Data science (DS) is an interdisciplinary field focused on extracting knowledge from large data sets and applying that knowledge to solve problems. Artificial intelligence (AI) is the study of systems that perceive their environment and take actions that maximize their chance of achieving their goals. The two fields are interwoven, with DS systems using AI techniques for knowledge extraction and representation, and AI systems improving by examination of existing performance data. The proposed new major in Data Science and Artificial Intelligence gives students critical skills in both DS and AI, and teaches them about the interplay between the two fields. This knowledge is based on a foundational underpinning of computer science and mathematics, provides a range of electives to develop skills in subareas, and exposes the application of DS and AI in various domains.

Curriculum Requirements for B.S. in Data Science and Artificial Intelligence

Core Computer Science Courses
CSC 113Data Science for the World (New course: Data Science for Everyone) 14
CSC 120Computer Programming I4
CSC 220Computer Programming II4
CSC 315Introduction to Python for Scientists3
CSC 317Data Structures and Algorithm Analysis3
CSC 545Introduction to Artificial Intelligence3
CSC 546Introduction to Machine Learning with Applications3
Core Mathematics Courses
MTH 161Calculus I (Also fulfills Quantitative Proficiency Skills Requirement)4
MTH 162Calculus II4
MTH 210Introduction to Linear Algebra3
MTH 224Introduction to Probability and Statistics3
MTH 309Discrete Mathematics I3
Python Programming for Everyone (only if taken before CSC 120)
System Programming
Database Systems
Logic and Automated Reasoning
Statistical Learning with Applications
Design with Al
Neural Networks
Agent Technology
Introduction to Statistics and Research Design
Advanced statistics: Using regression for predictive modeling
Statistical Programing in R and SAS
Introduction to Infographics and Data Visualization
Advanced Infographics and Data Visualization
Introduction to Probability
Introduction to Mathematical Statistics
Statistical Analysis
Introduction to Biobehavioral Statistics Section B (not permitted with MTH 524, MTH 525, or MTH 542)
Introduction to Game Programming
Computer Science Project Planning 2
Computer Science Project Implementation
Computer Science Internship
Biomedical Data Science
Computational Neuroscience
Data science of culture and language
Spatial Data Analysis I
Geographic Information Systems I
Spatial Data Analysis II
Geographic Information Systems II
Introduction to Psychology
Introduction to Research Methods
Additional Required Course for the Major
PHI 115Social and Ethical Issues in Computing3
Written Communication Skills:
WRS 105First-Year Writing I3
WRS 106First-Year Writing II3
or WRS 107 First-Year Writing II: STEM
or ENG 106 Writing About Literature and Culture
Quantitative Skills (3 credits) (fulfilled through MTH 161)
Areas of Knowledge:
Arts & Humanities Cognate9
People & Society Cognate9
STEM Cognate (9 credits) (fulfilled through the major)
At least 3 credit hours in Natural Science3
Language Requirement3-9
Advanced Writing and Communication Requirement 2
Total Credit Hours120

EPS 402 may be accepted as an alternative to CSC 113.  However, since EPS 402 is a 3 credit courses, students who take EPS 402 will be required to take additional elective credits to sum 120 for the B.S. degree.


To fulfill the Advanced Writing and Communication Skills requirement, students must complete 4 "W" courses including one of the following; CSC 405 Computer Science Seminars, CSC 410 Computer Science Project Planning, CSC 431 Introduction to Software Engineering or WRS 233 Advanced Writing for STEM

Plan of Study

Plan of Study Grid
Freshman Year
FallCredit Hours
CSC 115 Python Programming for Everyone 3
CSC 113 Data Science for Everyone 4
MTH 161 Calculus I 4
WRS 105 First-Year Writing I 3
2nd Language 3
 Credit Hours17
CSC 120 Computer Programming I 4
MTH 162 Calculus II 4
WRS 106 First-Year Writing II 3
2nd Language 3
 Credit Hours14
Sophomore Year
CSC 220 Computer Programming II 4
MTH 309 Discrete Mathematics I 3
PHI 115 Social and Ethical Issues in Computing 3
P&S Cognate 3
2nd Language 3
 Credit Hours16
CSC 317 Data Structures and Algorithm Analysis 3
MTH 224 Introduction to Probability and Statistics 3
P&S Cognate 3
Natural Science 3
Elective 3
 Credit Hours15
Junior Year
CSC 315 Introduction to Python for Scientists 3
MTH 210 Introduction to Linear Algebra 3
P&S Cognate 3
Elective 3
Elective 3
 Credit Hours15
CSC 546 Introduction to Machine Learning with Applications 3
Application 3
A&H Cognate 3
Elective 3
Elective 3
 Credit Hours15
Senior Year
CSC 545 Introduction to Artificial Intelligence 3
Application 3
A&H Cognate 3
Elective 3
Elective 3
 Credit Hours15
Application 3
A&H Cognate 3
Elective 3
Elective 3
Elective 3
 Credit Hours15
 Total Credit Hours122


The program aims to prepare students for professional and research careers in DS and AI, by giving them an understanding of both the principles and the practice of the two areas. The core courses will provide common knowledge that is necessary for all aspects of DS and AI; the elective courses will provide advanced knowledge in chosen subareas, and the application courses will illustrate how techniques in DS and AI can be applied in a range of domains. Additionally, the mathematics and statistics courses provide a formal basis for DS and AI techniques, and the ethics courses teach how DS and AI should be used in modern society. Students with this major in DS and AI will find employment in a range of industries, or to continue into academic or industrial research.

Learning Outcomes

Students will be able to: 

  • Write efficient computer programs in several programming languages (minimally Python and Java), using appropriate data structures, to solve application problems.
  • Use data analysis languages and libraries for the analysis of large data sets.
  • Apply basic and advanced techniques of AI.
  • Relate mathematical concepts and techniques to programming, data analysis, and AI algorithms.
  • Use specialized tools and techniques from DS and AI, for data repositories, statistical analysis, data visualization, machine learning, etc.
  • Translate their DS and AI skills to solve problems in application domains beyond computer science and mathematics.
  • Use DS and AI in an ethical way.