Data Science, BS

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.

University-Wide Requirements

All undergraduate students are required to complete the University-Wide Requirements.

NUpath Requirements

All undergraduate students are required to complete the NUpath Requirements.

Computer Science Major Requirements

Computer Science Overview
CS 1200Computer Science/Information Science Overview 11
CS 1210Computer Science/Information Science Overview 2: Co-op Preparation1
Computer Science Foundations Courses
A grade of C– or higher is required in CS 2500, CS 2510, and CS 1800:
CS 1800
and CS 1801
Discrete Structures
and Recitation for CS 1800
CS 2500
and CS 2501
Fundamentals of Computer Science 1
and Lab for CS 2500
CS 2510
and CS 2511
Fundamentals of Computer Science 2
and Lab for CS 2510
CS 3500Object-Oriented Design4
CS 3520Programming in C++4
CS 4000Senior Seminar1
Information Science Foundations
IS 2000Principles of Information Science4
CS 3200Database Design4
Mathematics and Statistics Foundations
MATH 1341Calculus 1 for Science and Engineering4
MATH 1342Calculus 2 for Science and Engineering4
Complete one of the following:4
Probability and Statistics
Statistics in Psychological Research
Data Science Foundations
DS 4100Data Collection, Integration, and Analysis4
DS 4200Information Presentation and Visualization4
DS 4300Large-Scale Information Storage and Retrieval4
DS 4400Machine Learning and Data Mining 14
DS 4420Machine Learning and Data Mining 24
DS 4900Data Science Senior Project4
Data Science Related Electives
Complete six courses from the categories A and B, at least three of which must be from Category B.24
Category A: Data-Science-Related Electives in Computer and Information Science
Information System Design and Development
Information Retrieval
Information Retrieval
Human Computer Interaction
Social Information Systems
Social Computing
Empirical Research Methods
Empirical Research Methods
Systems Security
Artificial Intelligence
Foundations of Artificial Intelligence
Natural Language Processing
Natural Language Processing
Large-Scale Parallel Data Processing
Parallel Data Processing in MapReduce
Software Development
Managing Software Development
Web Development
Web Development
High Performance Computing
High Performance Computing
Algorithms and Data
Knowledge-Based Systems
Machine Learning
Data Mining Techniques
Category B: Data-Science-Related Electives in Other Units 1
Information Design 1
Information Design 2
Information Design Studio 1—Principles
Information Design History
Information Design Research Methods
Visualization Technologies
Information Design Studio 2—Dynamic Mapping and Models
Information Design Studio 3—Synthesis
Bioinformatics Computational Methods 1
Bioinformatics Computational Methods 2
Advanced Engineering Algorithms
Computer Vision
Data Visualization
Introduction to Machine Learning and Pattern Recognition
Advanced Financial Strategy
Game Design and Analysis
Game Analytics
Introduction to Health Informatics and Health Information Systems
Data Management in Healthcare
Personal Health Interface Design and Development
Personal Health Technologies: Field Deployment and System Evaluation
Foundations of Information Assurance
Data Mining in Cyberspace
Security Risk Management and Assessment
Expert Systems and Neural Networks
Data Mining for Engineering Applications
Applied Econometrics
Linear Algebra
Probability and Statistics
Statistics and Stochastic Processes
Information Resource Management
Data Management in the Enterprise
Marketing Research
Marketing Analytics
Introduction to Computational Statistics
Statistics in Psychological Research

The statistics course options under Mathematics and Statistics Foundations are also listed here as Data-Science-Related Electives. A student is permitted to take at most one additional statistics course to see statistics from the perspective of a different department.

Computer Science English Requirement

College Writing
ENGW 1111First-Year Writing4
Advanced Writing in the Disciplines
ENGW 3302Advanced Writing in the Technical Professions4
or ENGW 3315 Interdisciplinary Advanced Writing in the Disciplines

Required General Electives

Complete eight general electives.32

Major GPA Requirement

Minimum 2.000 GPA required in all CS, IS, and DS courses

NUpath Requirements Satisfied

  • Engaging with the Natural and Designed World
  • Conducting Formal and Quantitative Reasoning
  • Analyzing and Using Data
  • Writing in the First Year
  • Advanced Writing in the Disciplines
  • Writing Intensive in the Major
  • Demonstrating Thought and Action in a Capstone

Integrating Knowledge and Skills Through Experience is satisfied through co-op.

Program Requirement

133 total semester hours required

Five Years, Three Co-ops in Summer 2/Fall

Year 1
FallHoursSpringHoursSummer 1HoursSummer 2Hours
CS 25004CS 25104VacationVacation
CS 25011CS 25111  
CS 18004IS 20004  
CS 18010MATH 13424  
CS 12001Elective4  
MATH 13414   
ENGW 11114   
 18 17 0 0
Year 2
FallHoursSpringHoursSummer 1HoursSummer 2Hours
CS 32004CS 12101VacationCo-op
CS 35004CS 35204  
DS 41004DS 42004  
Statistics4DS 43004  
 16 17 0 0
Year 3
FallHoursSpringHoursSummer 1HoursSummer 2Hours
Co-opDS 44004ENGW 33024Co-op
 DS-related elective4Elective4 
 DS-related elective4  
 0 16 8 0
Year 4
FallHoursSpringHoursSummer 1HoursSummer 2Hours
Co-opDS 44204Elective4Co-op
 DS-related elective4Elective4 
 DS-related elective4  
 0 16 8 0
Year 5
Co-opCS 40001  
 DS 49004  
 DS-related elective4  
 DS-related elective4  
 0 17  
Total Hours: 133