Data Science, B.S.

Minimum number of credits required to graduate: 124

Minimum cumulative GPA required to graduate: 2.0

Minimum number of credits to complete the major: 64

School: School of Engineering and Computational Sciences

Division: Computational Sciences

Department: Computer and Data Sciences

Learn more about the program

Data scientists use mathematics, statistics, and computer coding to transform raw data into meaningful information for decision making. Our majors develop competitive skills by using current industry software such as R, Python, and Tableau to analyze data sets drawn from application areas including transportation, business, health care, demographics, and sports. Our curriculum emphasizes teamwork, communication, and practical experience from the introductory courses through the internship and practicum. Students develop skills in data visualization, machine learning, data mining, and predictive analytics.

The B.S. in Data Science provides a foundation in data science that produces graduates who are competitive within the thriving data science job market.

The B.S. in Data Science provides a foundation in data science that produces graduates who are competitive within the thriving data science job market. The degree offers students more flexibility in their degree choices in the STEM fields, increases their career options, and provides one of the few B.S. in Data Science programs offered in New England within the current high-demand market for entry-level data scientists.

Major Requirements

The major consists of thirteen core courses and three electives.

Required Courses

Complete the following required courses:

CSC 1611 - Problem Solving with Python Credits: 4
CSC 2710 - Analysis of Algorithms Credits: 4
CSC 2821 Data Structures with Python Credits: 4
CSC 3810 - Databases Credits: 4
DSE 1001 - Foundations of Data Science I Credits: 4
DSE 1002 - Foundations of Data Science II Credits: 4
DSE 2001 - Creating Information Graphics Credits: 4
DSE 3001 - Data Mining and Predictive Analytics I Credits: 4
DSE 3002 - Data Mining and Predictive Analytics II Credits: 4
DSE 4900 - Data Science Practicum Credits: 4
MTH 1314 - Discrete Mathematics Credits: 4
Choose one course from the following four courses:
MTH 1111 - Basic Statistics Credits: 4
MTH 1505 - Applied Statistics and Probability for Engineers Credits: 4
MTH 2527 - Probability and Statistics I Credits: 4
PSY 2110 - Statistical Methods in Psychology Credits: 4
Choose one course from the following two courses:
MTH 1115 - Calculus and Quantitative Methods for Business Credits: 4
MTH 1217 - Calculus I Credits: 4

Required Major Electives

Choose three courses from the following:

CEN 3071 - Introduction to GIS Credits: 4
CHM 4430 - Introduction to Chemometrics Credits: 4
CSC 3030 - Randomized Algorithms Credits: 4
CSC 3335 - Artificial Intelligence Credits: 4
CSC 3995 - Machine Learning Credits: 4
DSE 3010 - Machine Learning for Data Science Credits: 4
DSE 4620 - Advanced Sports Analytics Credits:
DSE 4800 - Directed Study in Data Science Credits: Variable
MGT 2150 - Data Analysis for Business Credits: 4
MTH 3335 - Linear Algebra Credits: 4

Experiential Learning Component

All students are required to register at least one experience that is categorized as experiential learning. Possible ways to fulfill this requirement include external or internal internships, research experiences, or semester projects. Students must seek approval and register their experience with the O’Brien Center for Career Development.