Degree Offered
- Bachelor of Science
Nature of the Program
Students in the Applied Artificial Intelligence and Data Analytics program gain the skills necessary to develop, deploy, maintain, and manage current and next-generation AI technology and systems that businesses and organizations need to remain competitive. From developing machine learning algorithms to deploying the most cutting edge AI systems, our students work with the technology that industry professionals encounter in everyday environments while gaining real-world experience before graduation. Professionals who complete this program acquire hands-on skills through collaboration with organizations and develop foundational abilities critical for understanding both the business and technical needs of these entities. Additionally, a required minor can be pursued anywhere in the university based on the student’s interests.
Career opportunities are broad and include:
- Consulting
- Data Analyst
- Data Scientist
- AI Engineer
- and other emerging roles in this cutting-edge field
This program is excellent for students who enjoy technology, problem-solving, and engaging in hands-on projects across multiple sectors and fields.
Faculty
Chair
- Brad Price - Ph.D.
Statistics and Machine Learning
Professor
- Gary Templeton - Ph.D. (Auburn University)
Management of Information Technology and Innovation
Associate professors
- Stephane Collignon - Ph.D. (Virginia Tech)
Business Information and Technology - Bin Liu - Ph.D. (Rutgers)
Artificial Intelligence/Machine Learning - Salman Nazir - Ph.D. (McGill)
Management Information Systems - A. Graham Peace - Ph.D. (University of Pittsburgh)
Management Information Systems - Brad Price - Ph.D.
Statistics and Machine Learning - Nanda Surendra - Ph.D. (University of Cincinnati)
Management Information Systems
Assistant professors
- Mohammad (MJ) Ahmad - Ph.D. (West Virginia University)
Software Security and Machine Learning - Jeongsub Choi - Ph.D.
Artificial Intelligence/ Machine Learning
Service Assistant Professor
- Joshua Meadows - M.S. (West Virginia University)
Business Data Analytics
Admissions for 2027-2028
For specific information regarding the admissions requirements for First Time Freshmen to the John Chambers College of Business and Economics, please visit Chambers admissions.
Students who are direct admitted to the major as first-time freshmen must possess an overall university GPA of at least 2.5 and have completed the course prerequisites listed in the table below with minimum grade of C-, unless otherwise noted, to be eligible to enroll in upper-division course work.
Students who are not direct admitted to the major (i.e. Business) will declare the major during the semester in which they satisfy the course prerequisites listed below. Applicants also must possess an overall GPA of at least 2.5 to be considered for admission to the major.
| Code | Title | Hours |
|---|---|---|
| ECON 201 & ECON 202 | Principles of Microeconomics and Principles of Macroeconomics | 6 |
| ECON 225 | Elementary Business and Economics Statistics | 3 |
| or STAT 211 | Elementary Statistical Inference | |
| Choose one of the following: | 3-6 | |
| Introduction to Composition and Rhetoric and Composition, Rhetoric, and Research | ||
| Accelerated Academic Writing | ||
| Choose one of the following: a minimum of C- is needed in MATH 150 or D- in MATH 155 | 3-4 | |
| Algebra with Applications and Applied Calculus | ||
| Applied Calculus | ||
| Calculus 1 | ||
| Total Hours | 15-19 | |
General Education Foundations
Please use this link to view a list of courses that meet each GEF requirement.
NOTE: Some major requirements will fulfill specific GEF requirements. Please see the curriculum requirements listed below for details on which GEFs you will need to select.
| Code | Title | Hours |
|---|---|---|
| General Education Foundations | ||
| F1 - Composition & Rhetoric | 3-6 | |
| Introduction to Composition and Rhetoric and Composition, Rhetoric, and Research | ||
or ENGL 103 | Accelerated Academic Writing | |
| F2A/F2B - Science & Technology | 4-6 | |
| F3 - Math & Quantitative Reasoning | 3-4 | |
| F4 - Society & Connections | 3 | |
| F5 - Human Inquiry & the Past | 3 | |
| F6 - The Arts & Creativity | 3 | |
| F7 - Global Studies & Diversity | 3 | |
| F8 - Focus (may be satisfied by completion of a minor, double major, or dual degree) | 9 | |
| Total Hours | 31-37 | |
Please note that not all of the GEF courses are offered at all campuses. Students should consult with their advisor or academic department regarding the GEF course offerings available at their campus.
Degree Requirements
| Code | Title | Hours |
|---|---|---|
| University Requirements | 30 | |
| Pre-Business Requirements | 18 | |
| Business Core Requirements | 9 | |
| Information Science Requirements | 18 | |
| Major Requirements | 45 | |
| Total Hours | 120 | |
University Requirements
| Code | Title | Hours |
|---|---|---|
| General Education Foundations (GEF) 1, 2, 3, 4, 5, 6, 7, and 8 (31-37 Credits) | ||
| Outstanding GEF Requirements 2, 5, 6, and 7 | 13 | |
| BCOR 191 | First-Year Seminar | 1 |
| General Electives | 16 | |
| Total Hours | 30 | |
Pre-Business Requirements
| Code | Title | Hours |
|---|---|---|
| ENGL 101 | Introduction to Composition and Rhetoric (Minimum Grade of C-) | 3 |
| ENGL 102 | Composition, Rhetoric, and Research (Minimum Grade of C-) | 3 |
| ECON 201 | Principles of Microeconomics (Minimum Grade of C-) | 3 |
| ECON 202 | Principles of Macroeconomics (Minimum Grade of C-) | 3 |
| ECON 225 | Elementary Business and Economics Statistics (Minimum Grade of C-) | 3 |
| MATH 150 | Applied Calculus (Minimum Grade of C-) | 3 |
| Total Hours | 18 | |
Business Core Requirements
| Code | Title | Hours |
|---|---|---|
| BCOR 199 | Introduction to Business | 3 |
| BCOR 299 | Business Communication | 3 |
| BCOR 330 | Information Systems and Technology | 3 |
| Total Hours | 9 | |
Information Science Requirements
| Code | Title | Hours |
|---|---|---|
| MIST 320 | Managing Information Technology | 3 |
| MIST 351 | Database Management Systems (Minimum Grade of C-) | 3 |
| MIST 352 | Business Application Programming (Minimum Grade of C-) | 3 |
| MIST 353 | Advanced Information Technology | 3 |
| MIST 460 | Requirements Analysis and Design of Machine Learning and AI Based Systems (Minimum Grade of C-) | 3 |
| MIST 462 | Development and Deployment of Machine Learning and AI Based Systems (Minimum Grade of C-) | 3 |
| Total Hours | 18 | |
Major Requirements
| Code | Title | Hours |
|---|---|---|
| BUDA 450 | Business Data Mining and Visualization (Minimum Grade of C-) | 3 |
| BUDA 451 | Advanced Business Data Mining (Minimum Grade of C-) | 3 |
| BUDA 452 | Business Simulation Modeling (Minimum Grade of C-) | 3 |
| BUDA 453 | Advanced Simulation with AI (Minimum Grade of C-) | 3 |
| BUDA 455 | Introduction to Business Intelligence and Artificial Intelligence (Minimum Grade of C-) | 3 |
| BUDA 460 | Artificial Intelligence and Machine Learning for Business (Minimum Grade of C-) | 3 |
| BUDA 461 | Generative AI-Concepts, Models, & Applications (Minimum Grade of C-) | 3 |
| BUDA 468 | Introduction to Applied AI and Data Analytics in Practice (Minimum Grade of C-) | 3 |
| BUDA 470 | Applied Artificial Intelligence and Data Analytics in Practice | 3 |
| MANG 426 | Introduction to Decision Analysis | 3 |
| Required Minor or Double Major | 15 | |
| Total Hours | 45 | |
- *
A maximum of six credit hours of 491, Professional Field Experience, may apply towards the 120 credit hours required for the degree. Three may count toward the major and three can be counted as elective credit.
Suggested Plan of Study
| First Year | |||
|---|---|---|---|
| Fall | Hours | Spring | Hours |
| BCOR 191 | 1 | ENGL 101 (GEF 1) | 3 |
| BCOR 199 | 3 | ECON 201 | 3 |
| MATH 124 (GEF 3) | 3 | MATH 150 | 3 |
| GEF 2, 5, 6, or 7 | 3 | GEF 2, 5, 6, or 7 | 3 |
| General Elective | 3 | General Elective | 3 |
| General Elective | 2 | ||
| 15 | 15 | ||
| Second Year | |||
| Fall | Hours | Spring | Hours |
| ENGL 102 (GEF 1) | 3 | BCOR 299 | 3 |
| ECON 202 | 3 | BCOR 330 | 3 |
| ECON 225 | 3 | BUDA 455 | 3 |
| Minor | 3 | MIST 320 | 3 |
| General Elective | 3 | MIST 351 | 3 |
| 15 | 15 | ||
| Third Year | |||
| Fall | Hours | Spring | Hours |
| BUDA 450 | 3 | BUDA 451 | 3 |
| BUDA 452 | 3 | BUDA 453 | 3 |
| MANG 426 | 3 | MIST 353 | 3 |
| MIST 352 | 3 | BUDA 468 | 3 |
| Minor | 3 | GEF 2, 5, 6, or 7 | 3 |
| 15 | 15 | ||
| Fourth Year | |||
| Fall | Hours | Spring | Hours |
| BUDA 460 | 3 | BUDA 461 | 3 |
| MIST 460 | 3 | BUDA 470 | 3 |
| GEF 2, 5, 6, or 7 | 3 | MIST 462 | 3 |
| Minor | 3 | Minor | 3 |
| Minor | 3 | GEF 2, 5, 6, or 7 | 3 |
| 15 | 15 | ||
| Total credit hours: 120 | |||
Major Learning Outcomes
Applied AI and Data Analytics
- Competence in core technical areas associated with data analytics and applied artificial intelligence, such as programming, databases, and foundational mathematical, statistical, and data science models.
- Knowledge of the selection, implementation, and use of artificial intelligence and analytics tools in organizations.
- Competence in analyzing business problems and designing, building, deploying and maintaining appropriate artificial intelligence and analytics solutions to solve those problems.