DSA in Python Tutorial

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Data Structures & Algorithms is the single most tested subject in coding interviews, and Python's concise syntax makes it a genuinely pleasant language to learn it in — you spend less time on boilerplate and more time on the actual pattern being taught. This track covers the exact same 31 lessons as our DSA in Java track, so if you switch between the two you'll always recognize where you are. Every lesson here uses real, runnable Python 3 code, and every example ends with an honest time/space complexity breakdown — not glossed over, because that's exactly what interviewers grill candidates on.

Prerequisite: This track assumes basic Python syntax (functions, lists, loops, classes). If that's shaky, a quick pass through the Python track first will make this much smoother.

A Few Things Python Changes About DSA

In Java...	In Python...
Arrays have a fixed size (`int[]`)	Lists are dynamic by default — but we'll still cover fixed-size array thinking since interviews often ask for it explicitly
You declare a Node class with typed fields	Same idea, just without explicit types — Python's dynamic typing means less ceremony, same structure
`PriorityQueue` is built-in	Python's `heapq` module gives you a min-heap directly on a plain list — slightly different API, same idea
HashMap / HashSet	Python's `dict` and `set` — arguably even more central to idiomatic Python than in Java

A Quick Look Ahead

Example: Reversing a Singly Linked List

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def reverse(head):
    prev = None
    curr = head

    while curr:
        next_node = curr.next
        curr.next = prev
        prev = curr
        curr = next_node

    return prev

Time complexity: O(n) — one pass. Space complexity: O(1) — no extra structure, just three references. You'll see exactly this kind of complexity breakdown after every code example in this track.

What You'll Learn In This Track

Area	Covered in
Foundations	Why DSA matters, Big-O time/space complexity, lists, strings
Linear data structures	Singly/doubly/circular linked lists, stacks, queues
Searching & sorting	Linear/binary search, bubble/selection/insertion sort, merge sort, quick sort
Recursion & backtracking	Recursion fundamentals, backtracking problems
Trees	Binary trees & traversals, BST, heaps & priority queues (heapq), balanced trees intro
Hashing & graphs	dict/set, graph representation, BFS/DFS, Dijkstra, MST
Advanced patterns	Two pointers & sliding window, dynamic programming (1D & 2D), greedy algorithms
Wrap-up	Big-O cheat sheet, how to approach a DSA interview, best practices recap

Next steps: Head to Why DSA Matters to see exactly how this connects to real interviews before we touch any code.

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