Department website: https://business.wvu.edu/academics/management-information-systems/business-data-analytics
Degree Offered
- Master of Science in Applied AI and Data Analytics
Certificates Offered
- Business Data Analysis
- Data Science and Applied AI
- Business Data Technology Management
- Business Operations Research
Nature of the Program
This program is designed to provide students with the ability to perform data analytics in order to enhance business decision making and increase organizational value. The Applied AI and Data Analytics degree provides students with a set of tools applicable in every business and industry. For this reason the program is attractive to both recent graduates in entry-level positions and experienced professionals. The certificate programs allow students to dive deeply in a specific area over the course of one or two semesters. Both the M.S. and certificate programs are designed for working professionals with one-, one-and-a-half, and two-year plans of study, and many find they are able to apply the concepts learned to their work before graduation.
The M.S. and certificates in Applied AI and Data Analytics are delivered online and may be completed from anywhere in the world. The program is asynchronous, with the exception of one required presentation at the end of the Capstone project. Students may choose to come to Morgantown or participate virtually in this presentation. Capstone projects allow students to showcase and expand their skills by engaging in an experiential learning project within their own company, or provided by a sponsoring organization. Capstone projects traditionally address issues from a broad range of industries, including banking, government contracting, manufacturing, and healthcare. Capstone presentations are an opportunity to present the Capstone Project course findings to the sponsoring company's leadership, unifying the technical and organization value found in the program.
M.S. Applied AI and Data Analytics graduates will understand emerging technology trends in the job market and be well-positioned, by way of their strong technology and analytical and quantitative skills, for career and lifelong success. Applied AI and Data analytics is a rapidly emerging segment in business and industry, and all indications are that it represents one of the fastest growing job markets and has a sustainable future. This program seeks to provide students with the knowledge, skills, and tools to successfully compete for a variety of positions in the emerging job market.
Academic Standards
In addition to the University’s academic and professional standards, students enrolled in a John Chambers College of Business and Economics master’s degree program must also abide by the following standards:
- Students must have a minimum cumulative GPA of 3.0 to earn a degree from their graduate program, without exception.
- A student who cannot mathematically meet the 3.0 GPA requirement to successfully complete the degree, within a reasonable period of time (as defined by the Program Coordinator or designee), will be dismissed from their academic program. Visit the Probation, Suspension, and Dismissal section of the University’s Graduate Catalog for more information about this topic.
- Students must follow the professional standards established by their college, degree program and/or department. A student who violates the established professional standards may be placed on probation or dismissed from their program.
- A student whose cumulative GPA falls below 3.0 will automatically be placed on academic probation.
- A student will be dismissed from their program if their GPA is not raised to 3.0 by the end of their subsequent semester of enrollment.
- A student will be dismissed from their program if they earn a letter grade below C- in more than one required course.
- A student who earns a letter grade of D or F in any required course must repeat the course and earn a minimum letter grade of C-.
- Any grade earned in a repeated course at the graduate level is included in the calculation of a student’s overall and major GPA, along with the original grade earned in the course. Additionally, the original grade earned in the course will remain on the student’s academic transcript/permanent record. Visit the Grades section of the University Graduate Catalog for more information about this topic.
Any exceptions to the above standards must be approved in writing by the Associate Dean for Graduate Programs and the Program Coordinator.
Faculty
Coordinator
- Stephane Collignon - Ph.D. (Virginia Tech)
Management Information Systems and Supply Chain
Associate Professors
- Stephane Collignon - Ph.D. (Virginia Tech)
Management Information Systems and Supply Chain - Bin Liu - Ph.D. (Rutgers)
Management Information Systems and Supply Chain - Brad Price - Ph.D. (University of Minnesota)
Management Information Systems and Supply Chain
Assistant Professors
- Jeongsub Choi - Ph.D. (Rutgers)
Management Information Systems and Supply Chain
Expert Instructors
- Hannah Bailey - M.S. (WVU)
Teaching Assistant Professor and Assistant Director, Data Driven WV - Joshua Meadows - M.S. (WVU)
Service Assistant Professor and Executive Director, Data Driven WV
Admissions for 2027-2028
The Admissions Committee is made up of faculty teaching in the M.S. in Applied AI and Data Analytics Program. The committee members are looking for individuals who have an interest and demonstrated aptitude in quantitative and analytical domains and reviews applications holistically admitting students based on strength of their admissions application and potential to succeed in this program.
To apply, applicants must submit:
1) a completed application - https://graduateadmissions.wvu.edu/how-to-apply/first-time-graduate-applicant
2) application fee
3) up-to-date academic transcripts from all prior undergraduate and graduate institutions - a minimum of 2.75 is required
- Students can have an undergraduate degree in any field, but the Admissions Committee looks for undergraduate records in quantitative, analytical, and/or programming coursework. Successful students come from many academic backgrounds.
4) an up-to-date resume showing prior work experience
- Work or additional experience in the following areas – business intelligence, business analytics, data mining, data warehousing, database management, computer science, programming, web development, web analytics, risk management and related fields – are considered favorably.
International Applicants are required to submit a TOEFL, IELTS, or Duolingo score. Visit English Language Proficiency Requirements | Graduate Admissions | West Virginia University for specific information.
The Admissions Committee reviews applications on a rolling basis, and students admitted to the program may begin in the fall or spring semester. Please visit this program’s webpage to learn more about the specific application deadlines and other important information. Students may also contact the John Chambers College of Business and Economics Graduate Programs Office for assistance at (304) 293-5505.
WVU cannot accept scans, uploads, faxes, or unverified photocopies of transcripts as official.
Graduate degree-seeking applicants must send their official transcript from the undergraduate institution that granted the bachelor’s degree. It is preferred that official transcripts be sent via an online, secure service such as National Student Clearinghouse, or Parchment to graduateadmissions@mail.wvu.edu. Alternatively, sealed, untampered, physical official transcripts can be sent to:
WVU Graduate Admissions
1 Waterfront Place
P.O. Box 6009
Morgantown, WV 26506
International applicants and applicants using express mail, use the address below:
WVU International Admissions
1 Waterfront Place
P.O. Box 6009
Morgantown, WV 26506
Major Code: 2159
All graduate programs in the John Chambers College of Business and Economics require that enrolled students maintain a minimum cumulative GPA of 3.0 in coursework applied toward their degree program, as outlined in the specific academic program of study. Students must also have a minimum cumulative GPA of 3.0 to earn a graduate degree from their respective program.
Applied AI and Data Analytics
The 30-hour online program is comprised of ten courses that collectively expose students to data uses to facilitate business operations and decision making. The overview course (BUDA 510) helps students understand the role of data analytics in the context of business. The BUDA 515 and 520 courses cover the collection of data as well as the design and management of databases. A large set of courses (BUDA 525, BUDA 530, BUDA 535, BUDA 540, BUDA 545 and BUDA 550) covers analytical tools that can be applied to data sets, including statistical, data mining, visualization, and simulation modeling tools. Formal coursework concludes with a capstone course (BUDA 555) that requires students to take the knowledge and skills built in the previous nine courses and apply them to a real-world business problem. Throughout all ten courses, there will be an overarching emphasis on the application of data analytics to a business context. The MS in BUDA program also has two virtual residency requirements. The first residency will occur at the front-end of the program. Students will meet and interact with faculty and staff associated with the MS in BUDA program, as well as their fellow students. This will also provide an opportunity to cover the logistics of the program and build networking capacity. The second residency will occur at the end of the program. This residency will include presentations by student teams of their capstone project.
| Code | Title | Hours |
|---|---|---|
| A program GPA of 3.0 is required by the Chambers College. | ||
| BUDA 510 | Overview of Business Data Analytics and Applied AI | 3 |
| BUDA 515 | Big Data Technologies for Business | 3 |
| BUDA 520 | Data Management | 3 |
| BUDA 525 | Business Statistical Methods 1 | 3 |
| BUDA 535 | Artificial Intelligence and Machine Learning for Business | 3 |
| or BUDA 451 & BUDA 460 | Advanced Business Data Mining and Artificial Intelligence and Machine Learning for Business | |
| BUDA 536 | Requirements Analysis and Design of Machine Learning and AI Based Systems | 3 |
| BUDA 540 | Decision Sciences and Analytics | 3 |
| BUDA 545 | Business Simulation Modeling | 3 |
| or BUDA 452 | Business Simulation Modeling | |
| BUDA 550 | Business Data Visualization | 3 |
| BUDA 555 | Applied AI and Data Analytics in Practice | 3 |
| or BUDA 591 | Advanced Topics | |
| Total Hours | 30 | |
- *
Students whose cumulative GPA falls below 2.75 will be placed on academic probation. If the GPA is not brought up to 2.75 by the end of the following semester, the student will be dismissed from the MS in Applied AI and Data Analytics program.
Suggested Plan of Study (1-year option)
| Fall | Hours | Spring | Hours | Summer | Hours |
|---|---|---|---|---|---|
| BUDA 510 | 3 | BUDA 535 | 3 | BUDA 550 | 3 |
| BUDA 525 | 3 | BUDA 540 | 3 | BUDA 555 or 591 | 3 |
| BUDA 515 | 3 | BUDA 536 | 3 | ||
| BUDA 520 | 3 | BUDA 545 | 3 | ||
| 12 | 12 | 6 | |||
| Total credit hours: 30 | |||||
Suggested Plan of Study (2-year option)
| First Year | |||||
|---|---|---|---|---|---|
| Fall | Hours | Spring | Hours | Summer | Hours |
| BUDA 515 | 3 | BUDA 535 | 3 | BUDA 550 | 3 |
| BUDA 525 | 3 | BUDA 536 | 3 | ||
| 6 | 6 | 3 | |||
| Second Year | |||||
| Fall | Hours | Spring | Hours | Summer | Hours |
| BUDA 510 | 3 | BUDA 540 | 3 | BUDA 555 or 591 | 3 |
| BUDA 520 | 3 | BUDA 545 | 3 | ||
| 6 | 6 | 3 | |||
| Total credit hours: 30 | |||||
Suggested Plan of Study (18-Month Option)
| First Year | |||||
|---|---|---|---|---|---|
| Spring | Hours | Summer | Hours | ||
| BUDA 540 | 3 | BUDA 550 | 3 | ||
| BUDA 545 | 3 | ||||
| 6 | 3 | ||||
| Second Year | |||||
| Fall | Hours | Spring | Hours | Summer | Hours |
| BUDA 510 | 3 | BUDA 535 | 3 | BUDA 555 or 591 | 3 |
| BUDA 525 | 3 | BUDA 536 | 3 | ||
| BUDA 515 | 3 | ||||
| BUDA 520 | 3 | ||||
| 12 | 6 | 3 | |||
| Total credit hours: 30 | |||||
Degree Progress
The department of Management Information Systems and Supply Chain requires satisfactory completion of all curriculum requirements as defined in the graduate catalog. Students who are either not making adequate degree progress or who are failing to uphold professional standards may receive notice of probation or dismissal. Students may be notified of academic consequences outside of routine evaluation processes if an issue must be addressed immediately.
The graduate handbook will be distributed via email no later than the first day of classes of the Fall semester.
Satisfactory Progress
- To be in good standing, a student must maintain a cumulative and program GPA of 3.0.
- A student who falls below a 3.0 GPA will be placed on probation for a semester.
- Probation is lifted when students are back in good standing or have made enough satisfactory progress toward graduation.
- Students who cannot mathematically achieve the required GPA or who can no longer make progress toward graduation may be dismissed.
Major Learning Outcomes
Applied AI and Data Analytics
The educational goals and objectives of the M.S. in Applied AI and Data Analytics are as follows:
- Students will be able to demonstrate expertise in statistical techniques, data mining, utilizing databases, and analytical tools.
- Students will be able to apply data analytics to enhance the decision-making of the firm in performance metrics and measurement, risk indicators, assessment and response, and compliance.
- Students will be able to use business analytics to synthesize data trends and competitive drivers.
- Students will be able to communicate the analysis and findings of an analytics initiative in moving an organization forward.