Complete all courses listed below unless otherwise indicated. Also complete any corequisite labs, recitations, clinicals, or tools courses where specified and complete any additional courses needed beyond specific college and major requirements to satisfy graduation credit requirements.
Universitywide Requirements
All undergraduate students are required to complete the Universitywide Requirements.
NUpath Requirements
All undergraduate students are required to complete the NUpath Requirements.
Data Science Major Requirements
| Code | Title | Hours |
|---|---|---|
| Computer Science Overview | ||
| CS 1200 | First Year Seminar | 1 |
| or INPR 1000 | First-Year Interdisciplinary Seminar | |
| CS 1210 | Professional Development for Khoury Co-op | 1 |
| Fundamental Courses | ||
| All students can take a self-assessment to attempt to place out of CS 2000 and CS 2001. Students who place out of CS 2000 and CS 2001 will instead substitute 4-5 semester hours of CS, CY, or DS coursework at the 3000 level or higher not otherwise required in the degree. | ||
| CS 1800 | Discrete Structures | 4 |
| CS 2000 and CS 2001 | Introduction to Program Design and Implementation and Lab for CS 2000 | 5 |
| CS 2100 and CS 2101 | Program Design and Implementation 1 and Lab for CS 2100 | 5 |
| Computer Science Required Courses | ||
| CS 2700 and CS 2701 | Data Structures and Analysis and Lab for CS 2700 | 5 |
| CS 3200 | Introduction to Databases | 4 |
| CS 3100 and CS 3101 | Program Design and Implementation 2 and Lab for CS 3100 | 4 |
| or CS 3520 | Programming in C++ | |
| Data Science Electives | ||
| Complete three of the following: | 12 | |
| Principles of Artificial Intelligence | ||
| Natural Language Processing | ||
| AI Policy: Machine Learning, Markets, and Morals | ||
| Engineering LLM-Integrated Systems | ||
| Information Retrieval | ||
| AI Security and Privacy | ||
| Data Science Required Courses | ||
| DS 3000 | Mathematical Foundations of Artificial Intelligence | 4 |
| DS 3500 | Advanced Programming with Data | 4 |
| DS 4200 | Information Presentation and Visualization | 4 |
| DS 4300 | Large-Scale Information Storage and Retrieval | 4 |
| DS 4400 | Machine Learning | 4 |
| DS 4420 | Advanced Machine Learning | 4 |
| or DS 4440 | Modern Neural Networks | |
| Presentation Requirement | ||
| Complete one of the following: | 4 | |
| Public Speaking | ||
| Business and Professional Speaking | ||
| Persuasion and Rhetoric | ||
| Communication and Storytelling | ||
| Improvisation | ||
| Introduction to Acting | ||
| Dynamic Presence: Theatre Training for Effective Interpersonal Interactions | ||
| Acting for the Camera | ||
| Mathematics Foundations | ||
| MATH 1341 | Calculus 1 for Science and Engineering | 4 |
| MATH 1342 | Calculus 2 for Science and Engineering | 4 |
| MATH 2331 | Linear Algebra | 4 |
| MATH 3081 | Probability and Statistics | 4 |
| Data Science and Ethics | ||
| PHIL 1145 | Technology and Human Values | 4 |
| Khoury Approved Electives | ||
| With advisor approval, directed study, research, project study, and appropriate graduate-level courses may also be taken as upper-division electives. | ||
| Complete 4 semester hours from within the following options: | 4 | |
CS 2300 or higher, except CS 5010 | ||
CY 2000 or higher, except CY 4930 | ||
DS 2500 or higher, except DS 4900 | ||
| Embedded Design: Enabling Robotics | ||
| Fundamentals of Digital Design and Computer Organization and Lab for EECE 2322 | ||
| Digital, Analytics, Technology, and Automation Research Practicum | ||
| Data Science Related Electives in Other Units | ||
| Complete one of the following: | 4 | |
| Information Design Studio 1: Principles | ||
| Information Design History | ||
| Research Methods for Design | ||
| Visualization Technologies 1: Fundamentals | ||
| Information Design Studio 2: Dynamic Mapping and Models | ||
| Empirical Research Methods | ||
| Statistics for Economists | ||
| Applied Econometrics | ||
| Computer Vision | ||
| Data Visualization | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Biostatistics | ||
| Game Design and Analysis | ||
| Data-Driven Game Design | ||
| Introduction to Health Informatics and Health Information Systems | ||
| Data Management in Healthcare | ||
| Personal Health Interface Design and Development | ||
| Evaluating Health Technologies | ||
| Data Mining for Engineering Applications | ||
| Calculus 3 for Science and Engineering | ||
| Statistics and Stochastic Processes | ||
| Business Statistics | ||
| Data Management for Business | ||
| Marketing Research | ||
| Marketing Analytics | ||
| Information Ethics | ||
| AI Ethics | ||
| Statistics in Psychological Research | ||
| Cognition | ||
Computer Science Writing Requirement
| Code | Title | Hours |
|---|---|---|
| College Writing | ||
| ENGW 1111 | First-Year Writing | 4 |
| Advanced Writing in the Disciplines | ||
| ENGW 3302 | Advanced Writing in the Technical Professions | 4 |
| or ENGW 3315 | Interdisciplinary Advanced Writing in the Disciplines | |
Required General Electives
| Code | Title | Hours |
|---|---|---|
| Complete 28 semester hours of general electives. | 28 | |
NUpath Requirements Satisfied
- Advanced Writing in the Disciplines
- Analyzing and Using Data
- Conducting Formal and Quantitative Reasoning
- Demonstrating Thought and Action in a Capstone
- Engaging with the Natural and Designed World
- Writing-Intensive in the Major
- Writing in the First Year
Integrating Knowledge and Skills Through Experience is satisfied through co-op.
Program Requirement
132 total semester hours required
Sample Plans of Study
Four Years, Two Co-ops Summer Session B/Fall
| Year 1 | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| CS 1200 or INPR 1000 | 1 | CS 2100 and CS 2101 | 5 | CS 3200 | 4 | MATH 2331 | 4 |
| CS 1800 | 4 | MATH 1342 | 4 | MATH 3081 | 4 | General Elective 2 | 4 |
| ENGW 1111 | 4 | PHIL 1145 | 4 | ||||
| CS 2000 and CS 2001 | 5 | General Elective 1 | 4 | ||||
| MATH 1341 | 4 | ||||||
| 18 | 17 | 8 | 8 | ||||
| Year 2 | |||||||
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| CS 2700 and CS 2701 | 5 | CS 1210 | 1 | CS 3100 and CS 3101 | 4 | Co-op | 0 |
| DS 3000* | 4 | DS 4200 | 4 | General Elective 5 | 4 | ||
| DS 3500 | 4 | DS 4300 | 4 | ||||
| Presentation Requirement | 4 | General Elective 3 | 4 | ||||
| General Elective 4 | 4 | ||||||
| 17 | 17 | 8 | 0 | ||||
| Year 3 | |||||||
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| Co-op | 0 | DS 4400* | 4 | ENGW 3302 or 3315 | 4 | Co-op | 0 |
| Data Science Elective 1 | 4 | General Elective 7 | 4 | ||||
| Data Science Related Elective | 4 | ||||||
| General Elective 6 | 4 | ||||||
| 0 | 16 | 8 | 0 | ||||
| Year 4 | |||||||
| Fall | Hours | Spring | Hours | ||||
| Co-op | 0 | DS 4420 or 4440* | 4 | ||||
| Data Science Elective 2 | 4 | ||||||
| Data Science Elective 3 | 4 | ||||||
| Khoury Elective | 4 | ||||||
| 0 | 16 | ||||||
| Total Hours: 133 | |||||||
Four Years, Two Co-ops Spring/Summer Session A
| Year 1 | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| CS 1200 | 1 | CS 2100 and CS 2101 | 5 | CS 3200 | 4 | MATH 2331 | 4 |
| CS 1800 | 4 | MATH 1342 | 4 | MATH 3081 | 4 | General Elective 2 | 4 |
| ENGW 1111 | 4 | PHIL 1145 | 4 | ||||
| CS 2000 and CS 2001 | 5 | General Elective 1 | 4 | ||||
| MATH 1341 | 4 | ||||||
| 18 | 17 | 8 | 8 | ||||
| Year 2 | |||||||
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| CS 1210 | 1 | Co-op | 0 | Co-op | 0 | General Elective 3 | 4 |
| CS 2700 and CS 2701 | 5 | General Elective 4 | 4 | ||||
| DS 3000* | 4 | ||||||
| DS 3500 | 4 | ||||||
| Presentation Requirement | 4 | ||||||
| 18 | 0 | 0 | 8 | ||||
| Year 3 | |||||||
| Fall | Hours | Spring | Hours | Summer Session A | Hours | Summer Session B | Hours |
| CS 3100 and CS 3101 | 4 | Co-op | 0 | Co-op | 0 | ENGW 3302 or 3315 | 4 |
| DS 4200 | 4 | General Elective 6 | 4 | ||||
| DS 4300 | 4 | ||||||
| General Elective 5 | 4 | ||||||
| 16 | 0 | 0 | 8 | ||||
| Year 4 | |||||||
| Fall | Hours | Spring | Hours | ||||
| DS 4400* | 4 | DS 4420 or 4440* | 4 | ||||
| Data Science Elective 1 | 4 | Data Science Elective 2 | 4 | ||||
| Data Science Related Elective | 4 | Data Science Elective 3 | 4 | ||||
| General Elective 7 | 4 | Khoury Elective | 4 | ||||
| 16 | 16 | ||||||
| Total Hours: 133 | |||||||
- *
Course must be taken in the indicated term or earlier