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  
View KU Catalog

Students can declare a B.S. in Data Science beginning in Fall 2026.

Required Courses

  • Data I: Dealing with Data 
    • DASC199
  • Data II: Foundations of Data Science 
    • DASC399 or EECS 331
  • Data III: Data Management 
    • DASC599
  • Data IV: Introduction to Machine and Statistical Learning 
    • DASC 612
  • Community Data Labs (Capstone) 
    • DASC699
  • Introduction to Computing 
    • EECS 138  
  • Calculus I 
    • MATH 125 or MATH 145
  • Calculus II 
    • MATH 126 or MATH 146
  • Elementary Linear Algebra 
    • MATH 290 or MATH 291
  • Introductory Statistics 
    • MATH 107 or PSYC 210 or  ECON 226
  • Advanced Statistics 
    • PSYC 500 or SOC 380 or ECON 526

This requirement is satisfied by four courses (a minimum of 12 credit hours) numbered 300–699 selected from any of the following. All courses must be from a single domain:

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 possible courses from prefixes 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). 

Career Outlook

36%
Expected job growth for data scientists between 2023 and 2033
$108,020
Median salary for data scientists in 2023
20,800
Projected openings for data scientists each year