Faculty: Brian Avery, Spencer Bagley, Bill Bynum, Russ Costa, Jonas D’Andrea, Greg Gagne, Helen Hu, Kenan Ince, Kathy Lenth, Jingsai Liang, Sean Raleigh (chair), Bianca Thompson
Data Science Goals
 Critical 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.
 Creativity
 Employ novel and flexible strategies for attacking realworld issues.
 Collaboration
 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.
 Communication
 Discuss data and conclusions using effective verbal presentation and written explanation.
 Global responsibility
 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 either statistics or computer science 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  
DATA 110 Explorations in Data Science (4)*  
WCSAM 203 Linear Algebra (4)  
DATA 220 Introduction to Statistics (4)  
Choose one of the following courses:  

Corequisite: MATH 101 PHYS 211 or both PHYS 151 and MATH 201 

II. Emphasis  8  
Complete two courses from one group:  
Statistics DATA 350 Statistical Modeling (4) 
– DATA 220 

DATA 370 Statistical Learning (4)  DATA 350  
Computer Science CMPT 202 Introduction to Data Structures (4) 
– CMPT 201, or BIOL/PHYS/CHEM 370 with Java competency module 

CMPT 307 Databases (4)  CMPT 202 or Linux shell competency module  
III. Capstone Project  1  
DATA 470 Capstone Project (1)  Complete core courses  
TOTAL HOURS FOR DATA SCIENCE MINOR  25 
*Honors students may use HON 232 Data/Society/DecisionMaking as a substitute for DATA 110 Explorations in Data Science.
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. In practice, what this means is that Computer Science and Computer Information Systems majors may not elect the Computer Science emphasis of the Data Science minor and Math majors pursuing the Statistics emphasis may not count Data Science courses as upperdivision elective credits toward the Math major.