Data structures are specialized formats for organizing, storing, and manipulating data on a computer. They provide a way to manage large amounts of data efficiently for uses such as large databases and internet indexing services. The choice of data structure often impacts the efficiency of an algorithm, both in terms of time and space complexity. Understanding the strengths and weaknesses of particular data structures helps optimize code and resource management.
Example: Let’s take the example of queue data structure:
We choose queue data structure when we need the result per the initial order of the elements stored (that follows FIFO), etc.
Classification of Data Structures
The Data Structure can be classified into two categories: -
1. Primitive Data Structure
These are the fundamental data structures that can be operated directly on data and machine instructions. These data structures are defined by programing language or we can sy it is built-in. The notations of these data structures vary from machine to machine.
Examples of primitive data structure:
Float
Integer
Character
Pointer
Double
2. Non-Primitive Data Structure
These data structures are derived from the primitive data structure. Their declaration depends entirely upon the programming languages. The non-primitive data structure emphasizes the structural grouping of homogenous or heterogeneous data items.
In Non-Primitive data structures, we have two categories:
Linear Data Structures:
These are the data structures in which elements are stored linearly or sequentially. Every element in the linear data structure is attached to its previous and next adjacent elements. In the linear data structure, a single level is involved, which helps in traversing all the elements in a single iteration. Let’s discuss some different types of linear data structures:
1. Arrays - An array is a collection of similar data types stored in a contiguous form in the memory. This data structure is one of the simplest ones to store data. In an array, we can also access data directly using their indexes.
Example: If we store the weights of 5 persons, then we don't need to define an individual variable for each person. We can define an array that will keep the weights data at contiguous memory locations.
2. Stack -A linear data structure that follows the principle of Last-In-First-Out(LIFO).
It has only one pointer, which points to the topmost element of the stack.
When new data is added, it is added at the top of the stack.
The deletion in the stack also follows the same criteria.
In short, a stack is a container where insertion and deletion occur from one end, i.e., the top of the stack.
3. Queue - A linear data structure that follows the principle of First-In-First-Out(FIFO).
In the queue, insertion occurs from the rear end, whereas deletion occurs from the front end.
Example: In a line for a railway ticket, people standing at the front side will get access first as they initially joined the queue.
4. Linked List - A linear data structure in which nodes are distributed randomly in the memory.
In nodes, two types of information are stored
First is the data element of that node.
The second is a pointer that contains the address of the next node in the memory.
Non-Linear Data Structures:
These are the data structures in which data elements are stored in a non-sequential manner. In non-linear data structures, a single level is not involved. Therefore, we can’t traverse all the elements in a single iteration. Some different types of non-linear data structures are discussed here:
1. Graph - It is a non-linear data structure that combines two objects, i.e., vertices and edges.
Edges are the arcs that join any two nodes in the graph.
Vertices are the nodes that store the data item.
The graph is a widely used data structure.
It has many applications like designing social networking websites(Facebook, LinkedIn), telephonic networks, Google Maps, etc.
2. Trees - It is a non-linear data structure representing the data hierarchically.
It is a collection of objects called nodes linked together to represent the data in a hierarchy.
In a tree data structure, the topmost node is the root node. The nodes at the bottom level are called leaf nodes.
Each node in a tree contains the data and a reference of the other nodes, i.e., children of that node.
Example: Let us see the hierarchy of a company.
The CEO of a company represents the topmost node, i.e., the root node. Childs of the root nodes represents the managers who report to the CEO. Further, the hierarchy will go on in the same way.
Difference between Linear and Non-linear Data Structures
Basis
Linear
Non-Linear
Data Storage Format
1- In the linear data structure, data storage occurs linearly.
1- In a non-linear data structure, data is placed non-linearly.
Relationship between the data
2- Every data item is linked with the data placed before and after them.
2- Data items are not linked since they are placed in a non-linear form.
Implementation
3- Easy to implement.
3- Difficult to implement.
Traversal
4- Can access one element at a time in a single iteration.
4- Can’t access all the elements in a single iteration because elements are placed in a non-sequential manner.
Examples
5- Examples: Array, stack, queue, etc.
5- Example: Graph, Trees, etc.
What is Data Type?
Data types are attributes that are predefined or can be created by the user. It tells the system how to interpret the value and ensures that data is present in the desired format, with each value in an expected form. In different programming languages, it is interpreted differently, such as
In Python, it is interpreted at runtime
In C++, C, and Java, it is interpreted at compile time
1. Float - The float data type is a 32-bit single precision data type. It stores decimal values or floating point values. The default value of the float data type is 0.0f. The range of float values is 1.17e-38 to 3.4e38.
2. Integer - It is a numeric data type that stores whole numbers without fractions. The default value for the integer data type is 0. The range of integer values is (-2^31) to (2^31-1).
3. Character - It is a data type that stores character data in a fixed length format, such as letters, digits, symbols, ASCII values, etc. The range of characters data type is -128 to 127 or, by default, 0 to 255.
4. Double - The double data type is a 64-bit double precision data type.It is a data type that stores fractional as well as whole values. The default value of the double data type is 0.0d. The range of double is 1.7e-308 to 1.7e308.
4. Boolean - It stores either positive or negative values in the form of True or False. The default value of the boolean data type is False.
5. Array - An array is a collection of similar data types stored in a contiguous form in the memory. This data structure is one of the simplest ones to store data. In an array, we can also access data directly using their indexes.
6. String - It is used to store characters in a sequential format, which forms a string. It also denotes a data structure or other sequential data types.
How to Choose a Data Structure
Sometimes, the selection of the right data structure is difficult for the problem solver. To choose the right data structure, one should have a proper understanding of data structures and all the algorithms associated with them. But some keywords or patterns are given in the problem, which can help the user choose the correct data structure. Below are some tips for choosing the right data structure:
Two Pointers/Sliding Window
If the sorted array is given or if there is a need to find the summation using two or three variables, in that case, we can think of two pointers.
A sliding window can be used if the contiguous part of an array is asked to find. Examples longest subarrays, most extended strings, max subarray sum, etc.
Hashing
If you have been given duplicates/frequency, then hashing is handy because we can store key-value pairs in better time complexity.
Stack/Queues
When you find problems like how many elements are greater/smaller than the given element, balancing of parentheses, etc.
We generally use queues in graph/tree-related problems such as BFS. One critical use case of this data structure is LRU Cache.
Binary Search
Binary search is an algorithm best applied when finding the target value present in the given data structure. This target value can sometimes be an optimized value, i.e., min/max value.
Heap
You should immediately think of heap if you asked for top/least k items to find or remove.
Tree or Graph
Some keywords related to graph problems are nodes, edges, connections, paths, cycles, direction, etc. Most tree or graph problems can be solved using DFS Algorithm & BFS algorithms.
Recursion/Backtracking
The use case of recursion is when we have to explore all possible possibilities in a given problem and find an optimal solution.
Backtracking is also similar to recursion. The only difference is we have to backtrack to find the optimal solution.
Dynamic Programming
When we need an optimized result after exploring all the cases in the best possible time complexity, DP is an extended or optimized recursion version. You must be familiar with the recursion concepts before using DP.
Why Data Structure?
As the amount of data increases day by day, the application becomes more complex, which causes problems in searching and handling multiple requests. To address these problems, we need data structures because they provide a way to manage, organize, and store data efficiently.
Data Structure manages the storing and retrieving data information stored in primary and secondary memory.
Data Structure helps in the efficient searching and retrieval of data.
Data Structure allows the management of large amounts of data, such as large databases, and indexing services, such as hash tables.
Different data structures allow data/information to be stored in a specific manner in the memory.
Data Structures are essential for all the efficient algorithms used to store the data in the desired data structure.
They are used in every program or software system to arrange the data.
Some of the characteristics of Data structure are given below:
1. Static and Dynamic -
In static data structures, the size of the structure is fixed. It can be modified during the compile time but not in runtime. Example - array.
InDynamic data structure, the structural size is not fixed. It can be changed as per the data requirement. Example - linked list, trees, heaps, etc.,
2. Time Complexity -
It is the number of operations an algorithm performs to complete its task. The less running time, the more accurate the device will be.
3. Space Complexity -
The space the program occupies to complete its task implies how much memory a program consumes during its execution.
4. Correctness -
This property is related to the implementation of the algorithms, that is how it is executed so that it can produce the correct output.
Applications of Data Structures
1. Array
It is useful when it is required to multiply data of two databases having different dimensions having the elements in numerical form, i.e., matrix multiplication.
Helpful in implementing other data structures such as stacks, queues, lists, and heaps.
2. Stack
It helps to reverse strings, mathematical expressions, and the traversal of graphs and trees.
Helpful in solving programming problems like recursion and backtracking.
3. Queue
Used in resource-sharing algorithms such as CPU scheduling, disk scheduling, etc.
Helpful in ticket booking websites where a booking is to be done on a first-come-first-served basis.
4. Linked Lists
Used in dynamic memory allocation.
Navigation systems.
Undo and Redo operations used doubly linked lists as the underlying operation.
5. Graph
Social media websites.
Used to find the shortest path to build an efficient network.
Molecular structure representation.
6. Trees
Searching elements in a set, a binary search tree is used.
The shortest path trees are used in routers.
Major Operations to Perform with Data Structure.
Some primary operations you can perform on data structures are
Sorting- It implies the storage of data elements in a particular order, whether it can be increasing or decreasing. We have various sorting algorithms such as quick sort, selection sort, heap sort, bubble sort, merge sort, etc.
Searching is exploring a piece of information in the data structures. When a target value is discovered, it is termed a success. It can be performed on data structures such as arrays, linked lists, trees, etc.
Insertion- The insertion operation is performed by adding one or more data elements to the data structure as per the requirement.
Deletion- The deletion operation removes an existing data structure element and reorganizes the data structure.
Updation- It refers to updating or modifying an existing data structure element at a specified index with another element.
Key to Learn Data Structure
Strengthen Your Fundamentals
The foremost requirement for mastering data structures and algorithms is a solid foundation of the fundamental concepts of Mathematics, Computer Architecture, and of course Data Structures.
Maths – A concrete mathematical base is essential to learn algorithms. Try to cover Math concepts from different areas such as set theory, regular expressions, linear equations, finite-state machines, matrix multiplication.
Primary Data Structures – Strengthen your knowledge about the primary data structure concepts like arrays, hash tables, graphs, stacks, queues, heaps, linked lists, and binary trees.
Computer Architecture – To be able to work with data structures and algorithms you need to have basic knowledge of computer arithmetic, boolean algebra, floating-point representation, cache design, and digital logic design.
Learn To Visualize Data Structures
To become a great coder, one must possess the ability to visualize different data structures. You need to picture how a data structure will look like, how you can implement it within your code, and also how it is organized in your computer ’s memory as well as in the abstract. To visualize data structures, you can pen down your thought pattern on paper.
Dive Into Algorithms
Once you’ve learned about the fundamental concepts of data structures, it’s time to leap into algorithms. An excellent way to start would be to learn Big-O notation as it would help you understand how to classify algorithms based on their running time and space requirements. Books like Introduction to Algorithms, and Algorithm Design Manual are great learning options.
As you learn more about algorithms, you should start implementing algorithms in your codes and learn about their running times in real-time. You could try implementing Euclid’s algorithm, Binary search, Binary tree traversals, Dijkstra’s shortest path, Min & max heaps, to name a few. Platforms such as HackerRank, LeetCode, CodeChef, and Coderbyte are excellent for practicing coding and sharpen your coding skills.
Learn Dynamic Programming
If you wish to master data structures and algorithms, dynamic programming is a must. It is a technique of solving complicated problems by breaking them down into smaller fragments of “subproblems.” These subproblems are then solved at once and their solutions are stored for future reference. Thus, in future if similar problems occur, you don’t need to solve it again from scratch; instead, you can refer to the previous solutions and save both time and effort.
Advantages of Data Structure
The use of data structure makes it easier to retrieve data from a storage device.
As data is stored in the data structure in an organized form, it helps the programmer to save a lot of time or processing time while performing tasks such as data storage and retrieval or processing.
The data structure allows for the effective and efficient processing of small and big data.
Provides usability and abstraction.
Manipulating a large amount of data can be done when a proper data structure technique is used.
Disadvantages of Data Structure
Many data structures are involved for storage when we work on a more extensive application. We need several professionals to create and maintain the application, which eventually increases the cost of maintenance of the data structure.
Designing a new data structure involves a lot of complex algorithms, time, and testing to conclude whether it is effective. Sometimes, this comes with a lot of costs, and developed data structures could be more effective.
An application using data structures requires highly qualified professional resources to manage the operations, which sometimes bears a lot of cost and effort.
Language Support
Some of the topmost languages that support data structure are:
Two months can provide a solid foundation in data structures and algorithms, especially with dedicated study and practice. End of the day, it depends on how advanced you want to learn it.
How much time does it take for a beginner to learn DSA?
For a beginner, mastering data structures and algorithms typically takes three to six months with consistent study and practice.
Is DSA hard for beginners?
DSA can be challenging for beginners due to its abstract concepts, but understanding improves significantly with guided learning and practical application.
Conclusion
This article discusses different aspects of a data structure, such as characteristics, classification, applications, advantages, disadvantages, etc. It gives you an idea of how a data structure can be chosen to solve a particular problem. We further discussed all the primary operations that can be performed on data structures. We discussed sorting, searching, insertion, deletion, and updation. It also makes you understand all the programming languages that support data structure operations, such as C, C++, Python, Java, etc. So, after reading this article, readers, you have a thorough understanding of data structures.
To learn more about Data Structures and Algorithms, you can enroll in our DSA in C++ Course.
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