Data Science Major

The Major in Data Science exposes students the theory and skills necessary to analyze and derive insights from large sets of structured and unstructured data.  The program aims to provide students with deep understanding of concepts related to statistics, machine learning, neural networks, natural language processing, data mining, data visualization and the mathematics that is foundational to these concepts.

Goal 1: Students will be prepared for a variety of professions in Data Science and/or Master’s degree programs in Data Science and be able to adapt to complex technological and analytical environments in the workplace.

Objective 1.1:  Students will be proficient programming in Python and in R.

Objective 1.2:  Students will be able to analyze data sets using a variety of software and analytical approaches.

Objective 1.3:  Students will be able to communicate the results of technical data analysis in a manner that is understandable to non-specialists. 

The traditional undergraduate programs include 40 courses distributed across three components: A General Education component divided into Signature Courses, Variable Courses, and an Integrative Learning requirement; a Major and Divisional component; and Free Electives. In addition to course requirements as specified in each area, students must complete one certified course in each of the following overlay areas:

  1. Diversity, Globalization or Non-western Area Studies,
  2. Ethics Intensive, and
  3. Writing Intensive. Overlay requirements are part of the forty-course requirement.

General Education Signature Courses

See this page about Signature courses. Six courses

General Education Variable Courses

See this page about Variable courses. Six to Nine courses

Required Math Beauty
MAT 161Calculus I4
Total Hours4

General Education Overlays

See this page about Overlays.

General Education Integrative Learning Component

See this page about Integrative Learning Component. Three courses:

Any three courses from the College of Arts and Sciences

Major Requirements:

MAT 162Calculus II4
MAT 213Calculus III4
MAT 223Intro Math of Data Science3
MAT 226Introduction to Linear Algebra4
MAT 321Probability3
MAT 322Mathematical Statistics3
MAT 325Essentials of Data Science3
or CSC 346 Introduction to Data Science
CSC 347Advanced Data Science3
CSC 115Intro to Computer Science3
CSC 120Computer Science I4
CSC 351Database Management Systems3
CSC 362Artificial Intelligence3
DSS 415Data Wrangling & Visualization3
DSS 445Statistical Programming Lang3
Regression and Time Series
Machine Learning/Data Science
Numerical Analy & Comp Tech
Operations Research
Combinatorics & Graph Theory
Image Data Science
Advanced Machine Learning
Intro to Security
Big Data and Web Intlgce
Internet Application Develpmnt
Introduction to Data Mining
Advanced Business Analytics
Machine Learning for Bus I
Machine Learning for Bus II
Economic Forecasting
Research Methods
Special Topics
Total Hours55