CSCE 210 Data Structures and Algorithms

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CSCE 210Data Structures and Algorithms

• Prof. Amr Goneid
• AUC
• Part 0. Course Outline

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Course Resources

• Instructor Prof. Amr Goneid
• E-mail goneid_at_aucegypt.edu
• 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/

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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

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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 ADTs
• Simple Containers Stacks and Queues
• Introduction to the Analysis of Algorithms
• Trees
• Dictionaries(2) Binary Search Trees
• Dictionaries(3) Hash Tables
• Priority Queues
• Sorting
• Sorting (1) Elementary Algorithms
• Sorting (2) (n log n) Algorithms
• The Set Data Structure Disjoint Sets
• Graphs

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Course ContentsR1

• 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 ContentsR2

• 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 ContentsPart 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

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Summary

Parts R1,R2,R3 are revisions of CSCE110 material
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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.

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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
• 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.

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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).