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Oct 04, 2024
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2024-2025 Undergraduate Catalog
Ethical Data Science - Major
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Return to: ACADEMIC PROGRAMS A-Z
Data science is an interdisciplinary field combining elements of statistics, mathematics, computer science, information science, machine learning, and artificial intelligence. The B.S. in Ethical Data Science program will give students an understanding of the tools and methods used to analyze data in order to help organizations find useful insights and make better, well-informed decisions. Our undergraduate major is unique in that it integrates the social and ethical considerations of data science in every course.
Student Learning Outcomes
- Evaluate and construct data-centered arguments.
- Understand and implement an ethical data science life cycle process: posing a question; collecting, cleaning, and storing data; performing analysis and visualization; making inferences and predictions; communicating results; and making decisions.
- Explore how data, artificial intelligence and other analytical tools can be used to solve social problems and know when these approaches may be inappropriate.
- Assess the institutional contexts and practices of ethical data collection, classification, storage, analysis, and use.
- Define “big data” and articulate the advantages and disadvantages of using large datasets in data analysis.
- Critically evaluate power, fairness, accountability, trustworthiness, transparency, and other ethical considerations when using mathematical, computational, and/or sociotechnical approaches to data analysis.
- Be able to connect emerging issues in big data analytics to interdisciplinary theories, debates, and frameworks.
- Demonstrate ethical self-awareness, personal conviction, and responsible action in the use of data science practices and technologies.
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Technology, Artificial Intelligence, and Society
Mathematics and Statistics
Electives
Choose three courses (9 credits) from the following course options:
Ethical Data Science Sample Program
Fall Semester, 1st Year |
Spring Semester, 1st Year |
ACS 101 Academic and College Success |
1 |
ENGW 102 Argument and Research |
3 |
ENGW 101 Exposition |
3 |
MTH 212 Calculus II with Multivariable Calculus |
3 |
MTH.Q 113 Calculus I (1) |
3 |
TAS 239 Can Computers Think? An Introduction to Artificial Intelligence and Machine Learning |
3 |
TAS 211 Revolutionary Computing: Programming and Problem Solving for a Better World |
3 |
PEQ (4) |
3 |
FYS/PEQ (2) |
3 |
PEQ (5) |
3 |
PEQ (3) |
3 |
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TOTAL CREDITS |
16 |
TOTAL CREDITS |
15
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Fall Semester, 2nd Year |
Spring Semester, 2nd Year |
TAS 251 Artificial Intelligence and Data Ethics |
3 |
MTH 208 Statistics for Scientists |
3 |
PEQ (6) |
3 |
TAS 233 Technology & Society |
3 |
PEQ (7) |
3 |
Elective |
3 |
DEIB designated course |
3 |
PEQ with lab (8) |
4 |
Elective |
3 |
Integrative Studies (IS-1) |
3 |
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Health & Wellness |
0 |
TOTAL CREDITS |
15 |
TOTAL CREDITS |
16 |
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Fall Semester, 3rd Year |
Spring Semester, 3rd Year |
MTH 308 Applied Statistics and Visualization |
3 |
MTH 314 Introduction to Probability |
3 |
TAS 337 Practical Artificial Intelligence and Machine Learning |
3 |
TAS Elective |
3 |
Elective |
3 |
Elective |
3 |
Elective |
3 |
Elective |
3 |
Integrative Studies (IS-2) |
3 |
Integrative Studies (IS-3) |
3 |
TOTAL CREDITS |
15 |
TOTAL CREDITS |
15 |
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Fall Semester, 4th Year |
Spring Semester, 4th Year |
TAS Elective |
3 |
TAS 449 Technology, AI, and Society Capstone |
3 |
TAS 483 Technology, Artificial Intelligence and Society Internship |
3 |
TAS Elective |
3 |
MTH 325 Mathematical Modeling with Differential Equations |
3 |
Elective |
3 |
Elective |
3 |
Elective |
3 |
Elective |
3 |
Elective |
3 |
CME 050 Core Milestone Experience |
0 |
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TOTAL CREDITS |
15 |
TOTAL CREDITS |
15 |
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Return to: ACADEMIC PROGRAMS A-Z
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