CIS 228 - Algorithms and Data Structures

Description
This course introduces the basics of algorithms and data structures, including sorting, runtime complexity, lists, stacks, queues, hash tables, trees, and graphs. Students develop in-depth understanding of data structures, their representations, and role as the foundation in the development of computer algorithms.

Credit Hours: 3
Contact Hours: 3
Prerequisites/Other Requirements: CIS 123 (C or Higher)
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: Yes
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
None

Other Courses Where This Course is a Prerequisite: None
Other Courses Where this Course is a Corequisite: None
Other Courses Where This course is included in within the Description: None

General Education Requirement:
None
General Education Learner Outcomes (GELO):
NA
Course Learning Outcomes:

Explain the connection between data structures and algorithms, and how these concepts influence program efficiency and problem-solving strategies.
Analyze algorithms using Big-O notation to evaluate efficiency, compare runtime behaviors, and classify computational complexity.
Design and implement abstract data types and core data structures—including lists, stacks, queues, hash tables, trees, and graphs—to support a variety of algorithmic operations.
Apply searching, sorting, traversal, and graph-based algorithms to solve computational problems and compare their relative performance.
Represent, model, and reason about problems using appropriate data structures and algorithmic techniques to develop effective software solutions.

Course Outline:
I. Introduction to Data Structures and Algorithms

II. Abstract Data Types

III. Algorithm Analysis and Big-O Notation

IV. Searching Algorithms

V. Sorting Algorithms

VI. Lists

VII. Stacks

VIII. Queues

IX. Hash Tables

X. Trees

XI. Introduction to Graphs

XII. Advanced Topics in Graphs

Approved for Online and Hybrid Delivery?:
Yes
Instructional Strategies:
Lecture: 30-60%

Facilitated discussion: 0-20%

Group work: 0-10%

Applied work: 10-40%
Mandatory Course Components:
None
Equivalent Courses:
None

Accepted GRCC Advanced Placement (AP) Exam Credit: None
Name of Industry Recognize Credentials: None

Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: N/A

Mandatory Department Assessment Measures:
None

GRCC Course Type:
Elective: Expands learning opportunities for degree seeking students. May or may not be required for students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 3
People Soft Course ID Number: 105057
Course CIP Code: 11.9999
Maximum Course Enrollment: 24
Course Software Utilized: Python, PyCharm

School: School of STEM
Department: Computer Information Systems
Discipline: CIS
First Term Valid: Fall 2021 (8/1/2021)
1st Catalog Year: 2021-2022
Name of Course Author:
Jonnathan Resendiz

Faculty Credential Requirements:
Master’s Degree (GRCC general requirement), Professionally qualified through work experience in field (Perkins Act or Other) (list below)
Faculty Credential Requirement Details:
The instructor must possess a Master’s Degree in Computer Science; at least five years of programming experience that includes object-oriented programing; and the ability to clearly explain the topics covered in the course.

Major Course Revisions: Prerequisite
Last Revision Date Effective: 20260220T14:29:26
Course Review & Revision Year: 2030-2031