Overview
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
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses - 29 credits | ||
| DSC 110 | Introduction to Vectors and Matrices for Data Science | 1 |
| or MTH 210 | Introduction to Linear Algebra | |
| CSC 113 | Data Science for the World | 4 |
| CSC 115 | Python Programming for Everyone | 3 |
| or CSC 315 | Introduction to Python for Scientists | |
| CSC 120 | Computer Programming I | 4 |
| CSC 220 | Computer Programming II | 4 |
| DSC 344 | Principles and Practices of Data Science (Principles and Practice of Data Science) | 3 |
| DSC 345 | Principles and Practice of Artificial Intelligence (Principles and Practice of Artificial Intelligence) | 3 |
| MTH 161 | Calculus I (Core) | 4 |
| PHI 115 | Social and Ethical Issues in Computing | 3 |
| Electives | 6 | |
| Techniques | ||
| Database Systems | ||
| Statistical Learning with Applications | ||
| Introduction to Artificial Intelligence | ||
| Design with AI | ||
| Introduction to Statistics and Research Design | ||
or PSY 292 | 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 | ||
| Applications | ||
| 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 105 | First-Year Writing I | 3 |
| WRS 106 | First-Year Writing II | 3 |
| or ENG 106 | Writing About Literature and Culture | |
| or WRS 107 | First-Year Writing II: STEM | |
| Quantitative Skills: | ||
| MTH 108 | Precalculus Mathematics II | 3 |
| Areas of Knowledge: | ||
| Arts and Humanities Cognate | 9 | |
| People and Society Cognate | 9 | |
| STEM Cognate (9 credits) (fulfilled through the major) | ||
| Additional Requirements for the B.A. | ||
| Language Requirement | 3 | |
| Natural Sciences Course | 3 | |
| Minor Requirement | 15-18 | |
| 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 | ||
or CSC 410 | Computer Science Project Planning | |
or CSC 431 | Introduction to Software Engineering | |
or WRS 233 | Advanced Writing for STEM | |
| Electives | 24 | |
| Total Credit Hours | 120 | |
Sample Plan of Study
| Freshman Year | ||
|---|---|---|
| Fall | Credit 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 Hours | 15 | |
| Spring | ||
| 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 Hours | 17 | |
| Sophomore Year | ||
| Fall | ||
| 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 Hours | 14 | |
| Spring | ||
| CSC 220 | Computer Programming II | 4 |
| Writing | 3 | |
| P&S cognate | 3 | |
| Natural Science | 3 | |
| Minor | 3 | |
| Credit Hours | 16 | |
| Junior Year | ||
| Fall | ||
| 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 Hours | 15 | |
| Spring | ||
| 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 Hours | 15 | |
| Senior Year | ||
| Fall | ||
| Program elective | 3 | |
| Writing | 3 | |
| Minor | 3 | |
| Free elective | 3 | |
| Free elective | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| Minor | 3 | |
| Minor | 3 | |
| Free elective | 3 | |
| Free elective | 3 | |
| Free elective | 3 | |
| Credit Hours | 15 | |
| Total Credit Hours | 122 | |
Mission
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.
