7 courses with the subject CDA, 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.
Introduction to Data Analytics: Data Analytics is an interdisciplinary field including creation, storage, analysis, an communication of data. In this class, we will explore some of
Data Literacy in Human Context: This course develops students’ data literacy by exploring how humans create, analyze, understand, and share data. The course asks students to consider data in light of its full life cycle, from concept to message. Students build introductory skillsets in areas such as the rhetoric of data, data ethics, qualitative data analysis, and data visualization and accessibility.
Computer Programming for Data Analytics: The purpose of this course is to introduce the student with little or no prior programming experience to the fundamental principles and techniques of computer programming using the Python programming language and its associated data analysis libraries. Students will learn by solving practical problems using Python under the guidance of the instructor.
Analytical Models: In this course students will learn, apply, and interpret both supervised and unsupervised data mining methods and models commonly used by data analysts. Students utilize Python to implement various models with an emphasis on developing a conceptual understanding of how these models operate, the real-world systems in which they are applied, and interpretation of their results. Key models covered in the course may include, but are not limited to, Naive Bayes, K-Nearest Neighbors, Cluster Analysis, and Logistic Regression.
Databases for Data Analytics and Visualization: The purpose of this course is to introduce relational database concepts and to apply foundational knowledge of the SQL language. Students will learn to perform SQL queries in a data analytics environment including: creating and accessing a database instance, writing basic SQL statements, and filtering, sorting, and grouping results from multiple tables. In addition, students will learn the fundamentals and best practices of data visualization analysis using a modern data visualization tool.
Independent Study in Data Analytics: Students may pursue an independent investigation under the direction of a faculty member. May be repeated for credit.
Capstone/Senior Seminar: This course provides a cumulative learning experience in the Data Analytics major. In the capstone, students engage real-world data and projects provided by stakeholders from on campus and community partners to advance skills acquired during the major. Students complete the capstone course towards the end of the major to bring different learning experiences and problem-solving skills into team dynamics to solve meaningful problems. Analytical skill development will continue during the capstone by way of computer programming, statistical modelling, domain knowledge, and more.
Source: Queens University of Charlotte's catalog, linked per course · table learning_unit · CourseShelf publish 61
