Data Science Minor

Data Science is an emerging field in high demand across disciplines and in both the private and public sectors. The Data Science Minor is a cross-disciplinary minor that allows students from across campus to engage with coursework in computational and statistical methods to support and enhance their training in data science within their program. The Data Science Minor emphasizes: 1) Statistical and Computational Thinking, 2) Algorithms and Sofware Foundations, 3) Data Curation, and 4) Model Building and Assessment.

Required Courses

Please note: Twelve of the units taken to complete a minor (6 of which are upper division) may not be used in fulfillment of other minors, concentrations, or major requirements.

Complete one of the following courses:
STAT 100 - Introduction to Statistics Units: 3
BUS 204 - Business Statistics Units: 3
STAT 250 - Applied Stat:Sci Tech Units: 4 *
MATH 320 - Applied Probability and Statistics Units: 4 *
*Indicates courses that require Math 150 as a prerequisite
Complete one of the following courses**:
CST 238 - Introduction to Data Structures Units: 4
BIO 380 - Practical Computing for Scientists Units: 4
CST 319 - Computing Across the Disciplines Units: 3
**Note that each course has prerequisites outside of the minor, but that may be completed as part of your major or general education coursework.
Complete one the following courses:
STAT 210 - Statistical Computing with R Units: 1
STAT 211 - Statistical Computing with SAS Units: 1
Complete one of the following courses:
CST 381 - Data Science Fundamentals Units: 4
CST 383 - Introduction to Data Science Units: 4
Complete one the following courses:
STAT 310 - Data Visualization Units: 3
CST 463 - Advanced Machine Learning Units: 4
Complete one of the following courses not already completed above:
Note that some courses may have prerequisites outside of the minor.
STAT 210 - Statistical Computing with R Units: 1
STAT 211 - Statistical Computing with SAS Units: 1
STAT 310 - Data Visualization Units: 3
STAT 410 - Generalized Linear Models Units: 4
STAT 420 - Statistical Theory I Units: 4
STAT 440 - Bayesian Inference Units: 4
CST 231 - Problm-Solving/Programng Units: 4
CST 363 - Introduction to Database Systems Units: 4
CST 370 - Design and Analysis of Algorithms Units: 4
CST 463 - Advanced Machine Learning Units: 4
BUS 469 - Database Management Units: 4
BUS 308 - Information Systems for Decision Making Units: 4
BUS 468 - Business Analytics Units: 4
MATH 150 - Calculus I Units: 4
MATH 151 - Calculus II Units: 4
MATH 250 - Multivariate Calculus Units: 4
MATH 265 - Differential Equations and Linear Algebra Units: 4
BIO 410 - Bioinformatics Units: 4

Learning Outcomes

Statistical and Computational Thinking

Students apply statistical knowledge and computational skills to formulate problems, plan data collection campaigns, or identify and gather relevant existing data then analyze the data to provide insights while using professional statistical analysis software packages.

Algorithms and Software Foundations

Students employ algorithmic problem-solving skills to real-world problems. Students leverage existing packages and tools to solve their computational problems.

Data Curation

Students prepare, stucture, and manage data from a variety of source and formats for use with a variety of statistical methods and models and should recognize how the quality of the data and the means of data collection may affect conclusions.

Model Building and Assessment

Students employ informal modeling (such as data visualization, data aggregation, and summarization) and formal statistical and machine learning models while recognizing the strengths and weaknesses of proposed models.