CSCE 210 Data Structures and Algorithms Prof. Amr Goneid AUC Part 0. Course Outline Prof. Amr Goneid, AUC

Course Resources Instructor: Prof. Amr Goneid E-mail: goneid@aucegypt.edu Office: Rm 2152 SSE Textbook:"Data Structures and Algorithm Analysis in C++" by Mark A. Weiss, Int. Ed of 4th revised Edition, 2014, Pearson, ISBN: 9780273769385 Reference: "ADTs, Data Structures and Problem Solving with C++" by Larry Nyhoff, 2nd Edition, Pearson Prentice Hall, 2005 Lab: 2174 SSE Web Site: http://www1.aucegypt.edu/faculty/cse/goneid/csci210/ Prof. Amr Goneid, AUC

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

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) R4. Vectors (Revision of some CSCE 110 material) Prof. Amr Goneid, AUC

Course Contents Data Modeling and ADT’s Simple Containers: Stacks and Queues Introduction to the Analysis of Algorithms Trees Dictionaries(2): Hash Tables Dictionaries(3): Binary Search Trees Priority Queues Sorting Sorting (1): Elementary Algorithms Sorting (2): (n log n) Algorithms The Set Data Structure: Disjoint Sets Graphs Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

Course Contents R4 Vectors (Revision of some CSCE 110 material) The Standard Template Library (STL) What is a Vector Declaring a Vector Size and Accessing Elements Adding and removing Elements Emptying a Vector Other Member Functions Iterators Vectors as Function Parameters or Types Vectors of Vectors Some Matrix Operations using Vectors of Vectors Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

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 Array Models Array Based Queue ADT Linked Queues Simulation of a Waiting Line Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

Course Contents(continued) Part 4 Trees General Binary Trees Tree Traversal Prof. Amr Goneid, AUC

Course Contents(continued) Part 5 Dictionaries(2): Hash Tables Hash Tables as Dictionaries Hashing Process Collision Handling: Open Addressing Collision Handling: Chaining Properties of Hash Functions Template Class Hash Table Performance Prof. Amr Goneid, AUC

Course Contents(continued) Part 6 Dictionaries(3): 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 Self-Balancing Binary Search Trees: AVL Trees Red-Black Trees Prof. Amr Goneid, AUC

Course Contents(continued) Part 7 Priority Queues Definition of Priority Queue The Binary Heap Insertion and Removal A Priority Queue Class Analysis of PQ Operations Heapify: A Modified Insertion Algorithm Prof. Amr Goneid, AUC

Course Contents(continued) Part 8a Sorting(1): Elementary Algorithms General Selection Sort Bubble Sort Insertion Sort Prof. Amr Goneid, AUC

Course Contents(continued) Part 8b Sorting(2): (n log n) Algorithms General Heap Sort Merge Sort Quick Sort Prof. Amr Goneid, AUC

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 Prof. Amr Goneid, AUC

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

Summary Parts R1,R2,R3, R4 are revisions of CSCE110 material Part No. Subject Book Chapter R1 ADTs as Classes 1 R2 Elementary Data Structures R3 Dictionaries(1): key Tables and Lists 1 , 3 R4 Vectors Data Modeling and ADTs 2 Simple Containers: Stacks and Queues 3 Introduction to the Analysis of Algorithms 4 Trees 5 Dictionaries(2): Hah Tables 6 Dictionaries(3): Binary Search Trees 7 Priority Queues 8a Sorting(1): Elementary Algorithms 8b Sorting(2): (n log n) Algorithms 9 The Set Data Structure: Disjoint Sets 8 10 Graphs Parts R1,R2,R3, R4 are revisions of CSCE110 material Prof. Amr Goneid, AUC

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

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) Prof. Amr Goneid, AUC

Coursework Grading 30% Programming Assignments. 5 % Quizzes, class participation and attendance 20% Midterm Exam (1) 20% Midterm Exam (2) 25% Final Exam Prof. Amr Goneid, AUC

Course Outcomes After completing the CSCE 210, students should be able to: Demonstrate knowledge and understanding of Data Models, Data Abstraction and ADTs and their role in problem solving and S/W development. Choose the appropriate data structure for modeling a given problem. 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. Prof. Amr Goneid, AUC

Course Outcomes 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). Prof. Amr Goneid, AUC