One of the critical concepts for any developer is understanding data structures definition. In this article, we will go over some of the basics such as data structure definition, what is data structure, why does it exist in programming languages, advantage of data structure etc.
What is Data Structure?
Let’s come to what is data structure. Data structure is an organized collection of data. A data structure is a way of storing data in a computer so that it can be used efficiently. There are many kinds of data structures, but the most common are lists, which are sequences of items stored in order; arrays, which store items in an ordered sequence; trees, which store items in a hierarchical structure; graphs, which store information about connections between items; and files, which contain related data.
Now that we have an understanding of what is data structure and data structure definition, let’s look at an example. If you want to store information about a person's name and age, you might want to use two different variables: one for the person's name and one for their age. This is called an array. An array is a type of data structure because it stores multiple pieces of information together.
Why Data structure is Important?
What is data structures is clear, now here is why data structure is important – it is important because they allow us to organize data in a way that makes it easy for access and manipulation.
Following are the advantages of data structure:
- Organizing data in a way that makes it easy to access, modify, and store is a major advantage of data structure.
- It allows us to find and identify relationships between different pieces of data, being the next advantage of data structure.
- Data structures can be used to store a wide variety of information, including numbers, text, images, and more complex objects such as database records or programs.
Why should we learn Data Structures?
Data Structures is one of the most important topics that a programmer should know, after the advantage of data structure. It's the backbone of any application you develop and is used to store and retrieve information in memory. It helps you manage your memory efficiently so that you can create programs that run faster.
It's important to understand how data structures work because they're used everywhere in programming. You'll see them when you use an array, a stack or a queue. They are used in programming languages like C++, Java and Python. Check out the important data engineering interview questions to start your preparation today.
What are the Objectives of Data Structures?
After understanding what is data structure, here are main objectives of data structure.
- The first is to simply store the data. It's important that you can find that data again, even if it's been stored for a long time or you're looking for something that isn't super common.
- Another objective is to let you organize the data so it's easier to find what you're looking for. This includes grouping similar things together and making sure that the values are organized in a way that makes sense.
- Finally, there's also an objective of keeping the data secure and protecting it from unauthorized access. Data structures help keep your information safe by not letting anyone else access it without your permission. Learn all about to how become data science engineer and get a saucerful career option.
Key Features of Data Structures
In this guide of what is data structure, lets explore the key features of data structures are:
- A data structure is a collection of data that is organized in a particular way.
- Data structures provide an abstraction from the details of how the data is stored, allowing the programmer to focus on the desired functionality rather than on how it is implemented.
- Data structures provide a way to manage large amounts of information efficiently, by using system resources more efficiently, often through the use of caching, and typically providing faster access than arrays or other direct representations of their elements would allow. Check out the scope of data engineering in India.
Classification of Data Structure
The classification of data structure is a way to classify the different types of data structures. The primitive and non-primitive are two such classifications.
Primitive data types store their values directly in memory locations; for example, integers are stored as binary numbers, and floating-point numbers are stored as their binary representation with an exponent field.
Non-primitive data types have to be allocated from memory and initialized before they can be used. Primitive data types do not need to be allocated before use because they will be automatically allocated when necessary, by the compiler or interpreter. A non-primitive type includes arrays, records, pointers, etc., which store information about the object they reference rather than just containing its value directly as with primitives like integers or floats.
Major Types of Data Structures
Every programmer knows what is data structure but you must know the most common ones that you'll encounter as a programmer:
Arrays have fixed size and elements are accessed by their index. Arrays can be one-dimensional or multi-dimensional.
Linked lists are a sequence of nodes that can be accessed in any order. Each node contains data and a pointer to the next node in the list.
A stack is a last in first out (LIFO) structure that stores items in an ordered sequence. Stacks are often used for storing local variables and for building recursion trees.
A queue is a first in first out (FIFO) structure that stores items in an ordered sequence. Queues are often used for storing messages from one thread to another and for implementing priority queues.
Get a detail list of types of data structure you must know.
Primitive Data Structure
A primitive data structure is a basic data structure that directly represents a particular object, such as an integer, a floating-point number or a string of characters. The term "primitive" comes from the fact that these structures can only be built up by combining other primitive data structures.
Features of Primitive Data Structure
There are many features of primitive data structures.
- First, they are simple. They do not have a lot of features. They only have the ones that are needed to do their job. This makes them easier to understand and learn, as well as easier to implement in practice.
- Second, they are fast. Primitive data structures are designed to be small and efficient, which means that they can be used in many different ways without slowing down the software or causing problems with memory usage.
- Third, they are flexible. Primitive data structures can be re-used in different contexts and situations without having to worry about whether or not they will work correctly or efficiently.
Non-Primitive Data Structure
A non-primitive data structure is a data structure that consists of more than one primitive. It's made up of two or more primitive data types, and it's not just a collection of those primitives.
Features of Non Primitive Data Structure
The key features of non-primitive data structure include:
- They can be made up of other objects and data types.
- They are used to hold complex data sets that don’t fit into simple variables or primitive data types.
- They allow for easy manipulation and access to their components, which may be stored as separate values or as part of a larger object.
Major Operations of Data Structure
The next step in this guide of understanding what is data structure, we will now explore the major operations of data structure. The data structure determines how the data is organized and managed by the computer. The most important data structure operations that can be performed are:
It is one of the most basic data structure operations. Searching is the process of finding an element with a certain key in a data structure. It involves traversing the elements of the data structure and checking for the presence of a key that matches the one we are looking for.
Sorting is the process of rearranging elements in such a way that their keys are in ascending order (or descending order, depending on our needs). The data structure operations depends on whether or not we need to maintain the order of keys once they have been sorted.
Insertion is the process of adding an element to an existing data structure in such a way that it retains its ordering with respect to other elements in the structure. If our data structure does not allow duplicate keys, then we can simply add our new value into its place. However, if there are duplicate keys allowed, we must find an appropriate location within our existing structure to insert it into so as not to disrupt any relationships between other elements in that structure.
Updation operation of data structure is similar to insertion except that instead of adding an element into an existing data structure, it involves modifying an existing element with a new key value and/or updating some other value associated with it (such as its position within its parent node).
Deletion is a type of data structure operation that refers to the deletion of a particular element from a given data structure. The element may be deleted from its current position or from any other position in the data structure depending on how the deletion is performed. In general, there are two types of deletion that are commonly used: single-linked list and double-linked list.
Basic Functions/Operations of Data Structures
The basic function and operations of data structures include accessing, inserting, deleting and updating. The updating operation is the most important one in this category as it is used to modify an already existing data item. It can be done either by replacing a value or changing the value of certain fields in an existing structure.
The accessing operation helps us get information back from a data structure by reading it out. This is done through indexing or enumeration methods that let you find specific items in a collection or list.
Insertion means adding new items to the collection while deletion refers to removing old ones from the collection. There are two main types of insertion: overwriting (replacing) and appending (adding). Appending new items after others may require additional space for them, which means that you have to create room for them before you can add any new elements.
Some Applications of Data Structures
Data structures are everywhere, and their applications are endless. Now that we know what data structure is, we will look into some key applications of data structure.
Data structures are the tools that allow you to store, retrieve and manipulate data in a computer program. They can be used to solve many problems and can be applied to a wide variety of fields. In this article, we will look at some specific examples of how data structures are used in practice.
- Database Systems: Databases are a type of application that uses data structures for storing information about people or things. They typically store data in tables that have rows (records) and columns (fields). The rows contain information about one person or thing while the columns contain different attributes such as name, age etc. Each record is associated with a unique key which allows us to find it easily when we search by name or ID etc.
- Computer Graphics: Computer graphics use data structures to store images so they can be manipulated easily before being displayed on-screen or printed out onto paper etc... The most common type of image file used today (JPEG) stores color information using RGB colour models where each colour channel has its own number range between 0-255 intensities; these intensity ranges are stored separately inside each pixel's data structure so that when we draw them onto screen.
- Biology: Data structures are also used by biologists when they study genomes and genomes sequences. For example, biologists use trees to represent DNA sequences or protein families.
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