Major Requirements
Degree Type: B.S.
Minimum Credits Required: 120
 University Degree Requirements
 For complete details on these and other university degree requirements, refer to the Degree and Graduation Requirements section in the University Catalog.
  	- Minimum of 120 semester credits (some programs may exceed this minimum).
- Complete the University General Education requirements.
- Minimum institutional GPA of 2.00 based on work taken at NDSU.
- Minimum of 30 credits in resident at NDSU.
- Minimum of 36 upper level credits (courses numbered 300 or higher).
- Students with transfer credit must meet the NDSU 30 credits in residence (#4). Of these 30 credits in residence, a minimum of 15 credits must be in courses numbered 300 or above, and 15 credits must be in the student's declared major curricula.
University General Education Requirements
 A list of university approved general education courses along with the administrative policies governing the requirement and the categories is available here.
  Course List        | Code | Title | Credits | 
   |  | 12 | 
 |  | 3 | 
 |  | 10 | 
 |  | 6 | 
 |  | 6 | 
 |  | 2 | 
 |  |  | 
 |  |  | 
 |  |  | 
 | Total Credits | 39 | 
 
 
 Course List        | Code | Title | Credits | 
    |  | 3 | 
 |  | Computer Applications |  | 
 |  | Business Software Applications |  | 
 |  | Computer Science Problem Solving |  | 
 |  | Introduction to UNIX |  | 
 | ENGL 321 | Writing in the Technical Professions | 3 | 
 | or ENGL 324 | Writing in the Sciences | 
 | BUSN 380 | Business Analytics: Business Problem Solving with Spreadsheets | 3 | 
 | MATH 165 | Calculus I | 4 | 
 | MATH 166 | Calculus II | 4 | 
 | STAT 367 | Probability | 3 | 
 | STAT 368 | Statistics | 3 | 
 | STAT 412 | Statistics for Data Science using R | 3 | 
 | STAT 460 | Applied Survey Sampling | 3 | 
 | MIS 340 | Applied Business Intelligence | 3 | 
 | MIS 479 | Business Data Mining and Predictive Analytics | 3 | 
 | CSCI 312 | Survey of Programming Languages | 3 | 
 | CSCI 222 | Discrete Mathematics | 3 | 
 | CSCI 227 | Computing Fundamentals in Python I | 3 | 
 | CSCI 228 | Computing Fundamentals in Python II | 3 | 
 | CSCI 161 | Computer Science II | 4 | 
 | CSCI 366 | Database Systems | 3 | 
 |  | 3 | 
 |  | Business Ethics |  | 
 |  | Social Implications of Computers |  | 
 |  | Ethics, Engineering, and Technology |  | 
  |  | 12 | 
 | Total Credits | 69 | 
 
  Track One: Artificial Intelligence
  Course List        | Code | Title | Credits | 
   |  | 12 | 
 |  | Software Development with Frameworks |  | 
 |  | Introduction to Data Science in Python |  | 
 |  | Fundamentals of Data Engineering |  | 
 |  | Machine Learning |  | 
 |  | Introduction to Artificial Intelligence |  | 
 |  | Artificial Intelligence, Ethics, and the Environment |  | 
 |  | Cloud Computing |  | 
 |  | Introduction to Data Mining (Introduction to Data Mining) |  | 
 | Total Credits | 12 | 
 
  Track Two: Statistical Data Analytics
  Course List        | Code | Title | Credits | 
   | STAT 462 | Introduction to Experimental Design | 3 | 
 | STAT 463 | Nonparametric Statistics | 3 | 
 | STAT 464 | Discrete Data Analysis | 3 | 
 | STAT 470 | Statistical SAS Programming | 3 | 
 | Total Credits | 12 | 
 
  Track Three: Business Analytics
  Course List        | Code | Title | Credits | 
   | MRKT 466 | Digital Marketing Analytics | 3 | 
 | SCM 330 | Supply Chain Analysis and Analytics | 3 | 
 | SCM 455 | Supply Chain Technology Enablers | 3 | 
 | MIS 350 | Enterprise Systems | 3 | 
 | Total Credits | 12 | 
 
  Track Four: Generalist
  Course List        | Code | Title | Credits | 
   |  | 12 | 
 |  | Operations Research I |  | 
 | Total Credits | 12 |