17 courses with the subject DATA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
A study of the basic principles of data science and the tools and skills that are essential in data science. Topics to be covered include: data acquisition, cleaning, processing, and drawing inferences from such data.
A study of the methods of statistical description, inference, probability, sampling, hypothesis testing and regression analysis with a focus on application to real situations.
Statistical models for data analysis and discovery in big-data settings, with primary focus on linear regression models. The challenges of building meaningful models from vast data are explored, and emphasis is placed on model building and the use of numerical and graphical diagnostics for assessing model fit. Interpretation and communication of the results of analyses is emphasized.
This course will provide students and faculty the opportunity to examine current issues or specialized topics within the discipline at a lower- level of study (appropriate for freshmen or sophomore academic experience). Topics will be determined by the department. Class will meet 15 hours for each hour of credit offered. Repeatable for different topics. (Offered by department discretion.)
The study of the basic principles of organizing, managing, and presenting (visual format) data in multiple ways from any source with applications in multiple disciplines. Prerequisites: DATA 2553.
This course introduces students to the principles, concepts, and technologies of artificial intelligence (AI), focusing on their application in cybersecurity. Students will gain an understanding of AI terminology, components, tools, and various algorithms. They will explore how AI can enhance security protocols, predict threats, and identify vulnerabilities. The course also covers ethical considerations and potential risks associated with AI use throughout the lifecycle of a system or tool. By examining suitable AI tools for specific cybersecurity tasks, students will learn to apply intelligent systems and machine learning in 113
Supervised internship provides students with the opportunity to integrate classroom instruction with on-the-job training in an area associated with data analytics. Students will be required to document a minimum of 45 clock hours of internship engagement per credit hour and reflection in the course. Prerequisites: DATA 3553, IDNS 3611, junior standing. (Offered by department discretion.)
Research course in the academic discipline with individual or group topics determined between student and faculty member. Credit varies from 1-3 hours although a student can repeat for up to six hours of credit. Prerequisite: Consent of the Department Chair.
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TOTAL 42 If students take a full-time load each term, they could complete this program in four years. 82
Source: Montreat College's catalog, linked per course · table learning_unit · CourseShelf publish 61
