Python Lesson Plan for Full Understanding and Interview Prep

Table of Contents

1. Introduction
2. Python Fundamentals
   • Data Types
   • Variables and Operators
   • Control Flow
   • Functions
   • Object-Oriented Programming
   • Modules and Packages
3. Data Structures
   • Arrays and Lists
   • Linked Lists
   • Doubly Linked Lists
   • Stacks
   • Queues
   • Graphs
   • Heaps
4. LeetCode Exercises
   • Arrays and Strings
   • Linked Lists
   • Trees and Graphs
   • Dynamic Programming
5. Interview Preparation
   • Common Algorithms
   • Problem-Solving Techniques
   • Mock Interviews
6. Additional Resources

---

Introduction

This lesson plan covers the fundamental concepts of Python programming, essential data structures, and algorithmic problem-solving techniques. The curriculum is designed for full-blown understanding and interview preparation, aiming to provide learners with the knowledge and skills needed to succeed in technical interviews.

---

Python Fundamentals

Data Types

• Primitive Data Types: Integers, floats, booleans, and strings.
• Collections: Lists, tuples, sets, and dictionaries.
• Type Conversion: Converting between different data types using int(), float(), str(), etc.

Variables and Operators

• Variables: Assigning values to variables using the = operator.
• Arithmetic Operators: +, -, *, /, //, %, **.
• Comparison Operators: ==, !=, >, <, >=, <=.
• Logical Operators: and, or, not.

Control Flow

• Conditional Statements: if, elif, and else.
• Loops:
  • For Loops: Iterating over a range or collection.
  • While Loops: Executing code while a condition is True.
• Loop Control: Using break, continue, and pass.

Functions

• Defining Functions: Using the def keyword.
• Arguments and Return Values: Passing parameters and returning results.
• Lambda Functions: Creating anonymous functions with lambda.
• Higher-Order Functions: Functions that accept other functions as arguments (map, filter, reduce).

Object-Oriented Programming

• Classes and Objects: Defining classes and creating instances.
• Attributes and Methods: Adding data and functionality to classes.
• Inheritance: Extending classes to create specialized versions.
• Encapsulation and Abstraction: Managing access to data and simplifying complex systems.
• Polymorphism: Using methods interchangeably between different classes.

Modules and Packages

• Importing Modules: Using built-in and external modules.
• Creating Custom Modules: Organizing code into reusable modules.
• Virtual Environments: Managing dependencies with venv or virtualenv.
• Package Managers: Installing packages using pip.

---

Data Structures

Arrays and Lists

• Arrays: Fixed-size data structures for storing elements of the same type.
• Lists: Dynamic arrays in Python, allowing different data types and variable sizes.
• Basic Operations: Indexing, slicing, appending, inserting, and removing elements.

Linked Lists

• Singly Linked Lists: Nodes connected in one direction.
• Operations: Insertion, deletion, traversal, and searching.

Doubly Linked Lists

• Bidirectional Links: Nodes have pointers to both previous and next nodes, allowing traversal in both directions.
• Use Cases: Commonly used in navigation systems, undo/redo functionalities, and applications that require bidirectional traversal.

Stacks

• LIFO Principle (Last In, First Out): Data is accessed in reverse order of insertion; the last element added is the first to be removed.
• Operations: Includes push (add item), pop (remove item), and peek (view top item).
• Use Cases: Utilized in scenarios like function call stacks, expression parsing, and undo operations in text editors.

Queues

• FIFO Principle (First In, First Out): Data is accessed in the same order as insertion; the first element added is the first to be removed.
• Operations: Includes enqueue (add item), dequeue (remove item), and peek (view front item).
• Use Cases: Ideal for task scheduling, message processing, and implementing buffers.

Graphs

• Vertices and Edges: Graphs consist of nodes (vertices) connected by edges, which may be directed or undirected.
• Graph Types: Can be classified as directed, undirected, weighted (edges have weights), or unweighted.
• Traversal Algorithms:
  • Breadth-First Search (BFS): Explores all neighbors at the present depth before moving deeper.
  • Depth-First Search (DFS): Explores as far as possible along a branch before backtracking.
• Use Cases: Widely used in network routing, social networks, search engines, and map-based applications.

Heaps

• Heap Property: In a max heap, the parent node is always larger than the children, while in a min heap, the parent node is smaller.
• Common Operations: Insertion, extraction of the max/min element, and heapify operations to maintain the heap structure.
• Use Cases: Ideal for implementing priority queues, scheduling algorithms, and algorithms like Dijkstra's for finding the shortest path.