Faculty: Jonas D’Andrea, Brian Avery, Bill Bynum, Dan Byrne, Russ Costa, Greg Gagne, Helen Hu, Kenan Ince, Sean Raleigh (chair), Richard Wellman, Holly Zullo
Data Science Goals
 Critical, analytical and integrative thinking
 Apply data analysis to solve real problems and make predictions in real world contexts.
 Scrape, clean, process, and evaluate the validity of data from publicly available sources.
 Explore and contrast different methods of data visualization.
 Creative and reflective capacities
 Employ novel and flexible strategies for attacking realworld issues.
 Leadership, collaboration, and teamwork
 Effectively work in teams to use data science.
 Leverage unique talents and skills in a group setting to make the whole better than the sum of its parts.
 Writing and other communication skills
 Discuss data and conclusions using effective verbal presentation and written explanation.
 Global consciousness, social responsibility, and ethical awareness
 Apply data analysis to better understand real problems around the globe.
 Consider the ethical ramifications of gathering, storing, and analyzing data.
Program Objectives
The program offers an academic minor.
The Data Science minor is designed to help students develop the ability to use data to answer research questions and make predictions and decisions. In addition to core classes that give a foundation in math, computer science, and statistics, students will select an emphasis in one of these three areas to gain more depth. The program culminates in a capstone project that requires students to apply their data knowledge to a project related to their major or another area of interest.
Data Science Minor
Requirement Description 
Credit Hours  Prerequisites 
I. Required Core Courses  16  
WCSAM 110 Explorations in Data Science (4)  
WCSAM 203 Linear Algebra (4)  
DATA 220 Introduction to Statistics (4)  MATH 105  
Choose one of the following courses:  

Corequisite: MATH 105 PHYS 211, or both PHYS 151 and MATH 201 

II. Emphasis  8  
Complete two courses from one group:  
Statistics DATA 250 Statistical Modeling (4) 
– DATA 220 

DATA 370 Statistical Learning (4)  DATA 250, and WCSAM 203 or MATH 211  
Mathematics MATH 210 Discrete Mathematics (4) 
–  
MATH 370 Machine Learning (4)  MATH 210  
Computer Science CMPT 202 Introduction to Data Structures (4) 
– CMPT 201 or BIOL/PHYS/CHEM 370 with Java competency module 

CMPT 307 Database Systems (4)  CMPT 202 and either CMPT 251 or Linux shell competency module  
III. Capstone Project  1  
DATA 470 Capstone Project (1)  Complete core courses  
TOTAL HOURS FOR DATA SCIENCE MINOR  25 
Note: For purposes of this minor, the courses listed in Section II (Emphasis) are considered electives and therefore cannot be “doubledipped” with courses in other majors or minors with the one exception of CS majors who want the Mathematics emphasis (in which case, they will count MATH 210 as a required class for CS and as an elective course for the DS minor with the Mathematics emphasis). In practice, what this means is that math majors may not elect the Mathematics emphasis and CS students may not elect the Computer Science emphasis.