Data Analytics Minor

The data science programs prepare students for the analysis of data-driven problems within their major discipline. They are intended to supplement traditional statistical analysis, open doors for new types of analysis, and expose students to current techniques in the emerging field. Additionally, several courses in the program (as well as optional internships and capstone projects) have the students apply these techniques to real-world problems. Tangible results of this experiential component - in the form of pdfs of posters and/or presentations - can be uploaded to the D2L Data Analytics Program shell which can be transferred to the student’s D2L portfolio.

The program provides two avenues for interested students: a 24-credit hour minor program and a 12-credit hour micro-credential program :

The Minor in Data Analytics

A total of 24 semester hours comprising:

CS 130 - Data Visualization
CS 230 - Introduction to Data Science (prerequisite of one course in statistics or CS 152 or instructor permission)

One course from the following:

BIO 206 - Biostatistics and Experim Design (Prerequisite: C- or better in BIO 201)
ECO 220 - Statistics (Prerequisite: MTH 131 (or concurrent enrollment in MTH 131))
MTH 135 - Concepts of Statistics
MTH 335 - Statistics (Prerequisite: C- or better in MTH 152 and in MTH 241)
PSY 201 - Experimental Design and Statistics (Prerequisite: PSY 101)
SSC 252 - Data Analysis for Social Science

Four semester hours from the following:

CS 110 - Introduction to Python
CS 111 - Introduction to Ruby
CS 112 - Introduction to R
CS 151 - Principles of Computer Science I (prerequisite: prior programming experience recommended)

Eight credit hours from the following:

BA 225 - Management Information Systems (prerequisite: ACC 201 or BA 140 or ECO 150)
BIO 405 - Bioinformatics Explorations (prerequisite of BIO 201)
CS 110 - Introduction to Python (Any two of CS 110 Introduction to Python, CS111 Introduction to Ruby, CS112 Introduction to R (if not counted above))
CS 111 - Introduction to Ruby (Any two of CS 110 Introduction to Python, CS111 Introduction to Ruby, CS112 Introduction to R (if not counted above))
CS 112 - Introduction to R (Any two of CS 110 Introduction to Python, CS111 Introduction to Ruby, CS112 Introduction to R (if not counted above))
CS 151 - Principles of Computer Science I (if not counted above, (prerequisite: prior programming experience recommended)
CS 238 - Machine Learning
CS 321 - Database Theory and Design (co-requisite CS 251)
CS 341 - Artificial Intelligence (prerequisite CS 251)
ES 251 - Introduction to GIS
PHI 260 - Technology and Data Ethics
CS 590 - Field Experience/Internship (Disciplinary internship that involves Data Analytics)
CS 615 - Advanced Topics: Machine Learning (prerequisite CS 251)

Additional Requirement:

In addition, four credit hours other than CS 130 and CS 230 must be outside of the student’s major, i.e. courses that do not count towards the student’s major requirements.