DSA 101

📚 Series Overview

Data structures and algorithms form the foundation of computer science and software engineering. After years of working on backend systems, distributed applications, and tackling coding challenges, I've come to appreciate how deeply DSA knowledge influences everyday engineering decisions—from choosing the right collection type to optimizing database queries.

This series distills my learning journey into practical, Python-focused articles that bridge theory and real-world application. Whether you're preparing for technical interviews or want to write more efficient code, these fundamentals will serve you throughout your career.

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🎯 What You'll Learn

By completing this series, you'll be able to:

• Analyze complexity - Evaluate time and space trade-offs for any algorithm

• Choose appropriate data structures - Match problems to optimal structures

• Implement from scratch - Build core data structures in Python

• Recognize patterns - Identify common algorithmic patterns in problems

• Optimize solutions - Transform brute-force approaches into efficient algorithms

• Solve interview problems - Apply systematic problem-solving strategies

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🗺️ Learning Path

Phase 1: Foundation (Articles 1-3)

Build the mental models and analytical skills needed for algorithm design.

Article	Topic	Key Concepts
1	Introduction to DSA	Why DSA matters, Big O notation, Python memory model
2	Complexity Analysis Deep Dive	Asymptotic analysis, amortized analysis, practical examples
3	Recursion Fundamentals	Call stack, base cases, memoization, tail recursion

Phase 2: Linear Data Structures (Articles 4-6)

Master sequential data organization and manipulation.

Article	Topic	Key Concepts
4	Arrays and Strings	Contiguous memory, two-pointer technique, sliding window
5	Linked Lists	Pointer manipulation, fast/slow pointers, cycle detection
6	Stacks and Queues	LIFO/FIFO principles, monotonic stacks, deque

Phase 3: Non-Linear Data Structures (Articles 7-9)

Explore hierarchical and networked data relationships.

Article	Topic	Key Concepts
7	Hash Tables	Hashing functions, collision resolution, Python dict internals
8	Trees	Binary trees, BST, traversals, balanced trees
9	Heaps and Graphs	Priority queues, graph representations, BFS/DFS

Phase 4: Algorithm Paradigms (Articles 10-12)

Learn the major algorithmic strategies and when to apply them.

Article	Topic	Key Concepts
10	Sorting Algorithms	Comparison sorts, stability, Python's Timsort
11	Searching Algorithms	Binary search variations, search space reduction
12	Dynamic Programming	Overlapping subproblems, state design, optimization

Phase 5: Advanced Topics & Practice (Articles 13-15)

Apply everything together with advanced techniques and problem-solving patterns.

Article	Topic	Key Concepts
13	Greedy & Backtracking	Greedy choice property, backtracking template, pruning
14	Graph Algorithms	Shortest path, MST, topological sort, union-find
15	Problem-Solving Patterns	Pattern catalog, interview strategies, optimization

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🛠️ Prerequisites

• Python 3.11+ - All examples use modern Python with type hints

• Basic programming - Variables, functions, loops, conditionals

• Command line familiarity - Running Python scripts

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📊 Complexity Reference Card

Time Complexity Classes

Notation	Name	Example
O(1)	Constant	Array index access
O(log n)	Logarithmic	Binary search
O(n)	Linear	Linear search
O(n log n)	Linearithmic	Merge sort
O(n²)	Quadratic	Bubble sort
O(2ⁿ)	Exponential	Recursive Fibonacci
O(n!)	Factorial	Permutations

Data Structure Operations

Structure	Access	Search	Insert	Delete
Array	O(1)	O(n)	O(n)	O(n)
Linked List	O(n)	O(n)	O(1)	O(1)
Hash Table	N/A	O(1)*	O(1)*	O(1)*
BST	O(log n)*	O(log n)*	O(log n)*	O(log n)*
Heap	O(1)†	O(n)	O(log n)	O(log n)

*Average case, †Min/Max only

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🎨 Visual Learning

Each article includes Mermaid diagrams to visualize:

• Data structure organization

• Algorithm execution flow

• State transitions

• Complexity comparisons

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💻 Code Style

All examples follow these conventions:

from typing import Optional, List
from dataclasses import dataclass

@dataclass
class TreeNode:
    """Binary tree node with type hints."""
    val: int
    left: Optional['TreeNode'] = None
    right: Optional['TreeNode'] = None

def binary_search(arr: List[int], target: int) -> int:
    """
    Find target in sorted array.
    
    Time: O(log n)
    Space: O(1)
    """
    left, right = 0, len(arr) - 1
    
    while left <= right:
        mid = left + (right - left) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    
    return -1

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🚀 Getting Started

1. Start with Phase 1 - Foundation concepts apply throughout

2. Code along - Type examples yourself, don't just read

3. Solve exercises - Each article ends with practice problems

4. Build projects - Apply concepts to real code

5. Review regularly - Spaced repetition cements understanding

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📖 Recommended Resources

Books

• Introduction to Algorithms (CLRS) - The comprehensive reference

• Algorithm Design Manual (Skiena) - Practical problem-solving focus

• Grokking Algorithms - Visual, beginner-friendly introduction

Practice Platforms

• LeetCode - Interview-style problems

• HackerRank - Structured learning paths

• Codeforces - Competitive programming

Python-Specific

• Python collections module documentation

• heapq module for priority queues

• bisect module for binary search utilities

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🏗️ Series Architecture

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Let's begin the journey into data structures and algorithms!

Next: Article 1 - Introduction to DSA