Data Structures Training In Hyderabad

| Ranked #Best Data Structures Training Institute in Hyderabad.

Become Perfect in Logical Programming

Data structures are an essential part of computer science and programming.

45 Hours Theory | 45 Hours Lab | Practice Sheets | Material

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Best Data Structures Training In Hyderabad​

About Data Structures Training:

Data Structures and Algorithms Training in Hyderabad are an essential part of computer science and programming. They are used to organize and store data in a way that allows efficient manipulation and retrieval. In C programming language, there are several built-in data structures, such as arrays, structures, and pointers, which can be used to create more complex data structures.

Arrays are a simple data structure in C, which can be used to store a collection of similar data types. They provide a convenient way to access and manipulate data elements. The size of an array is fixed at the time of declaration, and the elements are accessed using an index value.

Structures are another data structure in C, which allow you to group different data types into a single entity. They can be used to represent complex data types, such as records, and provide a way to access each member of the structure individually. Structures can also be nested, allowing you to create more complex data structures.

Pointers are another important concept in C, which allow you to manipulate data indirectly by referring to the memory address of the data. They can be used to create dynamic data structures, such as linked lists and trees, which can grow and shrink as needed during program execution.

Overall, understanding data structures in C is crucial for writing efficient and effective programs. By utilizing the built-in data structures and creating more complex structures using arrays, structures, and pointers, you can create programs that can handle large amounts of data with ease.

Typically Covers the Topics:

  1. Re-Collecting C:
    • Functions, Arrays, Strings, Structures
    • Pointers & Dynamic Memory Allocation

 

  1. Stacks and Queues:
    • Stack data structure (LIFO).
    • Queue data structure (FIFO).
    • Implementations using arrays and linked lists.
    • Stack and queue applications.

 

  1. Linked Lists:
    • Singly linked lists.
    • Doubly linked lists.
    • Circular linked lists.
    • Operations (insertion, deletion, traversal).
    • Reverse and merge linked lists.

 

  1. Trees:
    • Binary trees.
    • Binary search trees (BST).
    • Tree traversals (in-order, pre-order, post-order).
    • Balancing in AVL trees.
    • Heap data structure (min-heap and max-heap).

 

  1. Graphs:
    • Graph representations (adjacency matrix, adjacency list).
    • Graph traversal (DFS, BFS).
    • Shortest path algorithms (Dijkstra’s, Bellman-Ford).

 

  1. Hashing:
    • Hash tables and hash functions.
    • Handling collisions (chaining, open addressing).
    • Hash table operations (insertion, deletion, retrieval).

 

  1. Sorting Algorithms:
    • Bubble sort, selection sort, insertion sort.
    • Quick sort, merge sort, heap sort.
    • Analysis of sorting algorithms (time complexity).

 

  1. Searching Algorithms:
    • Linear search.
    • Binary search.
    • Hash-based searching.

 

  1. Expression Evaluation:
    • Infix Notation
    • Prefix Notation (Polish Notation)
    • Postfix Notation (Reverse Polish Notation – RPN)
    • Conversions
    • Evaluation

 

  1. Recursion:
    • Recursive functions and algorithms.

 

  1. Greedy Algorithms:
    • Concepts and applications.
    • Huffman coding, Dijkstra’s algorithm.

 

  1. Dynamic Programming:
    • Concepts and applications.
    • Longest common subsequence, knapsack problem.

 

  1. Graph Algorithms:
    • Depth-First Search (DFS).
    • Breadth-First Search (BFS).
    • Shortest path algorithms (Dijkstra’s, Bellman-Ford).

 

  1. Backtracking:
    • Concepts and applications.
    • N-Queens problem, Sudoku solver.

 

  1. Divide and Conquer:
    • Concepts and applications.
    • Merge sort, closest pair of points.

 

  1. Complexity Analysis:
    • Big O notation.
    • Time and space complexity analysis.

 

 

Interview Questions

Variable is an identity of memory allocation.

Using variable, we can store and process information.

  • We need to specify the data type in the declaration of every variable.
  • Data type describes about type of data allowed to store into variable and how much memory allocated.

Operator is a symbol that performs operation on data.

A Block of instructions defined to perform a task.

Array is used to store more than one value but of same type.

String is a sequence of characters.

Pointer stores address of memory location by which we can process information.

A user defined data type by which we can store more than one element of different types of data.

File is a user defined data type by which we can store the information physically(permanently).