Do you know why the IT giants like Facebook, Google, and Amazon employ programmers who are exceptional at algorithm optimization?

 From improving the scalability to optimizing memory usage, algorithms and data structures help in every aspect of the business. 

Both fresh computer science graduates and seasoned IT learners struggle with the concept of data structures algorithm. So, if you want to crack the interviews at important companies, you must develop the skill of mastering algorithms and data structures. 

Companies like Google, Twitter, Facebook, etc. need continuous optimization of their apps to ensure that it is relevant to the needs and requirements of their users. This is why they hire applicants who can use data structure and algorithms optimally. 

Now that you know the importance of learning algorithms and data structures, below is a list of the 7 most important algorithms and data structures every programmer must know about. Take a look!

7 Critical Data Structures and Algorithms to Master 

1. Arrays 

Arrays are fixed-size data structures that are designed to hold the same category of data elements. These data elements can be integers, point numbers, or even strings. You can also randomly access the arrays because they're indexed. 

They're used by programmers when they need to categorize a large number of data elements. As a programmer, you need to define the number of data elements and their types. This is known as a single-dimension array. 

Arrays have several advantages such as - they save memory, they're cache friendly, having access time available, having compact memory usage, etc. 

There are a set of operations that a programmer can perform on arrays: 

• Traverse: Skim through the data elements and then print them. 
• Search: Search for a particular data element in an array using its value or index. 
• Update: The value of an existing data element can be updated.

Applications of arrays include: 

• Helpful as building blocks of successive data structures like hash tables, matrices, etc. 
• Helpful in sorting the algorithms such as merge sort, insertion sort, bubble sort, etc. 

2. Linked Lists 

Linked lists are a type of sequential data structure that are made of a series of data elements arranged in a linear order. Therefore, you have to access the data elements in a linked list sequentially. It is not possible for programmers to randomly access the data in a linked list. 

A linked list is composed of certain critical components. Every data element in a linked list is known as a node. Every node has a key and a pointer called next that points to the successive node. 

There’s an attribute named head that points to the first data element in the linked list. And the last data element in the linked list is called the tail. 

There are various types of linked lists that you must pay special attention to before your technical interview. These include: 

• Singly Linked List: You can only traverse the data elements in a forward direction in this linked list. 
• Doubly Linked List: Both forward and backward directions can be used to traverse the data elements in this linked list. It contains an additional data pointer called prev. This element points to the previous node. 
• Circular Linked List: In this linked list, the pointer prev of the head points in the direction of the tail and the pointer named next of the tail points in the direction of the head. 

3. Queues 

Queues are a data structure that functions just like a real-life queue. It is based on the First In First Out (FIFO) analogy. This means that the data element placed at the front of the queue will be the first one to be accessed by the programmer. 

Therefore, all the additions to the list will be made at one end of the queue and the deletion will be done from the other end. 

The first data element that is added to the queue is known as the front of the queue. Whereas, the last data element addition in the queue is called the tail.

The main characteristics of a queue are - it can be accessed from both ends, it can handle multiple elements of data, and it is a flexible data structure. 

4. Hash Tables 

Hash tables are data structures that store data elements in an array format. Every data element in a hash table has a unique index value assigned to it. 

You can easily access the data element that you desire by knowing its index. Therefore, it is very easy and fast for a programmer to insert data elements and search operations in a hash table irrespective of the data size. 

Hash Tables have the following programming applications: 

• Used to execute database indexes. 
• Used to execute associative arrays.
• Used to execute set data structures. 

5. Trees 

No technical interview is complete without a question based on trees. As the name suggests, a tree is a data structure wherein the data elements are organised hierarchically and then linked together. This is in contrast to the linked list where the data elements are arranged in a linear order. 

The applications of trees that every programmer must know about: 

• Binary Trees: They are used to execute expression solvers and expression parsers. 
• Heaps: They’re used to store Java objects by Java Virtual Machine.
• Treaps: They’re employed in wireless networking. 

6. Dynamic Programming 

In a technical interview, you’ll often encounter problems that are too difficult to solve in a single attempt. In such cases, you’ll have to break the problem into simpler subproblems. With the help of dynamic programming, you’ll first solve each subproblem, remember its solution, and then use it to move forward. 

There are several applications of dynamic programming. These include - longest common subsequence, bioinformatics, finding the shortest path, etc. 

7. Sort Algorithm 

The last one in our list of data structures and algorithms is the sort algorithm. It is one of the most exhaustively studied topics in computer science. The sort algorithm helps arrange a list of data elements in a particular order. 

Depending upon the requirement of a program, you can use the following subtypes of sort algorithm: 

• Merge Sort
• Bucket Sort
• Quick Sort
• Heap Sort
• Counting Sort 

Conclusion 

Being able to solve complex data structures and algorithms can help set the pace for your career advancements. You can also go through the concept of react native basics as a programmer which is of great help in building applications.

So, if you want to be the first hiring choice for every major IT company, you must master the above 7 algorithms and data structures!