 

Through collaboration with the GW School of Business, we’ve developed this program for ambitious professionals who are eager to dig into data. Students enrolled in our flexible part-time program can still access the same high-quality curriculum as full-time students at the Foggy Bottom campus, potentially moving towards a Master’s degree.

Course Schedule

  • DNSC 6206: Stochastic Fndn: Prob Models (1.5 credits)
  • DNSC 6203: Statistics for Analytics (1.5 credits)
  • DNSC 6211: Programming for Analytics (3 credits)
  • DNSC 6403: Visualization for Analytics (1.5 credits)
  • DNSC 6290: Special Topics. (0-3 credits)
  • DNSC 6279: Data Mining (3 credits)

 

 

Course Descriptions

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.

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.

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.

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.

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.

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.

""Admissions Information

Applications are being accepted for the Fall 2018 semester for the Graduate Certificate in Business Analytics. You will apply online and do not need to submit a GRE or GMAT score, however there may be other requirements to keep in mind as a Non-School of Business student applicant. The GW School of Business reviews applications to this program.

GW tuition and fees are comparable to the national average for private universities. These costs are set by the GW Board of Trustees and generally increase year to year, variable by program and location. Please use this information as an estimate based on current tuition rates and fee structures. Total tuition and fees will vary according to the courses taken and the timeframe in which you complete your coursework.

 

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

 

$19,200

Registration Fees:

2 registration sessions @ $35 each

 

$70

TOTAL:

Tuition + Fees

 

$19,270

 

Other costs to consider:

Application fee:

$80 

Matriculation fee (one time):

$200

 

 

""Contacts

 

Caroline Farris

Assistant Director, Graduate Admissions, School of Business
202-994-1212
[email protected]


 

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Request Information

Information Sessions

We periodically offer online and in-person information sessions about Business Analytics. Contact the program representative to learn more about upcoming events.