CSCE 210 Data Structures and Algorithms
Prof. Amr Goneid AUC
Part 0. Course Outline Prof. Amr Goneid, AUC
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Course Resources Instructor: Prof. Amr Goneid E-mail:
[email protected] Office: Rm 2152 SSE Textbook: "ADTs, Data Structures and Problem
Solving with C++" by Larry Nyhoff, 2nd Edition, Pearson Prentice Hall, 2005 Reference: "Problem Solving, Abstraction, and Design using C++" by Friedman and Koffman, Fourth Edition, Addison Wesley, 2005 Lab: To be assigned soon Web Site: www.cse.aucegypt.edu/~csci210/ Prof. Amr Goneid, AUC
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Course Goals To
introduce concepts of Data Models, Data Abstraction and ADTs in problem solving and S/W development To deepen the experience in Object Oriented Programming as an efficient software development methodology. To gain experience in the design of algorithms for problem solving and to introduce the concepts of algorithm analysis To gain experience in the design and implementation of various ADTs and their applications to practical problems Prof. Amr Goneid, AUC
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Course Contents Revision and Expansion on CSCI 110 Material R1. ADTs as Classes (Revision of some CSCI 110 material) R2. Elementary Data Structures (Revision of some CSCI 110 material) R3. Dictionaries(1): Key Tables and Lists (Revision of some CSCI 110 material)
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Course Contents Data Modeling and ADT’s 2. Simple Containers: Stacks and Queues 2. Introduction to the Analysis of Algorithms 3. Trees 4. Dictionaries(2): Binary Search Trees 5. Dictionaries(3): Hash Tables 6. Priority Queues 7. Sorting Sorting (1): Elementary Algorithms Sorting (2): (n log n) Algorithms 9. The Set Data Structure: Disjoint Sets 10. Graphs 1.
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Course Contents
R1
ADTs as Classes (Revision of some CSCE 110 material)
Class Definition: Private & Public Members Constructors & Destructor Data and Function Members Accessors & Mutators Polymorphism and Overloading Example: Rational Numbers Class Example: Simple String Class
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Course Contents
R2
Elementary Data Structures (Revision of some CSCE 110 material) Static and Dynamic Data Structures Static Arrays Pointers Run-Time Arrays The Linked List Structure Some Linked List Operations Variations on Linked Lists
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Course Contents(continued) R3 Dictionaries(1):Key Tables and Lists The Key Table
ADT Key Table The Key Table Class Definition Key Table Class implementation Example Application
The Linked List ADT Linked List The Linked List Class Definition Linked List Class implementation Example Application
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Course Contents
Part 1
Data Modeling and ADTs
Data Modeling Abstract Data types (ADTs) A Classification of Abstract Structures Another Classification Special Data Structures OOP and Classes Examples on Modeling
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Course Contents(continued)
Part 2
Simple Containers: Stacks and Queues
Introduction to the Stack data structure Designing a Stack class using dynamic arrays Linked Stacks Some Applications of Stacks Introduction to the Queue data structure Designing a Queue class using dynamic arrays Linked Queues An Application of Queues
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Course Contents(continued)
Part 3
Introduction to the Analysis of Algorithms Algorithms Analysis of Algorithms Time Complexity Bounds and the Big-O Types of Complexities Rules for Big-O Examples of Algorithm Analysis
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Course Contents(continued)
Part 4
Trees
Binary Trees Tree Traversal
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Course Contents(continued)
Part 5
Dictionaries(2): Binary Search Trees
The Dictionary Data Structure The Binary Search Tree (BST) Search, Insertion and Traversal of BST Removal of nodes from a BST Binary Search Tree ADT Template Class Specification Other Search Trees (AVL Trees)
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Course Contents(continued)
Part 6
Dictionaries(3): Hash Tables
Hash Tables as Dictionaries Hashing Process Collision Handling: Open Addressing Collision Handling: Chaining Properties of Hash Functions Template Class Hash Table Performance
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Course Contents(continued)
Part 7
Priority Queues Definition of Priority Queue The Binary Heap Insertion and Removal A Priority Queue Class
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Course Contents(continued)
Part 8a
Sorting(1): Elementary Algorithms General Selection Sort Bubble Sort Insertion Sort
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Course Contents(continued)
Part 8b
Sorting(2): (n log n) Algorithms
General Heap Sort Merge Sort Quick Sort
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Course Contents(continued)
Part 9
The Set Data Structure: Disjoint Sets
What are Disjoint Sets? Tree Representation Basic Operations Parent Array Representation Simple Find and Simple Union Disjoint Sets Class Some Applications
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Course Contents(continued)
Part 10 Graphs Basic Definitions Paths and Cycles Connectivity Other Properties Representation Examples of Graph Algorithms:
Graph Traversal Shortest Paths Minimum Cost Spanning Trees Prof. Amr Goneid, AUC
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Summary Part No.
Subject
R1
ADTs as Classes
R2
Elementary Data Structures
R3
Dictionaries(1): key Tables and Lists
Book Chapter
4 2 , 3, 6 6
1
Data Modeling and ADTs
2,3
2
Simple Containers: Stacks and Queues
7,8
3
Introduction to the Analysis of Algorithms
10
4
Trees
12
5
Dictionaries(2): Binary Search Trees
12
6
Dictionaries(3): Hash Tables
12
7
Priority Queues
13
8a
Sorting(1): Elementary Algorithms
13
8b
Sorting(2): (n log n) Algorithms
13
9
The Set Data Structure: Disjoint Sets
16
10
Graphs
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Parts R1,R2,R3 are revisions of CSCE110 material Prof. Amr Goneid, AUC
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Lab Assignments Hands-on experience will be gained through programming projects that cover the course material. Design documents are required for all the problems given. Design Document: The basic items in the design document will include: Problem Definition Requirement Specifications Solution Strategy S/W Design for the whole problem: Structured (Top-Down) Design in the form of modules (C++ functions) in which each module is associated with a given subproblem. Prof. Amr Goneid, AUC
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Lab Assignments S/W Design for Each Module:
Functional Specifications: the purpose of the module and what it is supposed to do (What to do) Data Specifications: the data resources needed by the module to achieve it functionality (with what) Precondition: the state of processing or data before the module is executed (state before) Postcondition: the state of processing or data after the module is executed (state after) Algorithm Specification: the algorithm or methodology used by the module (How to do it)
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Coursework Grading 30% Programming Assignments. 5 % Quizzes, class participation and
attendance 20% Midterm Exam (1) 20% Midterm Exam (2) 25% Final Exam
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Course Outcomes After completing the CSCE 210, students should be able to: 1. Demonstrate knowledge and understanding of Data Models, Data Abstraction and ADTs and their role in problem solving and S/W development. 2. Choose the appropriate data structure for modeling a given problem. 3. Design and implement various ADTs in a high level language (C++) using Object Oriented Concepts. Topics include Linked lists, Simple Containers (Stacks, Queues), Dictionaries (Key Tables and Lists, Binary Search Trees, Hash tables), Priority Queues and Heaps, Disjoint Sets and Graphs.
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Course Outcomes 4. 5. 6. 7.
Compare alternative implementations of data structures with respect to performance. Demonstrate experience in the design of algorithms for solving problem that use the above data structures. Demonstrate knowledge of common applications for each data structure in the topic list. Practice basic algorithm analysis using complexity bounds (Big-Oh, Big-Theta and Big-Omega). Applications include Quadratic Sorting methods and Divide & Conquer recursive sorting (n log n) examples (Merge Sort and Quick Sort).
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