Quantitative Analysis I
CIT 12000/ 3 Cr.
An introduction to both qualitative and quantitative problem solving, featuring a systems approach that relies on graphic models to describe such concepts as relations, sequences, and logic patterns. Course includes a brief introduction to set theory, logic, and descriptions of data.
- Available Online: No
- Credit by Exam: Yes
- Laptop Required: No
MATH 11100 or higher placement.
Course Outcomes (What are these?)
- Understand the need for and uses of various kinds of data types (CIT a)
- Draw decision logic charts to express the alternatives of simple and complex decision processes (CIT j)
- Create pseudocode to match the decision logic charts (CIT j)
- Determine the truth value of logic variables and propositions (CIT a)
CIT Student Outcomes (What are these?)
(a) An ability to apply knowledge of computing and mathematics appropriate to the program’s student outcomes and to the discipline.
(j) An ability to use and apply current technical concepts and practices in the core information technologies.
- Set Theory
- Modeling and Modeling Techniques
Principles of Undergraduate Learning (PULs)
1b. Identify and propose solutions for problems using quantitative tools and reasoning.
1c. Make effective use of information resources and technology.
4. Intellectual Depth, Breadth, and Adaptiveness