Undergraduate Degree in Data Science

The university offers an interdepartmental major in data science. The major combines computer science, statistics, and a student’s choice of advanced course work in any one of a number of different areas of computational science, including business, biology, earth and environmental science, political science and others.  The major consists of:

  • Prerequisite courses, Prerequisite course requirements may be satisfied by AP credit or by testing, according to the standards used by the department that is home to the particular course.  CSC 161 is satisfied by demonstration of knowledge of Python programming; CSC 160, by knowledge of MATLAB programming; and CSC 171, by knowledge of Java programming.
  • Core courses in Computer Science and Statistics. All are required.
  • Supplementary courses in Computer Science and Statistics.  See below for requirement information.
  • Three upper-level courses in one application area (e.g.: Biology, Economics, Political Science, Earth & Environmental Science, etc.) OR three more upper-level courses (200 level+) in any mix of Computer Science and Statistics.  

In order to plan and declare your major, please see Michelle Saile, undergraduate coordinator for the Institute for Data Science.


  • MTH 150 Discrete Mathematics OR MTH 150A Discrete Math Module
  • MTH 161 Calculus I and MTH 162 Calculus II
          OR MTH 141, MTH 142, and MTH 143
          OR MTH 171Q and MTH 172Q
  • CSC 161 The Art of Programming OR CSC 160 Engineering Programming
  • CSC 171 The Science of Programming
  • CSC 172 The Science of Data Structures 

Core Courses

  • MTH 165 Linear Algebra with Differential Equations OR both MTH 163 Ordinary Differential Equations I and MTH 235 Linear Algebra
  • CSC 262 Computational Introduction to Statistics
          OR STT 213 Elements of Probability and Mathematical Statistics
          OR STT 212 Applied Statistics for BIO and PHY Sciences I
  • CSC 265 Intermediate Statistical and Computational Methods OR both STT 216 Applied Statistics II and STT 226W Introduction to Linear Models
  • CSC 240 Introduction to Data Mining
  • CSC 242 Introduction to Artificial Intelligence
  • CSC 261 Database Systems
  • CSC 282 Design and Analysis of Efficient Algorithms

Supplementary Courses

BS students must take both of:

  • MTH 201 Introduction to Probability
  • MTH 203 Introduction to Mathematical Statistics

BS students must take one of:

  • CSC 246 Machine Learning
  • CSC 247 Natural Language Processing
  • CSC 248 Statistical Speech & Language Processing
  • CSC 249 Machine Vision
  • CSC 252 Computer Organization

Application Areas

Following are examples of application area course sets.  Many other sets of courses for these and other disciplines are possible. Please visit Michelle Saile for more information.

Biology: Genetics

  • Additional Prerequisites: BIO 110-111 or BIO 112-113
  • BIO 198 Genetics OR BIO 190 Genetics
  • BIO 253 Computational Biology
  • BIO 265 Molecular Evolution

Environmental Science

  • Additional Prerequisites: EES 101 and CHM 131
  • EES 211 Geohazards and Their Mitigation: Living on an Active Planet
  • EES 212 A Climate Change Perspective to Chemical Oceanography
  • EES 251 Introduction to Remote Sensing and Geographic Information Systems


  • Additional Prerequisites: ECO 108 or AP Credit
  • ECO 207 Intermediate Microeconomics
  • MKT 203 Principles of Marketing
  • ECO 231 Econometrics

Political Science

  • PSC 203 Survey Research Methods
  • PSC 204 Research Design
  • PSC 215 American Elections