Data Structure

Are you interested in enhancing your writing skills and advancing your software development profession? Please refrain from continuing your search. Our data structure course offers the potential to unlock a multitude of opportunities.

The identification and analysis of data patterns are crucial components in the development of efficient software systems. Databases facilitate the storage and organization of data, thereby enhancing its accessibility, modifiability, and suitability for scholarly examination.

Regardless of one’s level of experience in coding, it is imperative to possess a comprehensive understanding of data structures. If one desires to attain expertise in this particular domain, our Data Structure course is an ideal choice.

A data structure is a fundamental concept within computer science and software engineering. It pertains to how data is arranged, stored, and manipulated within a computer program or system.

The utilization of data structures is imperative in the management and effective retrieval of data, and they hold significant importance in the development and enhancement of algorithms.

Data structures offer a means of storing and arranging data in a manner that facilitates efficient activities such as insertion, deletion, retrieval, and searching. Various data structures are intentionally created to fulfill distinct objectives and possess advantages and disadvantages. 

Syllabus of Data Structure Course:

• Part 1: Introduction to Python and Data Structures in Python 
• Overview of data structures and their importance in Python 
• Basic data types in Python (int, float, str, list, tuple, dict, set) 
• Memory representation (stack vs. heap) 
• Time and space complexity analysis in Python 
• Part 2: Arrays and Lists 
• Introduction to arrays and lists
• One-dimensional arrays in Python
• Multidimensional arrays in Python 
• List data structure in Python 
• List operations and complexity analysis
• Part 3: Implementing arrays and lists Stacks and Queues 
• Stack data structure in Python 
• Queue data structure in Python 
• Implementing stacks and queues using lists 
• Applications and use cases of stacks and queues in Python 
• Part 4: Linked List 
• Linked list Data Structure in Python 
• Singly Linked list 
• Doubly Linked list 
• Part 5: Circular Linked list Trees 
• Introduction to trees 
• Binary trees and binary search trees (BST) in Python
• Tree traversal algorithms (in-order, pre-order, post-order) in Python 
• AVL trees (self-balancing BST) in Python 
• Tree applications and operations in Python 
• Part 6: Heaps and Priority Queues 
• Introduction to heaps in Python 
• Max heap and min heap in Python 
• Heap operations (insert, delete, extract) in Python
• Priority queues and their applications in Python 
• Part 7: Graphs
• Introduction to graphs (vertices and edges) in Python 
• Types of graphs (directed vs. undirected, weighted vs. unweighted) in Python
• Graph representations (adjacency matrix, adjacency list) in Python 
• Graph traversal algorithms (DFS, BFS) in Python
• Shortest path algorithms (Dijkstra’s) in Python
• Minimum spanning tree (Prim’s, Kruskal’s) in Python 
• Part 8: Sorting and Searching Algorithms
• Sorting algorithms (selection sort, bubble sort, insertion sort, merge sort, quicksort) in Python
• Time complexity analysis of sorting algorithms in Python 
• Searching algorithms (linear search, binary search) in Python