Python + DSA

Python 30 Days Plan - GitHub Step by Step 

# DSA - Easy Level - Roadmap

🧭 DSA Beginner’s 4-Week Notion Planner

📅 Overview

Goal: Master DSA fundamentals in 4 weeksTime per day: ~1–2 hoursDifficulty: Beginner-friendlyPlatform suggestions: LeetCode, GeeksforGeeks, HackerRank

✅ WEEK 1 – Foundations + Arrays + Strings

🎯 Goals

Understand algorithm complexity

Practice array & string manipulation

🔹 Topics

[ ] Time & Space Complexity

[ ] Arrays: Traversal, Insertion, Deletion

[ ] Searching: Linear & Binary Search

[ ] Reverse, Rotate, Max/Min, Sum

[ ] Strings: Palindrome, Reverse, Frequency, Anagram

💻 Practice Problems

[ ] Two Sum – LeetCode #1

[ ] Reverse an array

[ ] Find the second largest element

[ ] Check palindrome string

[ ] Count character frequency in string

🧠 Notes / Reflection

> What was hardest to understand this week?How do I visualize array or string operations better?

✅ WEEK 2 – Linked List + Stack + Queue

🎯 Goals

Learn dynamic linear structures

Understand how stacks and queues work

🔹 Topics

[ ] Linked List: Insert, Delete, Reverse

[ ] Find middle element

[ ] Stack: Push, Pop, Peek

[ ] Queue: Enqueue, Dequeue, Circular Queue

💻 Practice Problems

[ ] Reverse a linked list (iterative & recursive)

[ ] Check for balanced parentheses

[ ] Implement queue using array or linked list

🧠 Notes / Reflection

> Which data structure felt easiest?Could I implement them from scratch?

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✅ WEEK 3 – Trees + Searching & Sorting

🎯 Goals

Learn tree structures and basic sorting

🔹 Topics

[ ] Binary Tree: Traversals (Inorder, Preorder, Postorder)

[ ] Count nodes & height

[ ] Binary Search Tree (Insert, Search)

[ ] Sorting: Bubble, Selection, Insertion, Merge

[ ] Searching: Binary Search (detailed)

💻 Practice Problems

[ ] Implement binary tree traversals

[ ] Search in BST

[ ] Implement sorting algorithms

[ ] Binary Search on sorted array

🧠 Notes / Reflection

> Which traversal did I find easiest to remember?What is the time complexity of each sorting algorithm?

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✅ WEEK 4 – Recursion + Hashing + Patterns

🎯 Goals

Get comfortable with recursion

Learn to use hashmaps for optimization

🔹 Topics

[ ] Recursion basics: Factorial, Fibonacci

[ ] Sum of array using recursion

[ ] Hashing: Frequency maps, Sets

[ ] Prefix sum, Sliding window, Two pointers

💻 Practice Problems

[ ] Recursive Fibonacci

[ ] Subarray with given sum

[ ] Two-sum (hashmap version)

[ ] Maximum sum subarray (Kadane’s Algorithm – bonus)

🧠 Notes / Reflection

> How can recursion simplify certain problems?What patterns repeat across problems?

🏁 Final Week Project Ideas

Pick one mini-project:

[ ] Student Record System (Linked List + Sorting)

[ ] Text Analyzer (String + HashMap)

[ ] Simple Calculator (Stack)

[ ] Binary Tree Visualizer

🎓 Resources

📘 GeeksforGeeks DSA Course💻 LeetCode Easy Problems📖 DSA Made Easy – Narasimha Karumanchi🌐 Visualgo.net – visualize data structures

[Month 2 – DSA Intermediate Level Roadmap](https://www.notion.so/Month-2-DSA-Intermediate-Level-Roadmap-291da04f287480af8052f2f3879f80f3?pvs=21)

[Python Basics Roadmap - Beginners ](https://www.notion.so/Python-Basics-Roadmap-Beginners-293da04f2874808abe54f8334e8310d7?pvs=21)

# Python Basics Roadmap - Beginners

Step 1: Python Basics

Before DSA, you need a strong foundation in Python syntax and core features.

Topics to cover:

Variables, data types (int, float, str, bool)

Operators (+, -, *, /, %, **)

Conditional statements (if, elif, else)

Loops (for, while)

Functions (def, return)

Input/Output (input(), print())

Example:

def add(a, b): return a + b

print(add(5, 3)) # Output: 8

---

Step 2: Python Collections

Python’s built-in data structures are essential for DSA:

1. Lists – Dynamic arrays

arr = [1, 2, 3, 4]arr.append(5)arr.pop()

2. Tuples – Immutable lists

3. Dictionaries – Key-value pairs

d = {'a': 1, 'b': 2}print(d['a'])

4. Sets – Unique elements, fast lookup

s = {1, 2, 3}s.add(4)

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Step 3: Loops & List Comprehensions

Loops: Iterating arrays, dictionaries, or strings

List comprehensions: Compact way to create lists

arr = [i*2 for i in range(5)] # Output: [0, 2, 4, 6, 8]

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Step 4: Functions & Recursion

Functions: Break problems into smaller pieces

Recursion: Functions calling themselves (very important in DSA)

def factorial(n): if n == 0: return 1 return n * factorial(n-1)

print(factorial(5)) # Output: 120

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Step 5: Python-Specific Features for DSA

Slicing: arr[start:end]

Enumerate: Loop with index

Zip: Combine multiple lists

Defaultdict & Counter (from collections) for frequency counting

Heapq for priority queue (heap)

Deque for fast append/pop on both ends

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Step 6: Basic Algorithms in Python

Searching: Linear Search, Binary Search

Sorting: Built-in sort() / sorted()

Two-pointer techniques: Efficient array problems

Sliding window: Maximum/minimum subarray problems

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Step 7: Problem Solving

Start small problems on lists and strings

Gradually move to stacks, queues, recursion, DP

Use Python’s libraries smartly, but also know manual implementations

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