B.S. in Data Science
Overview
The Bachelor of Science (B.S.) in Data Science is aimed at students who are interested in pursuing careers in data science or related fields. The B.S. in Data Science degree is a multidisciplinary undergraduate program that will provide students training in:
- Mathematics, computation, and statistics
- Data collection, management, description, and analysis
- Communication and project management
- Ethics and Problem solving
- Judgment and decision making
Required Courses [44-46 credit hours]
- Data I: Dealing with Data
- PSYC/POLS/ECON/SOC 199 [3 credit hours]
- Data II: Foundations of Data Science
- PSYC/POLS/ECON/SOC 399 or EECS 331 [3 credit hours]
- Data III: Data Management
- PSYC 599 [3 credit hours] NEW
- Data IV: Introduction to Machine and Statistical Learning
- PSYC 612 [3 credit hours] NEW
- Community Data Labs (Capstone)
- PSYC/POLS 699 [3 credit hours]
- Introduction to Computing
- EECS 138 [3 credit hours]
- Calculus I
- MATH 115 [3 credit hours] or MATH 125 or MATH 145 [4 credit hours]
- Calculus II
- MATH 116 [3 credit hours] or MATH 126 or MATH 146 [4 credit hours]
- Elementary Linear Algebra
- MATH 290 or MATH 291 [2 credit hours]
- Elementary Statistics
- MATH 165 or PSYC 210 or PSYC 211 [3 credit hours]
- Advanced Statistics
- PSYC 500 or SOC 380 or ECON 526 [3 credit hours]
This requirement is satisfied by four courses (a minimum of 12 credit hours) numbered 300–699 selected from any of the following prefixes: ABSC, COMS, ECON, MATH, POLS, PSYC, or SOC. The rationale for this requirement is that data science is inherently applied within the context of one or more substantive domains; effective collaboration and responsible practice therefore require foundational knowledge of those domains. For example, a student who intends to apply data science within the domain of finance needs to know the main theories of economics (e.g., from ECON), and a student who intends to apply data science within the domain of emotion (e.g., in the affective computing discipline) needs to know the main theories of emotion and personality (e.g., from PSYC). This requirement provides data science students with the opportunity to acquire such domain knowledge and to personalize/specialize their training; it also promotes the formation of interdisciplinary connections across the college and university. The domain of application requirements also provides students with a pathway for acquiring foundational knowledge in multidisciplinary areas of study such as health or environmental studies, which necessitates pursuing courses across several units (e.g., SOC, POLS, ABSC). Students who intend to pursue data science careers that are less applied or translational may satisfy this requirement by specializing in more advanced quantitative skills (e.g., from MATH).
Examples of a possible course from each prefix include: ABSC 509 (Contemporary Behavioral Science), COMS 441 (Health Communication), ECON 550 (Environmental Economics), MATH 530 (Mathematical Models), POLS 624 (Environmental Politics and Policy), PSYC 370 (Behavioral Neuroscience), and SOC 424 (Sociology of Health and Medicine).