Data structure lectures

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  • Using the increasingly popular C language, this book teaches data structures from their theoretical conception through to their concrete realizations. It emphasizes structured design and programming techniques, and contains numerous debugged programming samples. For CS2 course in advanced programming or data structures in C.

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  • Tpreface his book is designed for a two-semester sequence in computer science, beginning with what is typically known as Data Structures and continuing with advanced data structures and algorithm analysis. It is appropriate for the courses from both the two-course and three-course sequences in “B.1 Introductory Tracks,” as outlined in the final report of the Computing Curricula 2001 project (CC2001)—a joint undertaking of the ACM and the IEEE. The content of the Data Structures course has been evolving for some time.

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  • Strengthen your understanding of data structures and their algorithms for the foundation you need to successfully design, implement and maintain virtually any software system. Theoretical, yet practical, DATA STRUCUTRES AND ALGORITHMS IN C++, 4E by experienced author Adam Drosdek highlights the fundamental connection between data structures and their algorithms, giving equal weight to the practical implementation of data structures and the theoretical analysis of algorithms and their efficiency.

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  • CS2604: Data Structures and File Processing C++ Edition Cli ord A. Sha er Department of Computer Science Virginia Tech Copyright c 1995, 1996, 1998 COURSENOTES .You might believe that faster computers make it unnecessary to be concerned with e ciency. However... ⇒ • • • Data structures organize data A primary concern of this course is e ciency. The Need for Data Structures more e cient programs. More powerful computers ⇒ more complex applications. More complex applications demand more calculations. Complex computing tasks are unlike our everyday experience.

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  • The course focuses on strategies and techniques to efficiently store data (Data Structures) and to perform processing on such data in efficient ways (Algorithms), as well as on the analysis and design of such techniques. In this lecture, the following topics will be covered: Mathematical review; asymptotic and algorithm analysis; relationships and data structures; requential storage: Lists, queues, stacks, deques; hash tables; trees; priority queues and heaps; sort algorithms; graphs and graph algorithms; algorithm design techniques; complexity classes and NP completeness.

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  • This topic will describe: The concrete data structures that can be used to store information, the basic forms of memory allocation, the prototypical examples of these: arrays and linked lists, other data structures, finally we will discuss the run-time of queries and operations on arrays and linked lists.

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  • An Abstract List (or List ADT) is linearly ordered data where the programmer explicitly defines the ordering. We will now look at our first abstract data structure: Relation - explicit linear ordering, operations, implementations of an abstract list with linked lists and arrays, memory requirements, strings as a special case, the STL vector class.

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  • An Abstract Deque (Deque ADT) is an abstract data structure which emphasizes specific operations: Uses a explicit linear ordering, insertions and removals are performed individually, allows insertions at both the front and back of the deque. This topic discusses the concept of a queue: Description of an Abstract Deque, applications, implementations, the STL and iterations.

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  • In this topic, we will cover: Definition of a tree data structure and its components. Concepts of: Root, internal, and leaf nodes; parents, children, and siblings; paths, path length, height, and depth; ancestors and descendants; ordered and unordered trees; subtrees.

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  • A hierarchical ordering of a finite number of objects may be stored in a tree data structure. Operations on a hierarchically stored container include: Accessing the root, given an object in the container: Access the parent of the current object, find the degree of the current object, get a reference to a child, attach a new sub-tree to the current object, detach this tree from its parent.

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  • This topic will cover tree traversals: A means of visiting all the objects in a tree data structure, we will look at breadth-first traversals and depth-first traversals (Pre-order and Post-order depth-first traversals), applications, general guidelines.

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  • In computer science, a binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. In this talk, we will look at the binary tree data structure: Definition, properties, a few applications (Ropes, expression trees).

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  • A binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. This chapter introduce perfect binary trees: Definitions and examples, number of nodes, logarithmic height, number of leaf nodes, applications.

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  • In computer science, a balanced trees is a self-balancing tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. In this topic, we will: Introduce the idea of balance and a few examples.

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  • In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store "items" (such as numbers, names etc.) in memory. This topic covers binary search trees: Abstract Sorted Lists, background, definition and examples,...

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  • Lecture Data Structures & Algorithms: Chapter 4 (Sorting Techniques) presented bubble sort, selection sort, insertion sort, quick sort and other content. Invite you to read the lecture.

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  • Lecture Data Structures & Algorithms: Chapter 0 - Introduction presented what is Data Structures and other content. Invite you to read the lecture.

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  • Lecture Data Structures & Algorithms: Chapter 1 (C-Language) presented address, pointers, arrays, address of each element in an array, accessing & manipulating an array using pointers, another case of manipulating an array using pointers, two-dimensional array, pointer arrays, structures, structure pointers.

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  • Lecture Data Structures & Algorithms: Chapter 2 - Function & Recursion presented Function, the Concept Of Stack, the Sequence Of Execution During A Function Call, Parameter Passing & Call By Reference, Resolving Variable References, Recursion, Stack Overheads In Recursion, Writing A Recursive Function, Types Of Recursion.

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  • Lecture Data Structures & Algorithms: Chapter 3 (Searching Techniques prens) presented linear (sequential) search, binary search, complexity of algorithms.

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