A typical B.Tech course in Data Structures covers a range of topics including linear and non-linear data structures, their implementations, and applications. Key areas include arrays, linked lists, stacks, queues, trees (especially binary trees and their variations), and graphs. The course also delves into algorithm analysis (time and space complexity) and sorting/searching techniques. 

I. Introduction to Data Structures:

• Definition and Classification: Understanding what a data structure is, its purpose, and how they are classified (linear vs. non-linear). 
• Abstract Data Types (ADTs): Concepts of ADTs, their operations, and examples (like List ADT). 
• Memory Representation: Static (arrays) and dynamic (linked lists) memory allocation. 
• Basic Operations: Insertion, deletion, traversal, searching, sorting, and merging. 

II. Linear Data Structures:

  • Arrays:

• Definition, operations (insertion, deletion, searching, sorting), and sparse matrix representation. 
• Applications like representing polynomials. 

  • Linked Lists:

• Singly, doubly, and circular linked lists, their structures, and operations (insertion, deletion, traversal). 
• Advantages and disadvantages compared to arrays. 

  • Stacks:

• Definition, LIFO (Last-In, First-Out) principle, and common operations (push, pop, peek). 
• Applications like expression evaluation (infix, postfix, prefix) and the Tower of Hanoi problem. 

  • Queues:

• Definition, FIFO (First-In, First-Out) principle, and common operations (enqueue, dequeue). 
• Types of queues (circular, deque, priority queues) and their applications. 

III. Non-Linear Data Structures:

  • Trees:

• Basic terminology (node, root, child, parent, etc.).
• Binary trees (complete, full, extended), their representation (arrays, linked lists), and traversal methods (preorder, inorder, postorder).
• Binary Search Trees (BST): Creation, operations, and applications.
• Advanced tree concepts: AVL trees, B-trees, B+ trees, and their advantages.

Graphs:

• Basic terminology and representation (adjacency matrix, adjacency list).
• Graph traversal algorithms (BFS, DFS).
• Minimum spanning tree algorithms (Prim's, Kruskal's).
• Shortest path algorithms (Dijkstra's). 

IV. Algorithm Analysis:

• Time and Space Complexity: Understanding how to analyze the efficiency of algorithms using notations like Big O, Big Omega, and Big Theta. 

V. Sorting and Searching:

• Sorting Algorithms: Bubble sort, insertion sort, selection sort, merge sort, quicksort, etc.
• Searching Algorithms: Linear search, binary search.