• Overview of logic gates and their importance in digital circuits.
• Explanation of commonly used logic gates: AND, OR, NOT, XOR, NAND, and NOR gates.
• Understanding the logical operations performed by each gate.
• Discussion on how logic gates are implemented using electronic components, such as transistors.
• Introduction to truth tables and their role in describing the behavior of logic gates.
• Hands-on exercises and examples to reinforce understanding of logic gates.

• Comprehensive introduction to the CPU, the core component of a computer.
• Exploring the main elements of a CPU: control unit, arithmetic logic unit (ALU), and registers.
• Functionality and responsibilities of the control unit, including instruction fetching and decoding.
• Role of the ALU in performing arithmetic and logical operations on binary data.
• Understanding registers and their importance in storing data and instructions.
• Detailed explanation of the fetch-decode-execute cycle and its significance in CPU operation.
• Discussion on how the CPU interacts with memory and I/O devices to execute instructions.

• Introduction to data types and their significance in programming.
• Explanation of fundamental data types, such as integers, floating-point numbers, characters, and booleans.
• Understanding the range and limitations of different data types.
• Overview of derived data types, including arrays, strings, and structures.
• Exploring the concept of variables and how they are used to store data of different types.
• Discussion on type conversion and typecasting.
• Practical exercises to reinforce understanding of data types and their usage in programming.

Throughout these weeks, there will be interactive sessions, coding exercises, and assignments to apply the concepts learned. Regular assessments will also be conducted to track progress and ensure comprehension of the material.

1.1 What are Data Structures?

Definition and purpose of data structures
How data structures organize and store data efficiently
The data structures that build Operating systems and 
execute programs. Introduce the idea of Buffers, 
how they apply to accessing and manipulating data.

1.2 Array-Based Data Structures

Introduction to arrays and their characteristics
Exploring linear data structures: stacks and queues
Understanding their implementation and operations
LIFO - The typical "stack"
FIFO - Queue (asynchonous Queues)

1.3 Linked Lists

Overview of linked lists and their properties
Singly linked lists vs. doubly linked lists
Insertion, deletion, and traversal in linked lists
Advantages and disadvantages of linked lists

Part 2: Hierarchical Data Structures 2.1 Trees

Understanding tree structures and their properties
Types of trees: binary trees, AVL trees, B-trees, etc.
Tree traversal algorithms: preorder, inorder, and postorder
Balanced vs. unbalanced trees

2.2 Binary Search Trees (BST)

Introduction to BSTs and their applications
BST operations: insertion, deletion, and search
Complexity analysis of BST operations
Balancing BSTs: AVL trees and Red-Black trees

2.3 Heaps

Introduction to heaps and their purpose
Heap operations: insertion, deletion, and heapify
Heap sort algorithm and its efficiency
Applications of heaps: priority queues and scheduling

Part 3: Advanced Data Structures 3.1 Graphs

Overview of graph theory and its applications
Directed vs. undirected graphs
Graph representations: adjacency matrix and adjacency list
Graph traversal algorithms: breadth-first search and depth-first search

3.2 Hashing

Understanding hash functions and hash tables
Collision resolution techniques: chaining and open addressing
Complexity analysis of hashing operations
Applications of hashing: dictionaries and caches

3.3 Advanced Topics

Self-balancing trees: Red-Black trees, B-trees, and their benefits
Trie structures and their applications in string matching
Skip lists and their efficiency in search operations
Bloom filters and their probabilistic nature

Part 4: Data Structures and Algorithm Analysis 4.1 Time and Space Complexity

Big O notation and its significance
Analyzing the efficiency of algorithms using Big O
Best, worst, and average case scenarios

4.2 Selecting the Right Data Structure

Guidelines for choosing the appropriate data structure
Considering efficiency, memory usage, and required operations
Real-world examples and trade-offs

4.3 Overview of Popular Programming Languages and Data Structures

Comparison of data structure implementations in popular languages
How different languages handle data structures