Data Science

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 Courses

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 real-world 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:
  •  CMPT 201 Introduction to Computer Science (4)
  •  BIOL/CHEM/PHYS 370 Scientific Computing (4)
Co-requisite: MATH 101
PHYS 211 or both PHYS 151 and MATH 201
 II. Emphasis 8
 Complete two courses from one group:
 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

*Honors students may use HON 232 Data/Society/Decision-Making 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 “double-dipped” 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 upper-division elective credits toward the Math major.

Print Friendly, PDF & Email
Print Friendly, PDF & Email