Data Structures Demystified: Simplifying Assignments with Expert Guidance

Electronic technology has surpassed our wildest imaginations. We now have systems that can process gigantic volumes of information of different kinds & thus understand natural human language, aid in decision-making, predict human behavior, market trends, the weather, and so much more. Data processing lies at the center, with central processing units (& even graphics processing units) executing different instructions on myriad data types.

So, how can a machine that takes electrical signals as input process and manipulate so many different kinds of data? How is data stored for real-time or subsequent processing? Well, data structures make it all possible! Alongside algorithms, data structures are fundamental aspects of computer science & engineering. If you are working on a data structure assignment & need some urgent recap of key concepts, principles, & types, then give this write-up a thorough read. It comes to you straight from professional data structure assignment help experts.

Let’s get started. 

 What are Data Structures?

Data structures are intangible constructs that store data and support the application of different algorithms/operations on data. Building blocks of any computer program and software application: if you need some computer science assignment help with data structures, connect with our experts at AssignmentHelp.us.  

As you may already know, data structures are a way to store data. They can also be considered to be representations of data, with each different kind representing data in a specific format. Different data structures also support different kinds of operations/algorithms, with the nature of representation affecting the kind of operations supported.

• All the different kinds of operations a data structure supports are known as an interface. 
• Interfaces specify the kind of operations and the kind of problems a data structure can be used for. 
• Data structures are akin to sets in mathematics. However, unlike mathematical sets, sets in computer science can grow, shrink, or have their elements replaced with algorithms.  
• Two key interfaces that all data structures should be able to support are sets and sequences. Sequences store information concerning some extrinsic order, while data in a set possess some intrinsic order.
• The information stored in any data structure is generally non-constant. 
• Different data structures can implement the same interface (operation) with varying performance levels. 

Now, look at some of the most commonly used data structures in computer science & software engineering.

Types of Data Structures & Their Features 

Data structures can be categorized primarily into two major classes 🡪 primitive & non-primitive. However, data structures can also be classified as static or dynamic, depending on whether they can grow or shrink. 

All data structures can be subdivided further, whether linear/non-linear or static/dynamic. 

PRIMITIVE DATA STRUCTURES

• Integer
• Real 
• Character
• Boolean
• Float

NON-PRIMITIVE DATA STRUCTURES

1. LINEAR

• Arrays 

They are linear & static mechanisms for storing/representing data, where everything is stored in adjacent locations. Elements can be ordered or unordered in an array, which can only store information of a particular data type.

While arrays are generally static, you can also have dynamic arrays.

• Stacks & Queues 

Stacks and queues are dynamic sets/data structures. Stacks follow the LIFO operation, where the last element entering the stack is the first to get out. Queues follow the FIFO rule, where the first element to go in is also the first element to get out. Like arrays, data is stored sequentially in both stacks & queues.

Both stacks and queues have multiple variants. You have circular-linked lists, double-linked lists, double-ended queues, circular queues, etc.

• Linked Lists

Linked lists are similar to stacks and queues as they store data linearly. However, unlike stacks & queues, linked lists do not store data sequentially. The order of storage depends on the value of the pointers associated with each data point.

2. NON-LINEAR

• Trees

Trees are non-linear and hierarchical data structures. They consist of nodes pointing to one or multiple other nodes containing further information. All trees have a single root node and can be subdivided into multiple sub-nodes.

Different kinds of trees are used across different kinds of applications. There are binary trees, ternary trees, N-ary trees, binary search trees, AVL trees, red-black trees, B-trees, B+ trees, segment trees, etc.

• Graphs 

Graphs are another major category of non-linear data structures. Nodes in a graph are known as vertices that store information, while the edges that connect the vertices are called edges that often denote the relationship between the information stored in the vertices.

Unlike trees, which restrict the number of nodes it can connect to, nodes in a graph can connect to any number of nodes. Graphs are some of the most heavily implemented data structures in computer science.

Let’s wrap things up with some tips on using data structures and algorithms.

Tips on implementing Data Structures & Algorithms 

• If you have a sorted array, then use the binary search algorithm. 
• For trees, master breadth-first and depth-first traversals. 
• Use linked lists if data is expected to grow dynamically, and you do not need to access random elements in a list. 
• Recursions are best used in tandem with stacks. 
• If you need an ordered sequence, then use a queue.