Data Analytics Minor

Professors: Cheryl Beaver, Scott Beaver, Hamid Behmard, Laurie Burton, Breeann Flesch, Jie Liu, Becka Morgan, Scot Morse

Associate professors: Matthew Ciancetta, Lucas Cordova, Matthew Nabity, Thaddeus Shannon

Assistant professors: Ben Coté, Leanne Merrill

Mission

The Data Analytics program prepares students to thrive in the constantly evolving field of working with big data. The field is interdisciplinary by nature and is built on a foundation of Mathematics, Computer Science, Information Systems and Data Analytics courses. This program emphasizes the applied practice of data analytics techniques for solving problems in the real world.

Learning Outcomes

Use relevant technology for the analysis of large, complex, or messy data sets to design, implement, and evaluate analytics-based solutions.
Use quantitative information in connection with the argument or purpose of the work, present it in an effective format, and explicate it with consistently high quality.
Collect, analyze and assess evidence to reach informed conclusions and judgments.

Core Courses

CS 161 Computer Science I Credits: 4
or CS 133 Introduction to Python Programming Credits: 4
DATA 101 Foundations of Data Analytics Credits: 4
DATA 432 Introduction to Data Analytics Credits: 4
DATA 434 Data Visualization Credits: 4
MTH 243 Introduction to Probability and Statistics Credits: 4
MTH 244 Introduction to Probability and Statistics II Credits: 4
or BA 367 Regression Analysis Credits: 3

Elective Credits: 4

One upper-division CS, MTH, DATA or IS elective planned with the help of a minor advisor

Total Credits: 27-28

Satisfactory Grade Use

Program requirements may be fulfilled with a grade of S.