01 · Start here

Data Structures Hub

From arrays to graphs—master how data is stored, linked, and traversed in code.

02 · Big picture

Classification of data structures

Data structures are often grouped by how elements are arranged and accessed. Linear structures organize data in a sequence: there is a clear first-to-last order, and you typically move one step at a time (arrays, linked lists, stacks, and queues). Non-linear structures break that single-line idea—elements can connect in hierarchies (trees) or arbitrary networks (graphs), which unlocks modeling for folders, syntax, road maps, social links, and more.

The diagram below summarizes this split. Your study path on this hub starts with linear building blocks (especially arrays and linked lists), then moves to stacks and queues, and finally to trees and graphs—matching how complexity and interview depth usually grow.

Classification of data structures — Linear vs non-linear overview: arrays, linked list, stack, queue, trees, and graphs. Drop your original infographic PNG at `nikhillearnhub/assets/images/data-structures-classification.png` to use it automatically; otherwise the SVG beside this page is shown.

Linear

Single logical sequence; often one predecessor or successor at a time.

Arrays — contiguous indices, O(1) access by position.
Linked list — nodes + pointers, flexible insert/delete.
Stack — LIFO (last in, first out).
Queue — FIFO (first in, first out).

Non-linear

Multiple connections; not one straight “line” of elements.

Trees — parent/child hierarchy (root, branches, leaves).
Graphs — vertices and edges; may contain cycles and many paths.

03 · Overview

Learning overview

Why this hub matters

Data structures are the vocabulary of efficient programs: the right structure makes operations fast, memory predictable, and interview explanations clear. This hub collects Nikhil Learn Hub’s C-language tracks for each major structure—aligned with the broader Problem Solving Hub and coding interview prep.

Each structure in the learning paths section below follows the same rhythm: notes first, then tricks, MCQs, short interview answers, small programs, and focused problem-solving sheets—so you can study one topic end-to-end without hunting across the site.

Linear structures — Arrays are the foundation for indexing, sliding windows, and two-pointer patterns; stacks and queues model LIFO/FIFO behavior for parsing, BFS, and scheduling.
Linked lists — Dynamic size and pointer (or reference) logic; essential for understanding insertion complexity and many interview “reverse / merge / cycle” problems.
Trees & graphs — Hierarchies (BST, traversals) and networks (adjacency lists, BFS/DFS) unlock a large share of medium and hard interview questions.

Pair reading with whiteboard traces and small C programs—structures stick when you draw memory and step the pointers yourself.

04 · Practice

Structure your practice • Build interview fluency

Read how each structure fits your study plan below, or open the full problem-solving hub for algorithms and mixed practice.

Problem Solving Hub

05 · Topics

Data structure learning paths

Below is a suggested journey through the core structures we cover in C. Each topic uses the same study pattern: read the notes, skim tricks, then work MCQs, short interview answers, programs, and problem-solving sheets. Links open the notes page first; related pages sit in the same folder under Problem Solving.

Suggested order: arrays → linked list → stack → queue → tree → graph. Your syllabus or interview prep might reorder topics (for example, trees before linked lists)—everything still connects through the Problem Solving Hub.

Arrays

Arrays are fixed or managed contiguous blocks: O(1) index access, but insertion and deletion in the middle are costly unless you use patterns such as two pointers, sliding windows, or prefix sums. They are the default representation for vectors, matrices, and many dynamic-programming tables. Start with the conceptual notes, then drill complexity and edge cases (empty array, single element, overflow) before timed practice.

Arrays notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLIKE HERE FOR ADVANCED TUTORIAL

Linked list

Linked lists trade random access for flexible insert/delete at known nodes—ideal for understanding pointers, sentinel nodes, and cycle detection. Interviews love reversal, merge of sorted lists, and fast/slow pointer tricks. Draw every transformation on paper before coding; most bugs come from losing a next reference or mishandling the head pointer.

Full tutorial (sidebar) · Linked list notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLICK HERE FOR ADVANCED TUTORIAL

Stack

A stack is last-in, first-out (LIFO): think function call stacks, undo buffers, and matching parentheses. It pairs naturally with depth-first ideas and many “parse this expression” problems. Implementations in C typically use arrays with a top index or linked nodes; compare space and overflow behavior for both.

Full tutorial (sidebar) · Introduction to Stack · Stack notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLIKE HERE FOR ADVANCED TUTORIAL

Queue

A queue is first-in, first-out (FIFO): scheduling, buffering, and especially breadth-first search (BFS) on trees and graphs. Circular array queues avoid shifting elements; linked queues avoid capacity limits. Understanding enqueue/dequeue invariants helps when you layer BFS on grids or social graphs in interviews.

Queue notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLIKE HERE FOR ADVANCED TUTORIAL

Tree

Trees generalize linked lists into hierarchies: binary trees, BSTs, height balance, and traversals (preorder, inorder, postorder, level order). Many problems reduce to recursion with a clear base case or to iterative stacks/queues mirroring those traversals. Master one traversal deeply—then map others as reorderings of the same visit pattern.

Tree notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLIKE HERE FOR ADVANCED TUTORIAL

Graph

Graphs connect arbitrary vertices with edges: use adjacency lists for sparse networks (typical in interviews) and matrices when density or weights are fixed. DFS (often stack-based) explores deeply; BFS (queue) finds shortest path in unweighted graphs. Practice translating a problem statement into vertices, edges, and visited state— that modeling step is half the battle.

Graph notes · Tricks · MCQ · Interview Qs · Programs · Problem solving

CLIKE HERE FOR ADVANCED TUTORIAL

Beyond these structures

Strings, search and sort, greedy algorithms, dynamic programming, backtracking, bit tricks, and pattern matching live in the broader Problem Solving Hub—alongside the same C notes style. Use that hub when you need mixed-topic revision or contest-style variety. The Technology Hub links out to programming languages, AI, databases, and roadmaps for full-stack context.

CLIKE HERE FOR ADVANCED TUTORIAL

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