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# ITCS 2700 - Data Structures

Credit Hours: 4.00

Prerequisites: ITCS 2550  or ITCS 2620

This course provides students with a study of classical abstract data types (ADT).  Emphasis will be placed on matching the appropriate data structures and algorithms to application problems.  Object oriented structures such as linked lists, stacks, queues and trees will be developed.   Algorithms such as hashing, searching and sorting, disjoint sets and graphing will also be implemented.   Students will evaluate complexity theory (Big O) across these algorithms.  This course assumes that students are already familiar with object oriented programming and dynamic data allocation using pointers.

Billable Contact Hours: 4

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OUTCOMES AND OBJECTIVES
Outcome 1: Upon completion of this course, students will be able to outline behaviors and properties of abstract data types.

Objectives:

1. Use Linked lists, stacks and queues to solve problems.
2. Use Hashing and Trees to solve problems.
3. Use sorting, disjoint sets and graphing algorithms to solve problems.

Outcome 2: Upon completion of this course, students will be able to  assess complexity of an algorithm based upon a specific criterion.

Objectives:

1. Discuss Big O notation including its implications
2. Differentiate complexity based upon various criteria

Outcome 3: Upon completion of this course, students will be able to design and implement fundamental data structures and algorithms.

Objectives:

1. Develop structures based upon the classical ADT models
2. Apply the classical algorithms to the listed Data structures.

Outcome 4: Upon completion of this course, students will be able to compare and contrast the operation of common data structures in terms of complexity and data structures that they implement.

Objectives:

1. Evaluate structures based upon the classical ADT models
2. Compare complexity of algorithms and data structures

Outcome 5: Upon completion of this course, students will be able to solve problems within the discrete math.

Objectives:

1. Identify the discrete math models.
2. Describe Graph Theory including trees, number and set theory.
3. Explain the use of Recursion in the algorithms.

COMMON DEGREE OUTCOMES (CDO)
• Communication: The graduate can communicate effectively for the intended purpose and audience.
• Critical Thinking: The graduate can make informed decisions after analyzing information or evidence related to the issue.
• Global Literacy: The graduate can analyze human behavior or experiences through cultural, social, political, or economic perspectives.
• Information Literacy: The graduate can responsibly use information gathered from a variety of formats in order to complete a task.
• Quantitative Reasoning: The graduate can apply quantitative methods or evidence to solve problems or make judgments.
• Scientific Literacy: The graduate can produce or interpret scientific information presented in a variety of formats.

CDO marked YES apply to this course:
Communication: YES
Critical Thinking: YES
Information Literacy: YES
Quantitative Reasoning: YES
Scientific Literacy: YES

COURSE CONTENT OUTLINE

1. Discrete Math Review
2. Algorithm Analysis
3. Lists, Stacks, Queues
4. Trees
5. Hashing
6. Priority Queues
7. Sorting
8. Disjoint Sets
9. Graph Algorithms

Primary Faculty
Schleis, George
Secondary Faculty

Associate Dean
Evans-Mach, Patrick
Dean
Balsamo, Michael

Official Course Syllabus - Macomb Community College, 14500 E 12 Mile Road, Warren, MI 48088

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