The path to becom­ing a pro­fi­cient and suc­cess­ful pro­gram­mer is dif­fi­cult, but one that is cer­tain­ly achiev­able. Data struc­tures are a core com­po­nent that every pro­gram­ming stu­dent must mas­ter, and chances are you may have already learned or worked with some basic data struc­tures such as arrays or lists.

Inter­view­ers tend to pre­fer ask­ing ques­tions relat­ed to data struc­tures, so if you’re prepar­ing for a job inter­view, you’re going to need to brush up on your data struc­tures knowl­edge. Read on as we list the most impor­tant data struc­tures for pro­gram­mers and job inter­views.

1. Linked List

Linked lists are one of the most basic data struc­tures and often the start­ing point for stu­dents in most data struc­tures cours­es. Linked lists are lin­ear data struc­tures that allow sequen­tial data access.

Ele­ments with­in the linked list are stored in indi­vid­ual nodes that are con­nect­ed (linked) using point­ers. You can think of a linked list as a chain of nodes con­nect­ed to each oth­er via dif­fer­ent point­ers.

Relat­ed: An Intro­duc­tion to Using Linked Lists in Java

Before we get into the specifics of the dif­fer­ent types of linked lists, it is cru­cial to under­stand the struc­ture and imple­men­ta­tion of the indi­vid­ual node. Each node in a linked list has at least one point­er (dou­bly linked list nodes have two point­ers) that con­nects it to the next node in the list and the data item itself.

Each linked list has a head and tail node. Sin­gle-linked list nodes only have one point­er that points to the next node in the chain. In addi­tion to the next point­er, dou­bly linked list nodes have anoth­er point­er that points to the pre­vi­ous node in the chain.

Inter­view ques­tions relat­ed to linked lists usu­al­ly revolve around the inser­tion, search­ing, or dele­tion of a spe­cif­ic ele­ment. Inser­tion in a linked list takes O(1) time, but dele­tion and search­ing can take O(n) time in the worst case. So linked lists are not ide­al.

2. Binary Tree

Bina­ry trees are the most pop­u­lar sub­set of the tree fam­i­ly data struc­ture; ele­ments in a bina­ry tree are arranged in a hier­ar­chy. Oth­er types of trees include AVL, red-black, B trees, etc. Nodes of the bina­ry tree con­tain the data ele­ment and two point­ers to each child node.

Each par­ent node in a bina­ry tree can have a max­i­mum of two child nodes, and each child node, in turn, can be a par­ent to two nodes.

Relat­ed: A Begin­ner’s Guide to Bina­ry Trees

A bina­ry search tree (BST) stores data in a sort­ed order, where ele­ments with a key-val­ue less than the par­ent node are stored on the left, and ele­ments with a key-val­ue greater than the par­ent node are stored on the right.

Bina­ry trees are com­mon­ly asked in inter­views, so if you’re get­ting ready for an inter­view, you should know how to flat­ten a bina­ry tree, look up a spe­cif­ic ele­ment, and more.

3. Hash Table

Hash tables or hash maps are a high­ly effi­cient data struc­ture that stores data in an array for­mat. Each data ele­ment is assigned a unique index val­ue in a hash table, which allows for effi­cient search­ing and dele­tion.

The process of assign­ing or map­ping keys in a hash map is called hash­ing. The more effi­cient the hash func­tion, the bet­ter the effi­cien­cy of the hash table itself.

Each hash table stores data ele­ments in a val­ue-index pair.

Where val­ue is the data to be stored, and index is the unique inte­ger used for map­ping the ele­ment into the table. Hash func­tions can be very com­plex or very sim­ple, depend­ing on the required effi­cien­cy of the hash table and how you will resolve col­li­sions.

Col­li­sions often arise when a hash func­tion pro­duces the same map­ping for dif­fer­ent ele­ments; hash map col­li­sions can be resolved in dif­fer­ent ways, using open address­ing or chain­ing.

Hash tables or hash maps have a vari­ety of dif­fer­ent appli­ca­tions, includ­ing cryp­tog­ra­phy. They are the first choice data struc­ture when inser­tion or search­ing in con­stant O(1) time is required.

4. Stacks

Stacks are one of the sim­pler data struc­tures and are pret­ty easy to mas­ter. A stack data struc­ture is essen­tial­ly any real-life stack (think of a stack of box­es or plates) and oper­ates in a LIFO (Last In First Out) man­ner.

Stacks’ LIFO prop­er­ty means the ele­ment you insert­ed last will be accessed first. You can­not access ele­ments below the top ele­ment in a stack with­out pop­ping the ele­ments above it.

Stacks have two pri­ma­ry operations—push and pop. Push is used to insert an ele­ment into the stack, and pop removes the top­most ele­ment from the stack.

They also have plen­ty of use­ful appli­ca­tions, so it is very com­mon for inter­view­ers to ask ques­tions relat­ed to stacks. Know­ing how to reverse a stack and eval­u­ate expres­sions is quite essen­tial.

5. Queues

Queues are sim­i­lar to stacks but oper­ate in a FIFO (First In First Out) man­ner, mean­ing you can access the ele­ments you insert­ed ear­li­er. The queue data struc­ture can be visu­al­ized as any real-life queue, where peo­ple are posi­tioned based on their order of arrival.

Inser­tion oper­a­tion of a queue is called enqueue, and deleting/removing an ele­ment from the begin­ning of the queue is referred to as dequeu­ing.

Relat­ed: A Begin­ner’s Guide to Under­stand­ing Queues and Pri­or­i­ty Queues

Pri­or­i­ty queues are an inte­gral appli­ca­tion of queues in many vital appli­ca­tions such as CPU sched­ul­ing. In a pri­or­i­ty queue, ele­ments are ordered accord­ing to their pri­or­i­ty rather than the order of arrival.

6. Heaps

Heaps are a type of bina­ry tree where nodes are arranged in ascend­ing or descend­ing order. In a Min Heap, the key val­ue of the par­ent is equal to or less than that of its chil­dren, and the root node con­tains the min­i­mum val­ue of the entire heap.

Sim­i­lar­ly, the root node of a Max Heap con­tains the max­i­mum key val­ue of the heap; you must retain the min/max heap prop­er­ty through­out the heap.

Relat­ed: Heaps vs. Stacks: What Sets Them Apart?

Heaps have plen­ty of appli­ca­tions due to their very effi­cient nature; pri­mar­i­ly, pri­or­i­ty queues are often imple­ment­ed through heaps. They are also a core com­po­nent in heap­sort algo­rithms.

Learn Data Structures

Data struc­tures can seem har­row­ing at first but devote enough time, and you’ll find them easy as pie.

They are a vital part of pro­gram­ming, and almost every project will require you to use them. Know­ing what data struc­ture is ide­al for a giv­en sce­nario is crit­i­cal.

Read Next

About The Author