Skip to content

Course Syllabus

Course: MATH 3310

Division: Natural Science and Math
Department: Mathematics
Title: Discrete Mathematics

Semester Approved: Spring 2017
Five-Year Review Semester: Spring 2022
End Semester: Spring 2023

Catalog Description: This course in discrete mathematics covers Boolean algebra, sets and relations, functions, induction, recursion, enumerative combinatorics, elements of number theory, complexity of algorithms, trees, and graph theory.  

Semesters Offered: Fall
Credit/Time Requirement: Credit: 3; Lecture: 3; Lab: 0

Prerequisites: Math 1210

Justification: Discrete mathematics is a required topic in a variety of major programs. Included are computer science, engineering, and certain emphases in mathematics.  In particular, this course is a requirement for the Bachelors of Science Degree in Software Engineering.  It transfers as Math 3310 at USU and Math 2200 at the University of Utah.


Student Learning Outcomes:
Upon successful completion of this course, students will: 

Demonstrate their ability to apply discrete mathematical concepts to real-world-type applications.
Topics for these applications may include data networks and their design, data encryption, logic circuits, and algorithmic design and efficiency.
Students will be assessed using homework, quizzes, tests, or projects. 

Demonstrate that they can use the ideas of logic to write proofs that solve problems in discrete mathematics.
Writing proofs is an important way to logically organize one's thoughts while solving a problem. This logical organization represents an important skill for software engineers and anyone else who will need to both write code and understand complex relationships clearly and precisely.
Students will be assessed using homework, quizzes, tests, or projects. 


Content:
Through lecture, instruction, and various other methods, this course may include any of the following topics:
• logic and proof
• Boolean algebra
• sets and relations on sets, including cardinalities and partial order
• functions
• induction and recursion
• counting principles, permutations, and combinations
• elements of number theory
• algorithmic complexity
• graph theory
• trees.

Key Performance Indicators:
Student learning may be evaluated using:

Midterm Exams 20%-70% 

Quizzes 0%-20% 

Comprehensive Final Exam 15%-35% 

Homework 5%-25% 

Projects 0-20% 

 

 

 

 

 



Representative Text and/or Supplies:
Kolman, Busby, and Ross, Discrete Mathematical Structures, current edition, Prentice Hall.

Kenneth H. Rosen, Discrete Mathematics and its Applications, current edition, McGraw Hill.


Pedagogy Statement:


Maximum Class Size: 36
Optimum Class Size: 20