4 Pillars of DSA

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1)  Procedural Programming Language

2) OOPs (C++,Java,Python etc)

3) STL/JavaCollection/Python DataTypes (list, set,dict, etc)

4) Recursion

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Detailed explanations will be posted on the YouTube channel Soon.

1) Procedural Oriented Programming Language

Overview of Procedural Programming Languages, Advantages & Examples

What Is Procedural Programming?

Procedural programming is a structured programming paradigm based on the concept of procedures

( functions or routines). It is a top-down approach in which applications are split into smaller procedures that are reusable and promote modularization.

 Main Characteristics of Procedural Programming

✅ Uses Functions / Procedures – The code is split up into reusable blocks (functions).

✅ Uses a Top-Down Approach – Execution is carried out logically, and step-by-step.

✅ Global and Local Variables – Functions may be aware of local or global data.

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 Popular Procedural Programming Languages

Language	Key Features	Used In
C	Fast, low-level, used in OS, embedded systems	Linux, Windows, IoT
Pascal	Structured, educational use	Legacy systems, teaching
Fortran	Scientific computing, high-performance	NASA, weather modeling
COBOL	Business and banking applications	Banking, legacy finance
Ada	Safety-critical systems	Aerospace, defense
BASIC	Beginner-friendly, early computing	Legacy applications
Go (Golang)	Procedural with concurrency features	Cloud, web services

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Benefits of Procedural Programming

Simplicity – Easy to understand and debug.
Modular Code – Functions improve reusability and maintainability.
Efficient Execution – Faster compared to object-oriented programming in some cases.
Better Control Over Hardware – Useful for system programming.
Easy to Translate into Machine Code – Optimized for performance.

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Procedural vs. Other Paradigms

Paradigm	Key Difference
Procedural	Uses functions/procedures to structure code.
Object-Oriented (OOP)	Uses objects and classes (e.g., Java, C++).
Functional	Emphasizes immutability and pure functions (e.g., Haskell, Lisp).

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Companies That Use Procedural Programming

4 Pillars of DSA

🏢 Microsoft – Windows kernel and Windows system utilities are daC: [9]
🏢 Apple – macOS and core iOS components in C.
🏢 NASA — Fortran for scientific computing and simulations
🏢 IBM – Cobol – for banking and financial systems
🏢 Google – Uses Golang (Go) extensively for cloud and backend services

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Conclusion

Procedural programming is still widely used, especially in system programming, scientific computing, and legacy applications. While newer paradigms like OOP and Functional Programming are popular, procedural languages continue to power critical software worldwide.

 

2) OOPs (C++,Java,Python etc)

What is OOP?

OOP stands for Object-Oriented Programming, and it is a programming paradigm based on the concept of objects, which may contain data in the form of fields i.e. attributes or properties, and code, in the form of procedures, i.e. methods. They can be thought of as objects that have attributes (data) and methods (behavior), representing real-world entities.

Key Benefits of OOP : 

✅ Encapsulated – Packages data and the functions that operate on it, protecting it.

✅ Abstraction – Hides complex implementation details by providing only necessary things.

✅ Inheritance – Reusing the code by creating new classes that inherit some of the properties of existing classes.

✅ Polymorphism -> allows us the flexibility to one function can be used with multiple types.

✅ Modularity & Maintainability — Reusable parts are easier to debug and collaborate on.

✅ Scalability – For large-scale applications like enterprise systems & web applications.

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Companies Using OOP

4 Pillars of DSA

🏢 Google – Uses Java, Python, and C++ for scalable applications.
🏢 Microsoft – C# and C++ for Windows, .NET applications.
🏢 Apple – Objective-C and Swift for macOS and iOS development.
🏢 Amazon – Java and C++ for AWS, backend services.
🏢 Facebook (Meta) – PHP, C++, and Hack for web applications.
🏢 Tesla – C++ for autonomous vehicle software.
🏢 Uber – Java and Python for backend services.

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OOP: The Backbone of Modern Software Development

Everything You’d Ever Want to Know about (the One and Only) Object-Oriented Programming!

Why OOP is the Future?

OOP dominance is very much visible, as it drives everything from mobile applications to large enterprise systems. The methodologies of object-oriented programming (OOP), which are encapsulation, inheritance, and polymorphism, allow developers to create scalable, reusable, and maintainable codebases.

OOP is a programming paradigm that helps in organizing data effectively when the code base is managed to build a complex web application, an AI-powered system, or a real-time analytics platform, thus allowing for the well-coordinated collection of data by a team of programmers.

 

3) STL/JavaCollection/Python DataTypes (list, set,dict, etc)

 

STL (C++), Java Collections, and Python Data Structures: A Comparison

Data structures represent the core of programming, and every programming language offers built-in libraries to handle the data structures efficiently. Here is how they look similar to STL in C++, Collections in Java, and Python data types.

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C++ Standard Template Library (STL)

STL provides generic data structures and algorithms.

STL Container	Type	Description
vector	Dynamic Array	Resizable array, fast random access.
list	Doubly Linked List	Fast insertions and deletions.
deque	Double-Ended Queue	Fast insertions/removals at both ends.
set	Ordered Set	Unique elements, sorted order.
unordered_set	Hash Set	Unique elements, no order, fast lookup.
map	Ordered Dictionary	Key-value pairs, sorted by key.
unordered_map	Hash Map	Key-value pairs, fast lookup.
stack	Stack (LIFO)	Last In First Out (LIFO).
queue	Queue (FIFO)	First In First Out (FIFO).
priority_queue	Min/Max Heap	Sorted queue (min-heap or max-heap).

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Java Collections Framework

Java provides an extensive Collections Framework for managing data.

Java Collection	Type	Description
ArrayList	Dynamic Array	Resizable, fast access.
LinkedList	Doubly Linked List	Fast insertions/removals.
HashSet	Hash Set	Unique elements, no order.
TreeSet	Ordered Set	Unique elements, sorted order.
HashMap	Hash Table	Key-value pairs, unordered.
TreeMap	Sorted Map	Key-value pairs, sorted by key.
Stack	LIFO Stack	Stack implementation.
Queue	FIFO Queue	Implements Queue interface.
PriorityQueue	Min/Max Heap	Ordered queue (natural sorting).

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Python Data Structures

Python provides built-in data types that are easy to use.

Python Data Type	Type	Description
list	Dynamic Array	Ordered, mutable, allows duplicates.
tuple	Immutable List	Ordered, immutable, allows duplicates.
set	Unordered Set	Unique elements, no duplicates.
dict	Hash Map	Key-value pairs, unordered (ordered in Python 3.7+).
deque	Double-Ended Queue	Fast insertions/removals at both ends.

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Comparison of STL, Java, and Python

Feature	C++ STL	Java Collections	Python
Performance	Fastest (low-level)	Slower than C++ (JVM overhead)	Slower than Java (dynamic typing)
Ease of Use	Medium (complex syntax)	Medium (boilerplate code)	Easiest (built-in, simple syntax)
Memory Usage	Efficient (manual control)	Higher (JVM overhead)	Higher (dynamic typing, garbage collection)
Sorting	sort() (O(n log n))	Collections.sort()	sorted()
Thread Safety	No built-in support	synchronized versions available	GIL (Global Interpreter Lock) restricts concurrency

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Which One to Use?

4 Pillars of DSA

1) C++ STL  is used for high-performance applications, game development, and system programming.
2) For enterprise applications, Android development, and large-scale systems, use Java Collections.
3) Become proficient in Data Structures of Python for Data science, web development, and prototyping.

4) Recursion

Recursion: Use Cases and Applications

What is Recursion?

Recursion is a method of programming in which we create a function that calls itself and better solves the task at hand by dividing the problem into subproblems. It works on the technique of Divide and Conquer.

Key Components of Recursion:

You can learn everything about recursion in just two steps:

✅ Base Case — The condition under the recursion stops

⏸️ Recursive Case — The part of the function to where it calls itself (Modified arguments in the same function call itself)

— Repeat steps 1 & 2 🙌 Recursion has solved our problem.

Use Cases of Recursion

🔹 Mathematical Computations

      Factorial (n! = n * (n-1)!)

      Fibonacci Series (F(n)=F(n−1)+F(n−2))

🔹 DS & ALGORITHMS

      Binary Tree, N-ary TreeTraversals (Tree Traversal)

      Graph traversal(DFS — Depth First Search)

      Operations on Linked List (Reverse, Search)

      Sort Algorithms (Merge Sort, Quick Sort)

🔹 Problem Solving

       Backtracking (N-Queens, Sudoku solver, Maze solving)

       Dynamic Programming (Recursion with Memoization мотивировано)

       Combinatorics (Generating Permutations, Subsets)

🔹 System and OS Level

       Recursion in Compilers (Parsing Expression, AST)

       Recursive Directory Traversal(Finding files, Searching in Directories)

🔹 AI & Game Development

       Minimax Algorithm (AI Decision Making, Chess, Tic-Tac-Toe)

       Fractals (Mandelbrot, Koch Snowflake)

Applications of Recursion in Real World

4 Pillars of DSA

🏢 Google – Search Algorithms and AI recursion

🏢 Amazon — for recommendation systems, for sorting vast amounts of data

🏢 Facebook (Meta) — Social Network Analysis with Recursive Graph Algorithms.

🏢 Microsoft — Navigation in compiler name resolutions (static binding) in a whole file system.

🏢 NASA – Recursion on simulation, and astrophysics calculations.

🏢 Game Development — Pathfinding algorithms (A* search, Minimax)

 

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3. Iteration vs. Recursion: When to Use?

Feature	Recursion	Iteration
Code Simplicity	More concise, elegant	Often longer, explicit
Performance	Can be slow (stack overhead)	Usually faster
Memory Usage	Uses stack memory (risk of stack overflow)	Uses loop variables (more efficient)
Best Used For	Trees, Graphs, Backtracking	Simple loops, array traversal

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Conclusion

Recursion is a powerful tool in problem-solving, especially for tree-based structures, graph traversals, and backtracking algorithms. While it provides an elegant solution, it must be used carefully to avoid stack overflow and optimize performance.

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