Python  DSA

Fee : ₹35,000 + GST  ₹6300, Duration : 5 months,  Contact : patkar@rajeshpatkar.com

1. Structured Programming

A. Information Modeling

1. Variables and Data Types

   1. Understanding different data types (int, float, str, bool)

   2. Variable declaration and assignment

   3. Type casting and type checking

2. Operators and Expressions

   1. Arithmetic, comparison, logical, and bitwise operators

   2. Operator precedence and associativity

B. Control Structures

1. Conditional Statements

   1. if, elif, else constructs

   2. Nested conditionals

   3. The match statement

2. Iterative Statements

   1. while loops

   2. for loops and iteration over sequences

   3. Loop control statements: break, continue, else clause in loops

C. Functions

1. Definition and Invocation

   1. Defining functions using def

   2. Calling functions with positional and keyword arguments

2. Parameters and Arguments

   1. Default parameters

   2. Variable-length arguments (*args, **kwargs)

3. Return Values

   1. Returning single or multiple values

   2. The None value

4. Scope and Lifetime

   1. Local vs. global variables

   2. The global and nonlocal keywords

5. Activation Records

   1. Understanding call stack and function call mechanics

D. Console I/O

1. Using print() for output formatting

2. Reading user input with input()

3. String formatting methods (%, .format(), f-strings)

E. Collections

1. Lists

   1. Creation, indexing, slicing, and methods

   2. List comprehensions

2. Tuples

   1. Immutable sequences

   2. Packing and unpacking

3. Strings

   1. String methods and operations

   2. Escape sequences and raw strings

A. Object Based

1. Objects and Classes

   1. Understanding objects: state and behavior

   2. Defining classes using class keyword

   3. Instantiating objects

2. Attributes and Methods

   1. Instance variables (attributes)

   2. Defining methods

   3. The self parameter

3. Constructors

   1. The __init__ method

   2. Initialization of object state

B. Object Oriented

1. Inheritance

   1. Single and multilevel inheritance

   2. Using super() to access parent class methods

2. Multiple Inheritance

   1. Method Resolution Order (MRO)

   2. Potential pitfalls and the Diamond Problem

3. Method Overriding

   1. Overriding parent class methods

   2. Polymorphism concepts

C. Exception Handling

1. Error Handling Constructs

   1. Using try, except, finally, and else blocks

   2. Catching specific exceptions

2. Custom Exceptions

   1. Defining user-defined exceptions by extending Exception class

3. Context Managers

   1. The with statement

   2. Implementing context managers using __enter__ and __exit__ methods

D. Collections

1. Dictionaries

   1. Key-value pairs

   2. Dictionary methods and comprehension

2. Sets

   1. Unordered collections of unique elements

   2. Set operations (union, intersection, difference)

E. File Handling

1. Basic File Operations

   1. Opening files using open()

   2. Reading (read(), readline(), readlines())

   3. Writing and appending data

2. File Pointers

   1. Using tell() and seek() methods

   2. Understanding file modes ('r', 'w', 'a', 'rb', 'wb')

A. Core Concepts

1. Closures

   1. Functions with enclosed scopes

   2. Nested functions and nonlocal variables

2. Lambda Functions

   1. Anonymous function syntax

   2. Use cases for concise functions

B. Higher Order Functions

1. Functions as First-Class Objects

   1. Passing functions as arguments

   2. Returning functions from functions

2. Built-in Higher-Order Functions

   1. map(), filter(), and reduce() (from functools)

   2. List comprehensions as alternatives

C. Generator

1. Generator Functions

   1. Using yield to produce iterators

   2. Memory-efficient data handling

2. Generator Expressions

   1. Similar to list comprehensions but with lazy evaluation

D. Decorator

1. Function Decorators

   1. Modifying functions using other functions

   2. Using @decorator_name syntax

2. Common Use Cases

   1. Logging, access control, caching

E. Modules and Packages

1. Modules

   1. Understanding and creating modules

   2. Importing modules (import, from ... import)

2. Packages

   1. Directory structures for packages

   2. Using __init__.py

   3. Relative imports

A. Fundamental Data Structures

1. Stack

1. Introduction

   1. Definition and LIFO principle

   2. Real-world analogies (e.g., stack of plates)

2. Implementations

   1. Array Implementation

      1. Fixed-size stacks using lists

   2. List Implementation

      1. Dynamic stacks using Python lists

3. Operations

   1. push(), pop(), peek(), is_empty()

4. Applications

   1. Expression evaluation (infix to postfix conversion)

   2. Backtracking algorithms (e.g., maze solving)

   3. Undo mechanisms in software

2. Queue

1. Introduction

   1. Definition and FIFO principle

   2. Real-world analogies (e.g., queues in a supermarket)

2. Implementations

   1. Array Implementation

      1. Using lists with pointers

   2. List Implementation

      1. Using collections.deque for efficient operations

3. Operations

   1. enqueue(), dequeue(), peek(), is_empty()

4. Types of Queues

   1. Circular Queue

      1. Efficient use of storage by connecting the end to the front

   2. Priority Queue

      1. Elements processed based on priority

   3. Deque (Double-Ended Queue)

      1. Insertion and deletion from both ends

5. Applications

   1. Order processing systems

   2. Scheduling algorithms

   3. Breadth-First Search (BFS)

3. Linked Lists

1. Singly Linked List

   1. Nodes with data and a pointer to the next node

   2. Operations: insertion, deletion, traversal

2. Doubly Linked List

   1. Nodes with pointers to both previous and next nodes

   2. Bidirectional traversal

   3. Use cases over singly linked lists

3. Circular Linked List

   1. Implementation and benefits

4. Applications

   1. Implementing stacks and queues

   2. Managing music playlists or photo galleries

4. Strings

1. String Manipulation

   1. Slicing, concatenation, and methods

2. String Algorithms

   1. Pattern matching algorithms (e.g., Knuth-Morris-Pratt)

   2. Anagram checks

   3. Palindrome detection

3. Applications

   1. Text processing

   2. Data validation

5. Hash Tables

1. Introduction to Hashing

   1. Hash functions and their importance

2. Implementations

   1. Using dictionaries in Python

3. Collision Resolution Techniques

   1. Chaining (linked lists)

   2. Open addressing (linear probing, quadratic probing)

4. Applications

   1. Caching mechanisms

   2. Database indexing

   3. Implementing associative arrays

6. Trees

1. Tree Terminology

   1. Nodes, edges, root, leaves, height, depth

2. Binary Trees

   1. Structure and properties

   2. Traversal methods: in-order, pre-order, post-order

3. Binary Search Trees (BST)

   1. Insertion, deletion, searching

4. Threaded Trees

   1. Purpose and implementation

5. AVL Trees

   1. Self-balancing properties

   2. Rotations (single and double)

6. Heaps

   1. Min-Heap and Max-Heap

   2. Heap operations

7. Applications

   1. Expression parsing

   2. Priority queues

   3. Memory management

7. Graphs

1. Graph Terminology

   1. Vertices (nodes), edges, paths, cycles

2. Implementations

   1. Adjacency Matrix

   2. Adjacency List

3. Graph Traversal Algorithms

   1. Breadth-First Search (BFS)

   2. Depth-First Search (DFS)

4. Shortest Path Algorithms

   1. Dijkstra's Algorithm

   2. Bellman-Ford Algorithm

   3. A* Algorithm

5. Topological Sort

   1. Use in scheduling tasks

6. Applications

   1. Social network analysis

   2. Routing algorithms

   3. Dependency resolution

B. Advanced Data Structures

1. Disjoint Sets (Union-Find)

   1. Concepts

      1. Representing disjoint subsets

   2. Operations

      1. find() with path compression

      2. union() by rank or size

   3. Applications

      1. Network connectivity

      2. Kruskal's algorithm for MST

      3. Detecting cycles in graphs

2. Tries

   1. Introduction

      1. Prefix trees for efficient retrieval

   2. Operations

      1. Insertion, search, deletion

   3. Applications

      1. Auto-completion features

      2. Spell checking

C. Algorithmic Techniques

1.  Sorting Algorithms

   1. Elementary Sorting Algorithms

      1. Bubble Sort

      2. Selection Sort

      3. Insertion Sort

   2. Efficient Sorting Algorithms

      1. Merge Sort

      2. Quick Sort

      3. Heap Sort

   3. Hybrid Sorting Algorithms

      1. Tim Sort (Python's built-in sort algorithm)

   4. Non-Comparison Sorts (Optional)

      1. Counting Sort

      2. Radix Sort

   5. Algorithm Analysis

      1. Time Complexity (Big O notation)

      2. Space Complexity

      3. Stability and in-place sorting

2.  Searching Algorithms

   1. Linear Search

   2. Binary Search

      1. Prerequisites (sorted data)

      2. Implementation and analysis

3. Recursion and Backtracking

   1. Understanding Base Cases and Recursive Calls

   2. Recursion vs. Iteration

   3. Common Recursive Algorithms

      1. Factorial, Fibonacci sequence

   4. Backtracking

      1. N-Queens problem

      2. Sudoku solver

4. Dynamic Programming

   1. Principles and Methodology

      1. Overlapping subproblems

      2. Optimal substructure

   2. Techniques

      1. Memoization (top-down)

      2. Tabulation (bottom-up)

   3. Classic Problems

      1. Knapsack problem

      2. Longest Common Subsequence

      3. Coin change problem

5. Greedy Algorithms

   1. Concepts

      1. Making locally optimal choices

   2. Common Problems

      1. Activity selection

      2. Huffman coding

      3. Fractional Knapsack problem

6. Divide and Conquer

   1. Techniques

      1. Master theorem

      2. Applications beyond sorting

   2. Applications

      1. Closest pair of points problem

      2. Karatsuba algorithm for fast multiplication

D. Advanced Graph Algorithms

1. Minimum Spanning Trees

   1. Prim's Algorithm

   2. Kruskal's Algorithm

2. Strongly Connected Components

   1. Tarjan's Algorithm

3. Advanced Shortest Path Algorithms

   1. Floyd-Warshall Algorithm

      1. All pairs shortest paths

   2. Johnson's Algorithm

E. Algorithmic Complexity

1. Time and Space Complexity

   1. Big O, Big Θ, Big Ω notations

   2. Best, average, and worst-case scenarios

2. Amortized Analysis

   1. Aggregate method

   2. Accounting method

   3. Potential method