Analytics - Minor

Analytics is the use of data, information technology, statistical analysis, quantitative methods, and mathematical and computer-based models to help decision-makers gain insight about their operations and make better data-based decisions. Students complete 18 credits hours in this minor.

Student Learning Outcomes

Students will be able to:

Analyze data, determine its validity; test claims; and draw valid conclusions using appropriate statistical methodology;
Retrieve, organize and manipulate data using a variety of analytical tools.
Use data visualization tools to effectively communicate results to multiple audiences.

*Note - Required and Elective courses in this minor typically require completion of one or more prerequisite courses. Click on each course to review the description and listing of prerequisite coursework needed.

Required Courses

Complete one introductory statistics course:
MTH 208 - Statistics for Scientists Credits: 3 (minimum grade of “C” required)
NOTE - MTH*208 is the preferred and recommended introductory statistics course. However, the following additional course options are also acceptable to meet this requirement:
- PSY 200 Statistics and Research Methods for Clinical Practice Credits: 4 (minimum grade of “B” required)
- PSY 202 Statistics and Research Methods II Credits: 4 (minimum grade of “B” required)
- MTH 207 Elementary Statistics Credits: 3 (minimum grade of “B” required)
  - A score of 3 in AP Statistics does not equate to the required minimum grade of “B” for MTH*207. In this case, students must take MTH*208 and receive a grade of “C” or better before taking MTH*308.
Upper level statistics requirement:
MTH 308 - Applied Statistics and Visualization Credits: 3
Upper level programming requirement:
TAS 211 - Revolutionary Computing: Programming and Problem Solving for a Better World Credits: 3
Plus, complete one data tool course from the following:
CIS 241 - Spreadsheet Applications Credits: 3
TAS 239 - Can Computers Think? An Introduction to Artificial Intelligence and Machine Learning Credits: 3

Electives

Students must complete two courses (six credit hours) from the following (courses completed in fulfillment of a Required Course cannot also be used to fulfill an Elective Course):

CIS 241 - Spreadsheet Applications Credits: 3
DMS 310 - Digital Marketing Analytics Credits: 3
MKT 327 - Applied Research Methods Credits: 3
MTH 325 - Mathematical Modeling with Differential Equations Credits: 3
TAS 239 - Can Computers Think? An Introduction to Artificial Intelligence and Machine Learning Credits: 3
TAS 251 - Artificial Intelligence and Data Ethics Credits: 3
TAS 293 - SQL & Data Representations, Relationships, and Structures for an Equitable World Credits: 3
TAS 337 - Practical Artificial Intelligence and Machine Learning Credits: 3
TAS 449 - Technology, AI, and Society Capstone Credits: 3
Internship with a focus on data analytics; internship in major may fulfill this elective. Must be approved by program director.

Prerequisites - students must complete all applicable prerequisite coursework prior to enrolling in any of the above courses.