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Data Structures Training Key Features

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Practical Data Structure Implementations

Gain hands-on experience by implementing and manipulating essential data structures like stacks, queues, trees, and graphs from scratch.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated Data Structures Mentorship

Receive personalized assistance for understanding complex concepts, optimizing algorithms, and debugging your data structure implementations.

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Essential for Tech Placements & Interviews

We equip you with the fundamental knowledge and problem-solving skills crucial for cracking technical interviews and competitive programming challenges.

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Real-World Problem Solving with DS

Apply various data structures to solve practical, real-world problems, significantly enhancing your logical thinking and solution design capabilities.

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Engaging Algorithms & DS Community

Collaborate with a supportive community of peers and instructors, fostering shared knowledge, advanced problem-solving, and valuable networking opportunities in algorithm design.

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Data Structures Training Overview

Value Learning offers comprehensive Data Structures training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to provide a deep understanding of how data can be efficiently organized and managed for optimal program performance, which is a cornerstone of computer science and software development.

Data Structures are specific ways of organizing and storing data in a computer so that it can be accessed and modified efficiently. Our expert-led training covers various fundamental types of data structures: arrays, linked lists (singly, doubly, circular), stacks, queues, trees (including binary trees, Binary Search Trees, AVL, and Red-Black Trees), heaps, hash tables, and graphs. We place strong emphasis on understanding the time and space complexity of different operations (insertion, deletion, search, traversal) and on selecting the most appropriate data structure for specific problem-solving scenarios. This knowledge is crucial for efficient algorithm design, excelling in competitive programming, and building high-performance software applications in any programming language.

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Successful Learners

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Training Hours Delivered

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Enterprise Projects Covered

Data Structures Training Objectives

The Data Structures course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to provide a fundamental understanding of efficient data organization and equip learners with the ability to design and implement optimal solutions for complex computational problems.

Through this training, you will gain hands-on experience with implementing and manipulating various data structures, analyzing their time and space complexities, and applying them to solve diverse programming challenges effectively. You'll achieve proficiency in selecting and using appropriate data structures to significantly enhance algorithm efficiency and overall program performance, which is vital for any software role.

The primary goal of the training is to empower learners to confidently approach algorithmic problems, build robust and efficient software, and excel in technical interviews requiring strong Data Structures knowledge. This prepares them for highly sought-after roles as Software Developers, Algorithm Engineers, or for success in competitive programming environments.

To equip learners with comprehensive, practical experience in solving a wide array of problems using appropriate data structures, including understanding trade-offs between different structures, effectively debugging complex implementations, and mastering problem-solving techniques for real-world software development challenges, ensuring career readiness.

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Course Curriculum - Data Structures

Overview:
  • What are Data Structures? Importance and Types
  • Understanding Algorithms: Definition, Properties, and Categories
  • Time Complexity Analysis: Big O Notation (O(1), O(log n), O(n), O(n log n), O(n^2))
  • Space Complexity Analysis
  • Recursion and Iteration: Understanding Trade-offs

  • Arrays: Definition, Static vs. Dynamic Arrays
  • Basic Array Operations: Traversal, Insertion, Deletion, Searching
  • Multi-dimensional Arrays
  • Strings: Representation, Immutability (in some languages)
  • String Manipulation Algorithms: Reversal, Palindrome Check, Anagrams

  • Singly Linked Lists: Node Structure, Operations (Insertion, Deletion, Traversal)
  • Doubly Linked Lists: Advantages and Disadvantages
  • Circular Linked Lists
  • Applications of Linked Lists (e.g., Implementing Stacks/Queues)
  • Problem Solving: Reversing a Linked List, Detecting Cycles

  • Stacks: LIFO Principle, Operations (Push, Pop, Peek, isEmpty)
  • Array-based vs. Linked List-based Implementations of Stacks
  • Queues: FIFO Principle, Operations (Enqueue, Dequeue, Peek, isEmpty)
  • Array-based vs. Linked List-based Implementations of Queues
  • Applications: Function Call Stack, Breadth-First Search (BFS), Task Scheduling

  • Tree Concepts: Root, Node, Parent, Child, Leaf, Depth, Height
  • Binary Trees: Properties and Traversal (Inorder, Preorder, Postorder)
  • Binary Search Trees (BST): Insertion, Deletion, Searching, Performance
  • Balanced Trees (AVL, Red-Black Trees - conceptual overview)
  • Applications: File Systems, Database Indexing

  • Heaps: Max Heap and Min Heap Properties
  • Heap Operations: Insertion, Deletion (Heapify)
  • Implementing Priority Queues using Heaps
  • Heap Sort Algorithm
  • Applications: Event Simulation, Dijkstra's Algorithm (Priority Queue component)

  • Graph Representation: Adjacency Matrix vs. Adjacency List
  • Graph Traversal Algorithms: Breadth-First Search (BFS)
  • Graph Traversal Algorithms: Depth-First Search (DFS)
  • Topological Sort for Directed Acyclic Graphs (DAGs)
  • Applications: Social Networks, GPS Navigation, Network Routing

  • Dijkstra's Algorithm for Single Source Shortest Path
  • Bellman-Ford Algorithm for Shortest Path with Negative Weights
  • Floyd-Warshall Algorithm for All-Pairs Shortest Path
  • Minimum Spanning Tree (MST) Concepts
  • Prim's Algorithm and Kruskal's Algorithm for MST

  • Hashing Concepts: Hash Functions, Hash Collisions
  • Collision Resolution Techniques: Chaining, Open Addressing (Linear Probing, Quadratic Probing)
  • Hash Table Operations: Insertion, Deletion, Searching
  • Hash Sets and Hash Maps (Dictionaries)
  • Applications: Database Indexing, Caching, Symbol Tables

  • Comparison-based Sorts: Bubble Sort, Selection Sort, Insertion Sort
  • Efficient Comparison Sorts: Merge Sort, Quick Sort (Divide and Conquer)
  • Non-Comparison Sorts: Counting Sort, Radix Sort, Bucket Sort
  • Analyzing Time and Space Complexity of Various Sorting Algorithms
  • Choosing the Right Sorting Algorithm for Different Scenarios

  • Linear Search and its Time Complexity
  • Binary Search: Prerequisites and Implementation (Iterative & Recursive)
  • Jump Search and Interpolation Search (overview)
  • Comparison of Search Algorithms Based on Data Structure
  • Applications of Searching in Databases and Data Retrieval

  • Introduction to Dynamic Programming: Overlapping Subproblems and Optimal Substructure
  • Memoization (Top-down DP) vs. Tabulation (Bottom-up DP)
  • Solving Classic DP Problems: Fibonacci Sequence, Longest Common Subsequence
  • Knapsack Problem (0/1 Knapsack)
  • Understanding When to Apply Dynamic Programming

  • Understanding Greedy Approach vs. Dynamic Programming
  • Characteristics of Greedy Problems
  • Solving Classic Greedy Problems: Activity Selection Problem, Fractional Knapsack
  • Coin Change Problem (Greedy vs. DP)
  • Limitations and Pitfalls of Greedy Algorithms

  • Tries (Prefix Trees) for String Searching
  • Fenwick Trees (Binary Indexed Trees) for Range Queries
  • Segment Trees for Range Query Problems
  • Disjoint Set Union (DSU) / Union-Find Data Structure
  • Introduction to B-Trees and B+ Trees (conceptual, for database indexing)

  • Solving LeetCode/HackerRank Problems for Practice
  • Common Data Structure & Algorithm Interview Patterns
  • Mock Interviews and Whiteboard Coding Sessions
  • Debugging and Problem-Solving Strategies
  • Relevance of DS&A in Software Development Jobs in Hyderabad, Telangana, India
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