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Data Structure And Algorithm Using Java

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Data Structure And Algorithm Using Java

Data Structure and Algorithm using Java

Duration
45 Hours

Course Description

        The content covering Data Structures and Algorithms using Java focuses on the efficient organization, storage, and manipulation of data within the Java programming language. It encompasses both the theoretical foundations of data structures and algorithms and their practical implementation using Java's features and libraries.

Course Outline For Data structure and algorithm using java

1. Introduction to data structures and algorithms

  • Understanding the importance of DSA in software development.
  • Defining data structures and algorithms.
  • Analyzing the efficiency of algorithms using Time and Space Complexity, especially focusing on Big O notation.
  • Choosing the best algorithm for a particular problem. 

2. Fundamental data structures

  • Arrays and Strings: Introduction, types, operations, and problem-solving techniques like Two Pointers and Sliding Window.
  • Linked Lists: Concepts, types (singly, doubly, circular), representation, and operations (insertion, deletion, traversal).
  • Stacks and Queues: Introduction, operations (push, pop, enqueue, dequeue), implementations using arrays and linked lists, and applications like expression evaluation.
  • Trees: Concepts (nodes, edges, root), types (binary, binary search tree, heap), traversals (inorder, preorder, postorder, level order), and heap implementation.
  • Graphs: Concepts, representations (adjacency matrix, adjacency list), and basic traversal algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS).
  • Hashing: Introduction to hashing techniques and their application in data retrieval. 

3. Core algorithms

  • Searching: Linear search and Binary search.
  • Sorting: Common algorithms like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, and Heap Sort, along with their implementations and analysis.
  • Recursion: Understanding recursion, recursive functions, base cases, recursion vs. iteration, and solving problems like Fibonacci sequence, Tower of Hanoi, and tree traversals.
  • Divide and Conquer: Introduction and application to problems like binary search and merge sort.
  • Dynamic Programming (DP): Introduction, key principles (overlapping subproblems, optimal substructure), memoization, tabulation, and solving problems like Fibonacci sequence, Knapsack problem, and Longest Common Subsequence.
  • Graph Algorithms: Exploring algorithms like Dijkstra's algorithm for shortest paths.
  • Greedy Algorithms: Learning the concept and applying it to relevant problems. 

4. Advanced topics (depending on course level)

  • Advanced Tree Structures: AVL trees, Red-Black trees, and B-trees.
  • String Matching Algorithms: Techniques like KMP algorithm (Knuth-Morris-Pratt).
  • Graph Theory Applications: Network flow, minimum spanning trees.
  • Bit Manipulation: Techniques for manipulating bits and their applications. 

5. Problem-solving and competitive programming

  • Solving various problems from competitive programming platforms and interview questions.
  • Developing strong logic-building and problem-solving skills.
  • Improving coding efficiency and preparing for technical interviews. 
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