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Data Structures 1

Data Structures 1. Instructor: Dr. Sahar Shabanah Fall 2010. Lectures. ST, 9:30 pm- 11 :00 pm Text book: M. T. Goodrich and R. Tamassia , “Data Structures and Algorithms in Java”, 4 th Edition, 2005, Wiley, ISBN: 978-0471738848

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Data Structures 1

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  1. Data Structures 1 Instructor: Dr. Sahar Shabanah Fall 2010

  2. Lectures • ST,9:30 pm-11:00 pm • Text book: • M. T. Goodrich and R. Tamassia, “Data Structures and Algorithms in Java”, 4th Edition, 2005, Wiley, ISBN: 978-0471738848 • Lecture slides will be posted on the course page before each lecture. • Read thru the lecture notes and the assigned readings before class. • Be prepared to ask questions. • Class website: http://groups.yahoo.com/group/CPCS204_F10/

  3. Grading • 20% Lab & Assignments • 20% Mid-Term Exam • 20% Final Project • 40% Final exam

  4. Course Content • Object Oriented Design • Arrays & Linked Lists • Analysis tools • Stacks & Queues • Lists • Trees • Heaps • Maps & Tables • Sorting & Searching Algorithms

  5. Data Structures • A data structure in computer science is a way of storing data to be used efficiently. • Adata structure is a representation of a finite data set [2]. • Data Structures examples are Array, List, Linked list, Doubly linked list, Stack, Queue, Hash table, Graph, Heap, Tree, Binary Search tree, Red-Black tree, etc

  6. Data Structure Basic Operations • Queries operations • get information about the data structure. • Search (data structure, key): • searches for a given key in a data structure. • Sort (data structure): • sorts elements of a data structure. • Minimum(datastructure): • finds the element with the minimum value in a data structure.

  7. Data Structure Basic Operations • Maximum (data structure): • finds the element with the maximum value in a data structure. • Successor (data structure, element): • finds the element that succeeds the given element in a data structure. • Predecessor (data structure, element): • finds the element that precedes the given element in a data structure.

  8. Data Structures Basic Operations • Modifying operations: • Change the status of a data structure. • Insert (data structure, element): • inserts an element into a data structure. • Delete (data structure, element): • deletes an element from a data structure.

  9. Algorithms • An algorithm is a sequence of computational steps that transform the input into the output . • Algorithms can be classified according to the problem-solving approach that they use or the problems that they solve.

  10. Algorithms with similar problem-solving approach • Recursive Algorithms: convert the problem into sub-problems, then solve each one using recursion. • Backtracking Algorithms: return a solution if found or recur through the problem with every possible choice until solution or failure is reached. • Brute Force Algorithms: try all possibilities until a satisfactory solution is found.

  11. Algorithms with similar problem-solving approach • Divide and Conquer Algorithms: divide the problem into smaller sub-problems of the same type, and solve these sub-problems recursively, then combine the solutions to the sub-problems into a solution to the original problem. • Dynamic Programming Algorithms: find the best solution of multiple exist solutions. Examples are Knapsack and Activity Selection Problem. Brute Force Algorithms: try all possibilities until a satisfactory solution is found.

  12. Algorithms with similar problem-solving approach • Greedy Algorithms: get the best solution at the moment, without regard for future consequences. By choosing a local optimum at each step, it will end up at a global optimum. Examples are Prim’s and Dijkstra’salgorithms. • Branch and Bound Algorithms: a tree of sub-problems is formed. • Randomized Algorithms: use a random number at least once during the computation to make a decision.

  13. Algorithms solve similar problems • Sorting Algorithms: Bubble Sort, Selection Sort, Insertion Sort, Shell Sort, Merge Sort, Heap Sort, Quick Sort, Bucket Sort, etc. • Linear-Time Sorting: Counting Sort, Radix Sort, Bucket Sort, etc. • Graph Algorithms: Breadth First Search (Bfs), Depth First Search (Dfs), Topological Sort, Strongly Connected Components, Generic Minimum Spanning Tree, Kruskal’S, Prim’S, Sin- gle Source Shortest Path, Dijkstra’S, etc.

  14. Algorithms solve similar problems • Searching Algorithms: • List Search: Linear Search, Binary Search, etc. • Tree Search: Breadth First Search, Depth First Search, etc. • Informed Search: Best-First Search, A*, etc. • String Matching: Naïve String Matching, Knuth-Morris-Pratt, Boyer-Moore, etc.

  15. Java Programming Basics • Base Types: • Objects • Enum Types • Methods • Expressions • Control flow • Arrays • Simple Input and Output

  16. Object-Oriented Design • Intro • Inheritance • Polymorphism • Exceptions • Interfaces and abstract Classes • Casting • Generics

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