Purdue School of Engineering and Technology

Purdue School of Engineering and Technology

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.


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
  • Logic
  • 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

What You Will Learn

Set Theory

  • Understand the need, and be able, to cluster objects based on common characteristics.
  • Formulate and describe sets based on membership criteria.
  • Count the number of elements in a set.
  • Perform basic set operations such as union, intersection and complementation.
  • Construct Venn Diagrams to illustrate set operations.
  • Learn and apply the Laws of Set Theory.


  • What is the meaning of truth? Be able to appreciate the subtleties surrounding truth, falsity, and logical paradoxes.
  • Learn the algebra associated with the sentence calculus.
  • Construct Truth Tables.
  • Understand and appreciate the different strategies for proving theorems.
  • Determine the validity of arguments.

Modeling and Modeling Techniques

  • Construct logic trees to graphically portray algorithmic behavior.
  • Express complex decision processes graphically using flowcharting techniques.
  • Build decision tables to illustrate complex alternatives.
  • Express complex process descriptions in structured English (pseudocode).