The data analytics minor prepares students across all industries to leverage the power of big data to identify and solve problems and improve decision-making. Students will be on the leading edge of this growing field after completing the program. They will learn a variety of data analytic techniques such as Excel decision making models, data analytics programming with Python, SQL database management, data visualization with tools such as Tableau, and more advanced technologies such as cloud computing, artificial intelligence, and deep learning.

Student Learning Outcomes

1. Analyze, design, implement, and maintain an information system.
2. Communicate clearly and effectively in writing and speaking.
3. Work effectively as a team member for a common purpose.
4. Identify ethical issues and provide alternatives or solutions.

Outcomes Assessment Activities

• The CIS program primarily uses a direct-assessment approach. Artifacts of student work pertinent to a particular learning outcome are collected. These artifacts are then evaluated by faculty external to the course in which the artifact was collected to determine students’ level of mastery. Each learning outcome has been separated into sub-skills, or “measurable objectives”, that are components of the overall learning objectives. Students’ level of mastery is assessed using rubrics which have been developed for this purpose. To ensure inter-rater reliability, we implement processes whereby raters meet before and after artifacts are assessed. In addition, for follow-up (loop-closing) activities on subsequent artifact evaluation, the same raters are utilized, when possible, for consistency and reliability.
• The CIS program meets annually with the CIS Industrial Advisory Committee to get feedback on the effectiveness of the CIS curriculum in meeting the needs of the IT industry along the Colorado Front Range. The CIS program also requires CIS graduates to complete a survey to determine the effectiveness of the program and curriculum in preparing them for jobs in IT.

Specific Program Requirements

(This minor is open to all majors except CIS with a Data Analytics Concentration)

¹

For BSAD 265 Inferential Statistics & Problem Solving (3 c.h.) substitution include one of the following: MATH 156 Introduction to Statistics (GT-MA1) (3 c.h.) or EN 275 Stochastic Systems (4 c.h.).

²

For BSAD 360 Advanced Business Statistics (3 c.h.) substitution include one of the following: MATH 356 Statistics for Engineers & Scientists (3 c.h.), EN 375 Stochastic Systems Engineering (3 c.h.), PSYC 209 Quantitative Research II (3 c.h.), NSG 371 Healthcare Informatics (2 c.h.), or EPER 343 Research and Statistics (3 c.h.).