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Undergraduate Catalog

B.S. in Data Science

 

Summary of Requirements


2024-2025
Core Curriculum 43
Pre-Major Courses 6
Required Related Courses 6
Required Mathematics Courses
  18
Required Data Science  Courses
15
Required Information Technology Courses
9
Free Elective Major Courses 12
Free Elective Courses
11
TOTAL: 120

 

Program entrance requirements

Students must complete or demonstrate the following before declaring a major in Data Science:

  • A grade of B or higher in DAS 101
  • A grade of B or higher in ITS 110, or a grade of B or higher in MAT 130
  • A cumulative grade point average of 2.5 or higher.

Required related courses 6 credits

CHE 240Computer Applications for Scientists

3-4
STM 403Senior Capstone

3

Required data science courses 15 credits

DAS 170Simulation and Probability

3
DAS 211Machine Learning for Data Scientists

3
DAS 221Data Visualization

3
DAS 231Genomics and Bioinformatics

3
DAS 532UFundamentals of Geographic Information Systems

3

Required information technology courses 9 credits

ITS 211Programming Language I

3
ITS 321Database Fundamentals

3
ITS 410Data Structures and Algorithms

3

Free elective major courses 12 credits

Choose from the following:
DAS 495Special Topics

1-5
ITS 212Programming Language II

3
ITS 495Special Topics

1-5
MAT 205Calculus II

3
MAT 348Introduction to Cryptography

3
MAT 360Introduction to Operations Research

3
MAT 361Numerical Analysis

3
MAT 414Applied Statistics II

3
MAT 495Special Topics

1-5

Required pre-major courses 6 credits

DAS 101Introduction to Data Analysis

3
ITS 110Programming Fundamentals

3
MAT 130Precalculus

3
*MAT 130 - 3 hours count towards Core Curriculum

Required mathematics courses 18 credits

MAT 140Discrete Structures

3
MAT 150Calculus I

3
MAT 206Multivariable Calculus

3
MAT 307Linear Algebra

3
MAT 313Introduction to Probability

3
MAT 314Applied Statistics I

3

 

Data Science Major Internship Requirement

One summer or semester internship related to Data Science is required. Students can start the internship program in their sophomore year. Our in-house internship coordinator and faculty advisor will work closely with each student on internship preparation, placement, and follow-up.

Program Outcomes

Demonstrate competence in discussing and presenting their data analysis results and insights to diverse audiences using both written English and American Sign Language.

Demonstrate competence in analyzing and interpreting complex datasets using suitable statistical techniques, pattern recognition methods, machine learning algorithms, and visualization tools.

Demonstrate competence in using programming languages that are commonly used in data science, such as Python or R, to effectively apply data transformation techniques and implement data science related algorithms.

Demonstrate competence in collaborating effectively within teams while working on data-related projects.

Demonstrate an understanding of the field of data science by exploring its applications and career opportunities.

Demonstrate an understanding of the importance of ethical considerations and decision-making in data science by responsibly handling data, and by making evidence-based decisions to address questions related to personal wellness choices, civic discourse within communities, and public policies.

Subject:

Data Science