Visual Lab — Akshay Animated DSA

Module 1 · Arrays

Array Operations

Read, replace, insert and delete — watch each element shift and see why some operations are O(1) and others O(n).

Module 2 · Binary Search Introduction

First Bad Version

Watch a Range class halve the versions until start meets end, then race it against a linear search.

Module 2 · Binary Search Introduction

Search Insert Position

Find where a target is, or where it would go, in a sorted array — with the same Range class.

Module 2 · Binary Search Introduction

Find First and Last Position

One binary search run twice: for the target, then for target + 1, to find both ends of a block of repeats.

Module 2 · Binary Search Application

Koko Eating Bananas

Binary search on the answer: try speeds on a number line and add up the hours pile by pile.

Module 2 · Binary Search Application

Painter's Partition

Search the possible time limits, handing boards out to painters to test each guess.

Module 2 · Common Mistakes

Binary Search: Common Mistakes

A range that stops shrinking, a loop that runs once too often, a step that drops the answer: watch each one break, then the fix.

Module 3 · Linked List Implementation and Operations

Design Linked List

A dummy head and two helpers behind get, addAtHead, addAtTail, addAtIndex and deleteAtIndex — watch every pointer change.

Module 3 · Linked List Implementation and Operations

Middle of the Linked List

Fast and slow pointers: fast takes two steps for every one of slow, so slow stops on the middle.

Module 3 · Linked List Questions

Reverse Linked List

Three pointers walk the list and turn every link around, until the old tail is the new head.

Module 3 · Linked List Questions

Merge k Sorted Lists

Watch nodes slide out of k lists into one, always the smallest first, picked by a priority queue.

Module 3 · Linked List Questions

Reverse Nodes in k-Group

Cut a group off, turn its links around with three pointers, and stitch it back between its neighbours.

Module 4 · Hashing

Hashing Operations

Hash a key to its bucket, chain the collisions, then search, delete and iterate — and see why it's O(1).

Module 4 · Hashing

Two Sum

One pass with a HashMap of values seen so far — next to the brute-force double loop.

Module 4 · Hashing

Sort Array by Increasing Frequency

Count with a HashMap, sort the counts, and watch the cards slide into order.

Module 4 · Hashing

Subarray Sum Equals K

Prefix sums in a HashMap: each match lights up the subarray it found.

Module 4 · Hashing

Count Unique Characters of All Substrings

Each letter's previous and next copy bound the substrings it's unique in.

Module 5 · Heap Tree Implementation

Heap Tree as an Array

Arrows from every tree node to its array cell, then the parent and child formulas worked on any node.

Module 5 · Heap Tree Implementation

Heap Operations

Insert bubbles up, pop min sinks down — watch the tree and the array behind it move together.

Module 5 · Heap Tree Implementation

Sort an Array

Heap sort with the course's Heap class: add everything, then poll the root until it's empty.

Module 5 · Applications of Heap

Kth Largest Element in an Array

A min heap of the k largest so far — its root is the answer.

Module 5 · Applications of Heap

Top K Frequent Elements

Count with a HashMap, then keep the k most frequent in a min heap by frequency.

Module 5 · Applications of Heap

Find Median from Data Stream

Two heaps meeting in the middle: the median is always at their tops.

Module 6 · Stacks Introduction

Stack Operations

A stack on an array: push, pop and peek just move one top index — every operation O(1).

Module 6 · Stack Applications

Backspace String Compare

Type each string into a stack — letters push, # pops — then compare what's left.

Module 6 · Stack Applications

Closest Smaller on Left

The monotonic stack pattern: pop what can never be the answer, and the top is the closest smaller value.

Module 6 · Stack Applications

Largest Rectangle in Histogram

The closest smaller bar on both sides of every bar, from one stack pass — then each bar's widest rectangle.

Module 6 · Sum of Subarray Ranges

Sum of Subarray Minimums

Count how many subarrays each value is the minimum of, with the closest smaller value on each side.

Module 6 · Sum of Subarray Ranges

Sum of Subarray Ranges

Every subarray's maximum minus every subarray's minimum — two contribution passes with a stack.

Module 6 · Queues

Queue Operations

A queue on a circular array: enqueue at the rear, dequeue at the front, both wrapping around — every operation O(1).

Module 6 · Queues

Sliding Window Maximum

A monotonic deque of max candidates: smaller values leave from the back, expired ones from the front.

Module 7 · Sliding Window Intro

Contains Duplicate II

A window of the last k indices: each value's last index says in O(1) whether it repeats nearby.

Module 7 · Sliding Window Intro

Longest Substring Without Repeating Characters

Grow while no character repeats, measure, then shrink until the repeat leaves.

Module 7 · Sliding Window Intro

Max Consecutive Ones III

The longest window with at most k zeros to flip — grow, measure, shrink until a zero leaves.

Module 7 · Sliding Window Advanced

Smallest Distinct Window

Grow until every distinct character is in, shrink while it stays good — the smallest window that has them all.

Module 7 · Sliding Window Advanced

Minimum Window Substring

Match every character of t with counts, then shrink while still matched — the smallest window holding t.

Module 7 · Sliding Window Advanced

Count the Number of Good Subarrays

Grow until the window has k equal pairs, then count every longer subarray starting at left in one step.

Module 7 · Sliding Window Advanced

Maximum Number of Occurrences of a Substring

A fixed window of minSize: last indexes give the distinct count, a map counts every window within maxLetters.