Data Science, B.S.
The B.S. in Data Science equips students with the skills and technology required to successfully work with the enormous amounts of data now generated by business, industry, and the sciences. The major itself is interdisciplinary, combining mathematics, statistics, computer science. and information systems into a coherent curriculum covering data exploration and analysis, data manipulation, data transmission and storage, prediction, machine learning, and visualization and presentation.
120 Overall Credits Required
LIBERAL EDUCATION PROGRAM AND WRITING REQUIREMENTS
Liberal Education Program
47 Credits Required
Students must complete a comprehensive three-tiered Liberal Education Program (LEP). View all requirements of the tiers on the Liberal Education Program.
While the choice of courses that fulfill the requirements is generally left up to students, some departments require that students select specific courses to complement their major. This major has specific Tier requirements/restrictions for the following:
Tier 1 - Quantitative Reasoning:
MAT 122 - Precalculus
Tier 3 - Connections
DSC 490 - Data Science Capstone Project
Writing Requirements (“W-Courses”)
Three W-courses are required. These may not be taken until after a student has passed ENG 112 — Writing Arguments. W-courses may count toward LEP, major, or cognate requirements, as well as free electives. Course sections that meet this requirement are designated by section numbers ending in “W”.
Transfer students who enter with 60 to 89 credits are required to pass two W-courses, while transfer students who enter with 90 credits or more must pass one W-course.
- DSC 100 - Data Science I
- DSC 101 - Data Science II
- CSC 212 - CS 2: Data Structures
- CSC 229 - Object - Oriented Programming
- CSC 235 - Web and Database Development
- CSC 321 - Algorithm Design and Analysis
- CSC 330 - Software Design and Analysis
- CSC 335 - Database Systems
- CSC 463 - Distributed and Parallel Computing
- CSC 477 - Data Mining
- CSC 481 - Artificial Intelligence
- or MAT 428 - Mathematical Foundations in Machine Learning
- MAT 150 - Calculus I
- MAT 151 - Calculus II
- MAT 178 - Elementary Discrete Mathematics
- MAT 221 - Intermediate Applied Statistics
- MAT 326 - Regression Analysis
- MAT 328 - Time Series Analysis
- MAT 329 - Bayesian Analysis and Decision Making
- MAT 372 - Linear Algebra
- MAT 429 - Modern Nonparametric Statistics