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 major in Data Science and Artificial Intelligence gives students fundamental skills in both DS and AI, and teaches them about the interplay between the two fields. This knowledge is based on an underpinning of computer science and introductory mathematics, provides a range of electives to develop skills in subareas, and exposes the application of DS and AI in various domains.

Curriculum Requirements

Core Courses - 29 credits
DSC 110Introduction to Vectors and Matrices for Data Science1
or MTH 210 Introduction to Linear Algebra
CSC 113Data Science for the World4
CSC 115Python Programming for Everyone3
or CSC 315 Introduction to Python for Scientists
CSC 120Computer Programming I4
CSC 220Computer Programming II4
DSC 344Principles and Practices of Data Science (Principles and Practice of Data Science)3
DSC 345Principles and Practice of Artificial Intelligence (Principles and Practice of Artificial Intelligence)3
MTH 161Calculus I (Core)4
PHI 115Social and Ethical Issues in Computing3
Database Systems
Statistical Learning with Applications
Introduction to Artificial Intelligence
Design with AI
Introduction to Statistics and Research Design
Introduction to Biobehavioral Statistics Section B
Advanced statistics: Using regression for predictive modeling
Statistical Programing in R and SAS
Introduction to Infographics and Data Visualization
Introduction to Game Programming
Computer Science Project Planning
Computer Science Project Implementation
Computer Science Internship
Biomedical Data Science
Spatial Data Analysis I
Geographic Information Systems I
Spatial Data Analysis II
Geographic Information Systems II
Digital Literacy Through Cultural and Literary Topics
Introduction to Psychology
General Education Requirements
Written Communication Skills:
WRS 105First-Year Writing I3
WRS 106First-Year Writing II3
or ENG 106 Writing About Literature and Culture
or WRS 107 First-Year Writing II: STEM
Quantitative Skills:
MTH 108Precalculus Mathematics II3
Areas of Knowledge:
Arts and Humanities Cognate9
People and Society Cognate9
STEM Cognate (9 credits) (fulfilled through the major)
Additional Requirements for the B.A.
Language Requirement3
Natural Sciences Course3
Minor Requirement15-18
Advanced Writing and Communication Requirement:
Four W courses, including one of the following: (may be fulfilled by W courses taken for other requirements or electives)10-12
Computer Science Seminars
Computer Science Project Planning
Introduction to Software Engineering
Advanced Writing for STEM
Total Credit Hours120

Sample Plan of Study

Plan of Study Grid
Freshman Year
FallCredit Hours
CSC 115 Python Programming for Everyone 3
MTH 108 Precalculus Mathematics II 3
WRS 105 First-Year Writing I 3
A&H cognate 3
Second language 101 3
 Credit Hours15
CSC 113 Data Science for the World 4
MTH 161 Calculus I 4
WRS 106 First-Year Writing II 3
P&S cognate 3
Second language 102 3
 Credit Hours17
Sophomore Year
CSC 120 Computer Programming I 4
DSC 110 Introduction to Vectors and Matrices for Data Science 1
Writing 3
A&H cognate 3
Second language 200 3
 Credit Hours14
CSC 220 Computer Programming II 4
Writing 3
P&S cognate 3
Natural Science 3
Minor 3
 Credit Hours16
Junior Year
DSC 344 Principles and Practices of Data Science (Principles and Practice of Data Science) 3
PHI 115 Social and Ethical Issues in Computing 3
WRS 233 Advanced Writing for STEM 3
A&H cognate 3
Minor 3
 Credit Hours15
DSC 345 Principles and Practice of Artificial Intelligence (Principles and Practice of Artificial Intelligence) 3
Program elective 3
P&S cognate 3
Minor 3
Free elective 3
 Credit Hours15
Senior Year
Program elective 3
Writing 3
Minor 3
Free elective 3
Free elective 3
 Credit Hours15
Minor 3
Minor 3
Free elective 3
Free elective 3
Free elective 3
 Credit Hours15
 Total Credit Hours122


The program prepares students for careers in the use and application of DS and AI, by giving them an understanding of both the principles and the practice of the two areas. The core courses provide knowledge that is necessary for all aspects of DS and AI, the elective courses provide knowledge in chosen subareas, and the application courses illustrate how techniques in DS and AI can be applied in a range of domains. Students with this major in DS and AI will find employment in a range of application areas, including those related to areas beyond technical development of DS and AI technology.

Student Learning Outcomes

  • Students will be able to write efficient computer programs in Python and Java, using appropriate data structures, to solve application problems.
  • Students will be able to use data analysis languages and libraries for the analysis of large data sets.
  • Students will be able to apply basic techniques of AI.
  • Students will be able to use specialized tools and techniques from DS and AI, for data repositories, statistical analysis, data visualization, machine learning, etc.
  • Students will be able to translate their DS and AI skills to solve problems in application domains beyond computer science and mathematics.