What you will learn

How arrays map onto contiguous bytes of RAM and what that controls

How multidimensional arrays serialise via row-major and column-major order

The two pointer pattern in all three of its forms: direct, reduction, and subproblem

Fixed-sized and variable-sized sliding windows, and when each applies

Line sweep for interval merging and maximum overlap

How to design a dynamic array with amortised O(1) append

Why this course

Most engineers can write arr[i] without thinking, then freeze when an interviewer asks why that index lookup is O(1). The shortcut works until you hit a problem where the answer depends on knowing how the array sits in memory, how a sliding window updates in amortised constant time, or why sorting first turns an O(N²) search into O(N).

This course rebuilds your understanding of arrays from the byte layout up, then walks you through the eight problem-solving patterns that cover almost every array question you will ever see in a coding interview. You finish by implementing a dynamic array of your own.

Requirements

The course assumes you can read code in any mainstream language, but no prior data structures experience is needed.

Comfort writing loops, conditionals, and functions in at least one language.
Familiarity with basic Big-O notation (O(N), O(log N), O(1)).
A willingness to think about memory addresses rather than just variable names.

Overview

An array is the data structure every other data structure is built on. Hash tables use one underneath. Heaps live inside one. A C++ vector, Java ArrayList, JavaScript Array, Python list, and Go slice are all the same idea dressed up in different syntax: a contiguous block of memory with index-based access. If you understand that block of memory, every operation's complexity becomes obvious instead of memorised.

How an array maps from indices to memory addresses

This course covers both halves of the problem: how the data structure actually works, and how to recognise which of the eight standard patterns a given problem fits into.

Fundamentals

The course opens with the memory model. You see how RAM is a linear sequence of byte-addressed cells, how an array's base address anchors its layout, and how the index i becomes the address base + i * element_size. Once that picture is clear, the cost of every supported operation, access, insertion at the end, insertion in the middle, deletion, and traversal, follows directly without memorisation.

From there the course extends to multidimensional arrays. You trace how a 2x2x3 integer array gets laid out in row-major order (C, C++, Objective-C) and column-major order (Fortran, MATLAB, Julia), and how the address of arr[i][j][k] is computed in each convention. This section answers the questions most courses skip: why iterating a 2D array by row is faster than by column in C, and what cache locality has to do with it.

The fundamentals close with a hands-on look at dynamic arrays, the construct most working developers actually use day to day. You see how doubling the underlying capacity gives you amortised O(1) append even though any single resize is O(N).

Problem solving

After the fundamentals, the rest of the course is patterns. Most array problems you will see in an interview reduce to one of eight templates. Once you can recognise the template, the solution writes itself.

The 8 array patterns you'll master

Two pointers (direct)

Two indices moving toward each other through a sorted or symmetric array, solving reversal and pair-finding in O(N) time and O(1) space. Flip Characters, Palindrome Checker, Vowel Exchange, Reverse Words, Reverse Segments, Reverse Word Order.

Two pointers (reduction)

Sort first, then close in on a target by moving the left or right pointer based on the running sum, turning O(N²) brute force into O(N) after the sort. Two Sum, Target Limited Two Sum, Duplicate Aware Two Sum, Largest Container.

Two pointers (subproblem)

Fix one index and run a two pointer search on the remaining slice, the canonical recipe for k-sum and rotation problems. K Rotations, Three Sum, Approximate Three Sum, Four Sum.

Simultaneous traversal

Two indices walking two arrays in lockstep, advancing one or both per iteration based on a comparison, solving merges and subsequence checks in O(N + M) time. Subsequence Checker, Merge Sorted Arrays, Unique Intersections, Repeated Intersections.

Fixed sized sliding window

A window of size k that slides across the array updating an aggregate via one add and one remove per step, computing rolling sums and counts in O(N) time. Subarray Size Equals K, Maximum Ones, Negative Window, Even Odd Count.

Variable sized sliding window

A window with two moving boundaries that expand and contract based on a constraint, processing subarrays in O(N) time with O(1) extra space. Consecutive Ones, Product Conundrum, Maximum Subarray Sum, Consecutive Ones With K Flips.

Interval merging

A line-sweep technique that sorts intervals by start time and expands the last merged interval whenever the next one overlaps, running in O(N log N) time. Verify Schedule, Overlap Reduction, Employee Free Time, Insert Interval.

Maximum overlap

A point-event line sweep that splits intervals into start and end points, sorts them, then sweeps to track concurrent overlaps, the engine behind minimum meeting rooms in O(N log N) time. Minimum Meeting Rooms, Remove Intervals, Busiest Interval, Peak Resource Requirement.

Each pattern is taught in three layers. First an Understanding lesson explains the technique on a problem where the pattern is obvious. Then an Identifying lesson teaches you the signals that tell you a new problem fits the template, the same triggers an experienced engineer spots in an interview. Then four to six problems give you progressively harder applications. The course ends with a design capstone where you implement a dynamic array of your own, putting the memory model and the resize-and-copy mechanic into working code.

How this course is different

Most array material on the internet either skips the memory model or skips the patterns. This course refuses to skip either.

Most DSA resources

This course

✗Treat arrays as a one-line topic before jumping to "100 array problems".

✓Opens with how arrays sit in memory, so every complexity claim has a reason behind it.

✗Teach problems in isolation, so you memorise solutions but cannot recognise the pattern next time.

✓Groups problems by pattern, then teaches the identification signals before the solution.

✗Skip the memory model, leaving you unable to explain why insertion at the front is O(N).

✓Covers all three two-pointer variants and both sliding-window variants as distinct templates.

✗Cover two pointers and sliding window as if each is a single trick instead of a family.

✓Treats interval merging and maximum overlap as their own line-sweep family with shared mechanics.

✗Stop at the algorithm and never have you build a dynamic array yourself.

✓Ends with a dynamic array implementation, the data structure your language already gives you.

Who this course is for

The course suits anyone who needs to actually understand arrays rather than just guess at solutions.

New programmers who have written loops but never thought about what arr[i] does at the hardware level.
Self-taught and bootcamp graduates who can solve LeetCode Easy problems but stall on Medium when the pattern is not obvious.
Working developers who use ArrayList or vector daily but cannot explain why an append is amortised O(1).
Engineers preparing for FAANG and Big Tech interviews where two-pointer and sliding-window questions appear in nearly every onsite.
Returning engineers who learned arrays years ago and want a clean refresher grounded in concrete patterns.
Anyone who has noticed they can write a solution but not justify its complexity, and wants to fix that.

Continue your DSA journey

Arrays connect to nearly every other topic in this curriculum. After this course, the natural next steps depend on where you want to deepen.

Singly linked list: The contrast between an array and a linked list, contiguous versus pointer-chased memory, is the cleanest way to internalise why each data structure has the complexity profile it does. Worth doing right after this course.
Hash table: Arrays and hash tables are the two structures most interview problems combine. Many array patterns, prefix sum, frequency counting, complement lookup, become hash table problems with one substitution. This course teaches the patterns themselves.
Stack: Several array problems collapse cleanly into stack problems, especially the previous-closest and next-closest patterns. Stacks also give you an iterative way to walk a binary tree without recursion.
Sorting: Several array patterns assume the input is sorted (two pointers reduction, interval merging, maximum overlap). Knowing how the sorting happens, and how quicksort and mergesort behave on real inputs, makes those patterns click faster.
Searching: Binary search and its lower-bound and upper-bound variants are the missing companion to arrays. Once you have a sorted array, binary search is what unlocks O(log N) lookups instead of O(N) scans.
Dynamic programming: Almost every DP problem ends up using a 1D or 2D array for its state table. The memory layout and traversal order you learned here transfer directly to how you set up a DP recurrence.

Frequently asked questions

Code examples use Python for readability, but every concept (memory layout, indexing, dynamic resize) is language-agnostic. The course explicitly shows how arr[i] behaves in C, JavaScript, and Python, and explains where they differ.

Yes. The course starts from how RAM stores bytes and builds up from there. If you can write a for loop in any language and read Big-O notation like O(N), you have everything you need.

There are around 31 lessons and 37 problems across 8 patterns plus a design capstone. Most learners finish the lessons in 10 to 15 hours and need another 15 to 25 hours to solve the problems, depending on prior experience.

The patterns covered, two pointers, sliding window, simultaneous traversal, interval merging, maximum overlap, account for a large share of the array questions asked in real onsite loops. You will recognise the template on first read of the problem.

LeetCode shows you problems one at a time with no grouping. This course teaches the eight templates first, then drills problems within each template so you build pattern recognition instead of memorising 200 individual solutions.

Random access by index is O(1) because the address is computed directly from base + i * element_size. Insertion or deletion at the end of a dynamic array is amortised O(1) thanks to capacity doubling, but a single resize is O(N). Insertion or deletion in the middle is O(N) because every element after the gap has to shift. Searching an unsorted array is O(N); on a sorted array it drops to O(log N) with binary search.

They are the same idea with different names. A C array is a fixed-size contiguous block. A C++ vector, Java ArrayList, JavaScript Array, Python list, and Go slice are dynamic arrays: contiguous storage that resizes itself by allocating a larger block and copying. Underneath, all five rely on the same memory layout this course teaches.

Yes. Once you finish all lessons and the design capstone, codeintuition issues a shareable certificate you can post to LinkedIn or attach to a job application.

Start Learning →

1. Array

2. Singly Linked List

3. Doubly Linked List

4. Hash Table

5. Stack

6. Queue

7. Binary Tree

8. Binary Search Tree

9. Heap

10. Graph

Array