Data Science, BS

Introduction

Data Science is a rapidly changing, interdisciplinary field and its imprint is found everywhere. The foundations of the Bachelor of Science in Data Science are data curation, mathematical foundations, computational thinking, statistical thinking, data modeling, communication and visualization, and ethics.

Baccalaureate Degree Core

Baccalaureate Core & Other Graduation Requirements

Major Requirements: 50 credits

CS 150 - Introduction to Computer Science I 4 credit(s)
CS 160 /DSCI 160 - Introduction to Python and Git 2 credit(s)
CS 250 - Introduction to Computer Science II 4 credit(s)
CS 445 - Introduction to Database Systems 4 credit(s)
DSCI 210 - Data Visualization With Python 2 credit(s)
DSCI 340 - Algorithms & Data Structures 4 credit(s)
DSCI 407 - Statistical Modeling and Regression 4 credit(s)
DSCI 440W - Machine Learning & Data Mining 4 credit(s)
DSCI 485 - Senior Seminar 2 credit(s)
DSCI 490 - Senior Capstone 2 credit(s)
MATH 206 - Computational Linear Algebra 4 credit(s)
MATH 226 - Calculus I 4 credit(s)
MATH 227 - Calculus II 4 credit(s)
MATH 307 - Calculus-Based Statistics 4 credit(s)
PHIL 308 - Artificial Intelligence 2 credit(s)

Program Details

Program Length

Four years for a complete BS degree. Two years for students transferring in with an AAOT degree. Program length may vary depending upon the student’s course load and academic progress.

Program Learning Outcomes

Students who complete this major will learn the data cycle of obtaining, curating, managing, processing, exploring, analyzing, and communicating data. They will:

Connect abstract mathematics with real-world problems involving data.
Apply computational and statistical thinking to the acquisition and analysis of data.
Build statistical models and assess them.
Become proficient in current statistical, numerical, and visualization software.
Responsibly handle data.
Communicate their analyses in both written and visual formats.

Undergraduate Admission

Undergraduate Admission Requirements