5 Data structures you must know to pass any coding interview

Coding is a vast concept. Yet, the one major element of success lies in mastering the fundamentals, the data structures. Now, it is not just about theoretical memorization; you need to have a full grasp of its strengths, trade-offs, and when to apply them.

Hence, we have curated this beginner-friendly guide, breaking down the five essential data structures that every candidate must know, supported with clear explanations, common operations, and practical use cases to practice each one.

Top 5 Data Structures You Need to Know to Pass Any Coding Interview

1. Arrays and Strings

Let’s try to understand this in simple, plain language, alright? 

You may think of an array as a row of lockers, where each separate locker holds an item with a unique number (its index), allowing for direct access. Amongst all the data structures, arrays are the simplest and are mostly used to store elements in contiguous memory locations. A string is simply an array of characters, which makes nearly all array concepts directly applicable.

Common operations:

• Access: Ultrafast access to any element by its index (i.e., O(1) time complexity).
• Search: Find an element by scanning through the array (e.g., O(n) time for linear search).
• Insertion/Deletion: A rather costly operation because adding or removing an element from anywhere but the end requires shifting all subsequent elements to maintain contiguity (O(n) time).

When to use it:

Use arrays when you need –

• Fast, random access to elements by their position.
• To determine the size of your data collection in advance.
• To solve iterative problems, sort algorithms can be applied as the backbone for more complex structures.

Now, solving coding challenges is a core skill that many interviewers often look for. To be efficient in this, a hands-on practice with array manipulation through AlgoCademy’s code skill module is a must.

2. Linked Lists – The Sequential Connector

A linked list is primarily a sequence of “nodes,” with each node containing data and a pointer (or reference) to the next node in the sequence. In contrast to arrays, the elements here can be scattered anywhere as they are not stored in contiguous memory.

Common operations:

• Insertion/Deletion: This is where linked lists simplify problem-solving. Adding or removing a node from the beginning or middle (any position) of the list is a constant-time operation (O(1)), as it only requires updating a few pointers. There’s no need to shift any element.
• Access/Search: To search an element, you must start at the head and pass over each node sequentially until you find it (O(n) time).

When to use it: 

A linked list should be applied when you need frequent insertions and deletions from the beginning or middle of a list, where random access is not a priority. It sets the groundwork for stacks, queues, and graphs, especially when solving problems that involve efficient reordering or merging of lists.

3. Trees and Graphs: Modeling relationships

Trees and graphs, as the terms are obvious enough, represent hierarchical or networked relationships. To be more precise, a tree is a type of graph that is connected but has no cycles.

• Trees have a single root node, parent-child relationships, and leaf nodes (nodes with no children). Its most common type is the Binary Search Tree (BST), where each node has at most two children, with values organized for efficient searching (left child < parent < right child).
• Graphs, as a whole, are a collection of nodes (vertices) connected by edges, with no particular parent-child rules. They can be either directed or undirected, and even have cycles. This makes them perfect for building modeling networks, like social media connections or maps.

Common operations:

• Search/Traversal: The key operations include traversing the structure to visit all nodes, using the Depth-First Search (DFS) and Breadth-First Search (BFS) algorithms.
• Insertion/Deletion: Adding or removing nodes while maintaining the structure’s properties (e.g., BST ordering).

When to use it:

• Use trees when you need to represent hierarchical data like file systems or for tasks that require efficient searching, insertion, and deletion (i.e., O(log n) in a balanced BST).
• Use graphs to resolve classic coding challenges, involving networks, connections, or shortest-path calculations.

4. Hash Maps (Hash Tables)

A hash map stores data in key-value pairs. It uses a hash function to calculate an index (a “hash”) from the key to determine the position of the value stored in an underlying array. This process allows for super-fast data retrieval.

Common operations:

• Insertion (Put), Deletion (Remove), Access (Get): In general, these operations are performed in constant time, O(1).
• Search: Checking if an existing key is also an average O(1) operation.

When to use it:

Hash maps are the best framework for storing, accessing, and updating data quickly based on a unique key. They are ideal data structures for solving problems like caching, frequency counting (e.g., “find the first non-repeating character”), and storing relationships between objects.

If you mastered hash maps, consider that you tackled one of the biggest leaps in solving interview problems efficiently. You just need to practice consistently to reach that point.

5. Stacks and Queues

These essential data structures define a specific order for adding and removing elements.

• Stack follows the Last-In, First-Out (LIFO) principle, like a stack of plates. You can only add (push) and remove (pop) from the top.
• The queue follows the First-In, First-Out (FIFO) principle, like a line at a grocery store. You add (enqueue) to the back and remove (dequeue) from the front.

Common operations:

• Push/Pop (Stack) and Enqueue/Dequeue (Queue): Each of them is an O(1) operation, provided they are implemented with a linked list.

When to use it: 

• Use a stack to solve problems that involve reversal, recursion, or undo functionality (e.g., parsing expressions, backtracking algorithms).
• Use a queue for tasks that require processing in the exact order they were received, such as handling requests, level-order tree traversal (BFS), or caches.

Since they are conceptually simple to understand, they are not only beginner-friendly but also quite common in interviews.

Therefore, mastering these five essential data structures is the first and most critical step for coding interview prep. Beyond that, there’s only relentless practice. Don’t just look over; implement them from scratch to solve real problems.