The MS in Applied Business Analytics at UWG will equip students with the advanced analytical skills needed to succeed in a data driven world. The program will train students in the fundamentals of business intelligence and data analytics and prepare them for jobs as business analysts, business intelligence analysts, data analysts, data engineers, data scientists, data visualization specialists, econometricians, forecasters, and other related positions.

Students in the program will learn programming skills, data management skills, and modern statistical methods in a collaborative, project-intensive, hands-on environment.

A program sheet, which provides a required coursework sequence, is available for download in the Courses tab below.

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UWG’s STEM-designated M.S. in Applied Business Analytics prepares students to thrive in a world where decisions are driven by data. Students will learn how to analyze large datasets and apply modern statistical techniques to solve real-world business problems. The program focuses not just on general business but on specific industry-areas such as healthcare, athletics and sports, and retail, allowing students the flexibility to mix and match tracks according to their interests. This program is intended for students who have already successfully completed business statistics courses as an undergraduate and who graduated with a 2.75 or greater GPA (out of a 4.0 scale).]    

The MS in Applied Business Analytics program has 3 tracks: Healthcare Analytics, Data Intelligence, and Sports Analytics. Students also have the option of designing a different track and submitting it to the director of the program for approval. The four courses must make up a coherent theme.

Program Location

Carrollton Campus, Online

Method of Delivery

Fully Online Optional

Accreditation

The University of West Georgia is accredited by The Southern Association of Colleges and Schools Commission on Colleges (SACSCOC).

Credit and transfer

Total semester hours required: 30
Maximum Hours Transferable into program: 6
A transfer credit evaluation will be completed by the UWG Transfer Team (transfer@westga.edu). Course application to a program is subject to review by the department.

Graduate students may be able to reduce their cost through prior learning, previous degrees earned at UWG, or transfer credits. We have created a tool to help students estimate their tuition costs.                

This program may be earned entirely online, entirely face-to-face, or anything in between.

Save money.

UWG is often ranked as one of the most affordable accredited university of its kind, regardless of the method of delivery chosen. In addition, online courses and programs can mean a cost-savings in many non-evident ways: No more high gas charges. No childcare needed. The flexibility can allow one to maintain a job while attending school. Regardless of state residency, out-of-state non-resident students are not charged non-resident tuition for online course credit hours.

Details

  • Total tuition costs and fees may vary, depending on the instructional method of the courses in which the student chooses to enroll.
  • The more courses a student takes in a single term, the more they will typically save in fees and total cost.
  • Face-to-Face or partially online courses are charged at the general tuition rate and all mandatory campus fees, based on the student's residency (non-residents are charged at a higher rate).
  • Fully or entirely online course tuition rates and fees my vary depending on the program. Students enrolled in exclusively online courses do not pay non-Resident rates.
  • Together this means that GA residents pay about the same if they take all face-to-face or partially online courses as they do if they take only fully online courses exclusively; while non-residents save money by taking fully online courses.
  • One word of caution: If a student takes a combination of face-to-face and online courses in a single term, they will pay both all mandatory campus fees and the higher eTuition rate.
  • For the cost information, as well as payment deadlines, see the Student Accounts and Billing Services website

There are a variety of financial assistance options for students, including scholarships and work study programs. Visit the Office of Financial Aid's website for more information.

Coursework

This program can be completed fully face-to-face or fully online.

Tracks

Below are three possible tracks: Healthcare Analytics, Data Intelligence, and Sports Analytics. Students also have the option of designing a different track and submitting it to the director of the program for approval. The four courses must make up a coherent theme.

 

Healthcare Analytics Track (4 courses):

Students must take:

ECON 5415 –Healthcare Analytics 

NURS-6115 –The Business of Healthcare: Financial and Economic Evidence

And choose two (2) of the following:

ECON 6415 –Healthcare Economics 

NURS-6104 –Scholarly Inquiry and Data Analysis in Nursing

NURS-6109 –Info, Tech & Healthcare Outcomes

ECON 6430 – Business Cycles and Forecasting

MGNT 6684 – Internship

 

Data Intelligence Analytics Track (4 courses):

Students must take:

MKTG-6868 –Marketing Models

ECON 6430 – Business Cycles and Forecasting

And choose two (2) of the following:

ECON 6428 – Retail Analytics (NEW) ***

MKTG 6850 –Analytical Methods in Marketing

CISM 5330– Enterprise Architecture

MGNT6604–Production and Operations Management Fundamentals with Quantitative Applications

MGNT 6684 – Internship

 

Sports Analytics Track (4 courses):

Students must take:

SPMG 6300 – Introduction to Sports Analytics

SPMG 6310 – Big Data and Statistical Analysis in Sports

And choose two (2) of the following:

SPMG 6320 – Analytics in Sports Business

SPMG 6330 – Applied Network Analysis in Sports

ECON 6430 – Business Cycles and Forecasting

ECON 6460 – Economics of Sports (NEW)***

MGNT 6684 – Internship


Each track has one elective:
choose one from any of the courses above

Downloads

General

This course introduces Business Analytics students to modern methods used for creating, accessing, handling, processing, analyzing and presenting data from a variety of sources. This course emphasizes a hands-on, practical approach to data analysis with SAS, an industry-standard data intelligence software package available for MS Windows, Linux, and UNIX and other operating systems.

View Instructors, Syllabi and Other Details

This course provides a rigorous treatment to modern tools in data visualization and analytics. The materials will be organized around two overarching themes: 1) creating professional-looking charts in popular statistical software, and more importantly, 2) processing data and presenting analysis results in an effective and visually appealing manner. The first module of the course will demonstrate how to make charts in Microsoft Excel charts commonly used in business reports (e.g. trend graphs, pie charts, and bar graphs). We will also cover data management and preparation for various data structures and formats, such as importing and exporting data, merging and joining datasets, and reshaping, collapsing or aggregating data for analysis purposes. In the second module, we will dive into more advanced topics in visual analytics mainly using Tableau and R. We will cover how to create more sophisticated visualization tools such as thematic maps and interactive dashboards. Students will have the opportunity to work with various data examples and create their own interactive graphs (e.g. with publicly available financial data or healthcare data). Finally, we will cover how to combine data visualization tools with state-of-the-art data science techniques such as cluster analysis, tree-based methods, and natural language processing.

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This course provides an introduction to state-of-the-art analytical methods widely used in the healthcare industry. Students will gain exposure to a wide array of data across different healthcare settings (such as clinical data, encounter data, and health insurance claims data). The goal is to demonstrate how healthcare data can be used to generate insights and actionable items that can help various stakeholders (e.g., providers, patients, and regulatory agencies) improve business processes and deliver care at the most cost effective point. We will provide an in-depth treatment of core methods in healthcare evaluation, health economics and outcome research (HEOR), and predictive analytics. The course consists of three modules: (1) healthcare data processing and reporting; (2) quality and outcome measurement; and (3) modeling and predicting outcome and cost. We will be using R as the main statistical tool throughout the course.

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The course emphasis is on applications of econometrics and techniques in business analytics. Topics include methods of data presentation, numerical measures and correlation, estimation, linear/non-linear regression, limited dependent variables, simultaneous equations/instrumental variables, models of duration, and the use of these models in decision making processes. An industry-standard business analytics software will be used in this course.

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This course provides an introduction to the study of health economics. We will cover a wide range of important topics in the field, while focusing on the healthcare system in the United States. The first half of the course will be devoted to applying standard microeconomic theory to studying the behavior of various economic agents in the healthcare market (e.g. patients, physicians, hospitals, and insurance companies, etc.). In the second half of the course, we will examine the evolution of healthcare industry in the U.S. as well as the effects and implications of various government policies (such as Medicare, Medicaid, and the Affordable Care Act).

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This course discusses how retailers and manufacturers use customer data and modern analytic tools to make pricing, promotion, marketing, and managerial decisions. The course is very hands-on and all of the data we work with is from real industry cases.

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Designed to meet the rapidly growing need for a systematic approach to data analysis. Analytical methods used include an understanding of the more commonly-used statistical methods and the use of SPSS a software package which is helpful in the analysis of marketing data. Skill sets developed include the processing, analysis, and interpretation of data and information, and presentation of the results orally and in writing.

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This course will provide students with a methodology to measure and track marketing performance. The course has three primary objectives: Learn and understand key marketing metrics; Employ statistical software to analyze a firm's marketing performance through marketing metrics; Use the resulting analysis to make optimal marketing decisions.

View Instructors, Syllabi and Other Details

Adrian Austin, Ph.D.

Adrian Austin, Ph.D.

Professor of Economics

David J. Boldt, Ph.D.

David J. Boldt, Ph.D.

Professor of Economics

Cynthia Brown, DNS, RN, AHN-BC, NBC-HWC, CNE

Cynthia Brown, DNS, RN, AHN-BC, NBC-HWC, CNE

Professor

Laura Caramanica, PhD, RN, CENP, FACHE, FAAN, CNE

Laura Caramanica, PhD, RN, CENP, FACHE, FAAN, CNE

Graduate Program Director & Professor

Melanie Hildebrandt

Melanie Hildebrandt

Senior Lecturer of Economics

Kim Holder

Kim Holder

Senior Lecturer of Economics and Director, Center for Economic Education and Financial Literacy

Wooyoung (William) Jang, Ph.D.

Wooyoung (William) Jang, Ph.D.

Assistant Professor

Lizhong Peng, Ph.D.

Lizhong Peng, Ph.D.

Associate Professor of Economics

Beheruz N. Sethna, Ph.D.

Beheruz N. Sethna, Ph.D.

Regents' Professor of Marketing

Roy Richards Sr. Hall
Room 364
Michael Sinkey, Ph.D.

Michael Sinkey, Ph.D.

Associate Professor of Economics

William Smith, Ph.D.

William Smith, Ph.D.

Chair, David A. Johnson Professor in Predictive Analytics

Young Ik Suh, Ph.D.

Young Ik Suh, Ph.D.

Associate Professor

Guidelines for Admittance

  • All graduate applicants must complete the online Grad Application. A one-time application fee of $40 is required.
  • Applicants should also review the Graduate Studies Website for individual program specific requirements and tasks that must be completed prior to admission. See Graduate Studies Application Process.
  • International applicants are subject to additional requirements and application deadlines. See Procedures for International Students.
  • Official transcripts from a regionally or nationally accredited institution are required and should be sent directly to the UWG Graduate Admissions Office.

Program Specific Admittance Guidelines

  • Submit official transcripts from all post-secondary schools attended.
  • Applicants must have successfully completed business statistics courses as an undergraduate.
  • A 2.75 or higher undergraduate GPA on a 4.0 scale is required.

Application Deadlines

Specific Graduate Admissions Deadlines are available via the Graduate School

* Application, app fee, and document deadline

See The Scoop for more specific deadlines.

                  

Admission Process Checklist

The Graduate Studies Application Process checklist is available here

One exception: If you will not ever be traveling to a UWG campus or site, you may apply for an Immunization Exemption. Contact the Immunization Clerk with your request after successful admission.

Contact

Graduate Admissions
graduate@westga.edu 
678-839-1394

Richard's College of Business
Dr. Adrian Austin
aaustin@westga.edu
678-839-4773

Specific Graduate Admissions Deadlines are available via the Graduate School

* Application, app fee, and document deadline

See The Scoop for more specific deadlines.

                

1: Demonstrate proficiency in a business intelligence application.

2: Demonstrate proficiency in a data visualization package.

3: Apply modern data analytical techniques to address real world problems in industry.

4: Communicate effectively and professionally with data.

5: Understand ethical and legal concerns of working with data.