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Rohit Sharma

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Critical Analyst / Storytelling Expert / Narrative Designer

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Head of Revenue & Programs at upGrad

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M.Tech., IIT Delhi

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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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4 Types of Trees in Data Structures Explained: Properties & Applications
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4 Types of Trees in Data Structures Explained: Properties & Applications

In this article, you will learn about the Types of Trees in Data Structures with examples, Properties & Applications. In my journey with data structures, I’ve encountered a fundamental concept: the various types of trees in Data Structures. Understanding these tree variations is essential for anyone in computer science and data analysis.  When diving into Data Structures, it’s important to grasp the different tree types. Each type has its own unique features and uses, catering to specific tasks and applications.  In this article, I’ll break down the various types of trees in Data Structures. By explaining their characteristics and real-world uses, I aim to give you the knowledge you need to make informed decisions in your projects and analyses. Let’s explore the world of trees in Data Structures together.  Take a glimpse at the types of Trees in the Data Structure. Binary tree Binary Search Tree AVL Tree B-Tree Read the entire article to learn in detail. What is a Tree in Data Structure? A tree is a type of data structure representing hierarchical data. It has a non-linear structure consisting of nodes connected by edges. Among the other types of data structures that perform operations in a linear data structure, the complexwebinarity increases with an increase in data size. However, the tree data structure provides quicker access to the data which is non-linear. Availability of the various types of data structures and the algorithms associated with them, the task performance has become an easy and efficient way. Check out our free data science courses to get an edge over the competition. A tree structure in DBMS organizes data hierarchically, with a single root node connected to child nodes, facilitating efficient data retrieval and management. The difference between the computing field tree and the real-world tree is that it is visualized as upside down with roots at the top and leaves at the bottom. In real-world applications, the tree data structure helps to demonstrate relationships between various nodes with the parent-child hierarchy. It is alternatively known as a hierarchic data structure. It is highly popular for streamlining and accelerating sorting and searching tasks. It is one of the resilient and most innovative data structures. It represents the non-linear data structure. It can also represent various primitive or user-defined data types. You can use classes connected lists, arrays, or other types of data structures to employ the tree. Its structure shows a group of interconnected nodes. These nodes are connected to the edges to depict the relationship. After understanding what is tree in data structure, let’s get familiar with some of its key terminologies. A few terminologies associated with trees in data structure are: Node: the node is an entity in a tree data structure that contains a key or a value and pointers to its child nodes.  Child node: A child node is the descendant of any node. Leaf nodes: The nodes which don’t have any child nodes and are the bottom-most node in a tree. They are also called the external nodes. Parent node: This node is located directly above a node. It is the principal in the hierarchy. For example, 50 is the parent node of 60, 70, and 80. Root: It is the topmost point of a tree. Internal node: The node having at least one child node. Edge: An edge refers to the connection between any two nodes in a tree. Height of a node: Number of edges from the node to the deepest leaf. Depth of a node: Number of edges from the root to the node. The root node’s depth is zero. Any other node’s depth is the number of edges it derives from the root node. Degree of a tree: It denotes the maximum of all the probable degrees of its nodes. Number of edges: If a tree has n nodes, the number of edges would be n-1. Excluding the root node, all other nodes in the tree possess a minimum of one incoming edge. Height of a tree: Height of the root node. It is the path length from a specific node to the extreme leaf node. It is always calculated from bottom to top. Hence, each leaf’s height is 0. Degree of a node: Total number of branches to that node. Forest: A collection of disjoint trees. Subtree: It denotes children in the tree. Every child is either a leaf node or a subtree. Generation: Nodes existing at the same level of the tree are called a generation. For instance, 60, 70, and 80 belong to the same generation. Ancestor: Suppose there are two nodes, A and B, and there is a path from A to B, then A is the ancestor of B. It is common terminology used in various trees in data structure. Descendent: It is the reverse of an ancestor. Sibling: Two nodes with the same parent are called siblings. Suppose 70, 80, and 90 are siblings and 100 is the parent. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. Subtree: It denotes children in the tree. Every child is either a leaf node or a subtree. Generation: Nodes existing at the same level of the tree are called a generation. For instance, 60, 70, and 80 belong to the same generation. Ancestor: Suppose there are two nodes, A and B, and there is a path from A to B, then A is the ancestor of B. It is common terminology used in various trees in data structure. Descendent: It is the reverse of an ancestor. Sibling: Two nodes with the same parent are called siblings. Suppose 70, 80, and 90 are siblings and 100 is the parent. Levels: The root node exists at level zero, and its children exist at level one. Level one’s children exist at level two. Types of tree Let’s dive right in to know how many types of trees in data structure: 1. Binary tree The binary tree is a type of tree data structure where every parent node has a maximum of two child nodes. As the name suggests, binary means two, therefore, each node can have 0, 1, or 2 nodes. A binary tree data structure is shown in Figure 1. Node 1 in the tree contains two pointers, one for each child node. Node 2 again has two pointers each for the two child nodes. Whereas node 3, 5, and 6 are leaf nodes and therefore, have null pointers on both left and right parts. This type of tree data structure allows a maximum of two children for each parent. The children are known as the right kid and the left kid. A binary tree in data structure is more popular than other trees. Applying several limitations and characteristics in a Binary tree, other tree structures like BST (Binary Search Tree), AVL tree, RBT tree, etc. are also used Figure 1:  A representation of a binary-tree.  Properties of a binary tree Maximum number of nodes at each level of I is 2 i. The height of the tree in Figure 1 is 3 which means that the maximum number of nodes will be (1+2+4+8) =15.  At height h, the maximum number of nodes possible is (20 + 21 + 22+….2h) = 2h+1 -1. At height h, the minimum number of nodes possible is equal to h+1. A minimum number of nodes will represent a tree with maximum height and vice versa. Must read: Excel online course free! Even the types of binary tree in data structure can be divided into the following: Full Binary tree: It is a special type of binary tree. In this tree data structure, every parent node or an internal node has either two children or no child nodes. Perfect binary tree: In this type of tree data structure, every internal node has exactly two child nodes and all the leaf nodes are at the same level. Complete binary tree: It resembles that of the full binary tree with a few differences. Every level is completely filled. Must read: Data structures and algorithm free! The leaf nodes lean towards the left of the tree. It is not a requirement for the last leaf node to have the right sibling, i.e. a complete binary tree doesn’t have to be a full binary tree. Degenerate or Pathological Tree: These trees have a single child either to the left or right of the parent node. Skewed binary tree: It is a pathological or degenerate tree where the tree is dominated by either the left nodes or the right nodes. Therefore, there are two types of skewed binary trees, i.e. left-skewed or the right-skewed binary tree. Balanced binary tree: The difference between the height of the left and right subtree for each node is either 0 or 1. Learn Data Science Courses online at upGrad Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses 2. Binary Search Tree These tree structures are non-linear and one node is connected to a number of nodes. The node can be connected to at most two child nodes. It is called a binary search tree because: Each node has a maximum of two child nodes. It can be used to search an element in 0(log(n)) time and hence known as a search tree Figure: An example of a Binary search tree (Example)  Properties of a binary search tree are: Value in all thenodes of a left subtree should be lesser than the value in the root node. Value in all the nodes of a right subtree should be larger than the value in the root node. Featured Program for you: Fullstack Development Bootcamp Course Binary Search Tree (BST) works as a binary tree extension with many optional restrictions. For example, a node’s left child value in BST must be less than or equal to the parent value.  A node’s right child must be greater than or equal to the parent’s value. It is one of those properties of tree in data structure that makes BST perfect for search operations. This is because it helps you to accurately determine whether the values exist at the right or left sub-tree.  Therefore, it is called a “Search Tree”. BST is best for the search operation because it helps you to decide where to move. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 3. AVL Tree The AVL trees are the types or variants of a binary tree. It consists of properties from both the binary as well as a binary search tree. Invented by Adelson Velsky Lindas, these trees are self-balancing which means the height of the left subtree and the right subtree is balanced. This balance is measured in terms of a balancing factor. Also read: Learn python online free! AVL tree is a self-balancing binary search tree. It is unique from other types of tree data structure because it was the first tree that was dynamically balanced. Each node in the AVL tree is assigned a balancing factor. This process depends on whether the tree is balanced or not.  The balancing factor is one of the prominent properties of the tree in data structure of the AVL Tree. The precise balance factor in the AVL tree is 1, 0, and -1. In case the tree got a new node, it would be rotated to ascertain that it is balanced. Certain common operations like insertion, viewing, and removal take O(log n) time in this tree. Usually, it is employed when working with the Lookups operations. Balancing factor:  It is the difference between the left subtree and the right subtree.  The value of a balancing factor must be 0, -1, or 1. Therefore, each node of an AVL tree should consist of a balancing factor value i.e. 0, -1, or 1. Balance Factor = (Height of Left Subtree – Height of Right Subtree)  An AVL tree is said to be a balanced tree if the balance factor of each node is between -1 to 1. Values of nodes other than -1, to 1 in an AVL tree will represent an unbalanced tree that needs to be balanced. If a node has a balance factor of 1, it means that the left subtree is one level higher than the right subtree. If a node has a balance factor of 0, it means that the height of the left subtree and the right subtree is equal. If a node has a balance factor of -1, it means that the right subtree is one level higher than the left subtree or the left subtree is one level lower than the right subtree. Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4   4. B-Tree B Tree is a more generalized form of a binary search tree. It is also known as the height-balanced m way tree, where m is the order of the tree. Each node of the tree can have more than one key and more than two child nodes. In the case of a binary tree, the leaf nodes might not be at the same level. However, in the case of a B Tree, all the leaf nodes should be at the same level.  Properties of a B-tree: Keys are stored in increasing order for each node x. A Boolean value “x.leaf” exists in each node, which is true if x is a leaf. The internal nodes should have at most “n-1” keys, where n is the order of the tree. It should also have a pointer for each child. All the nodes will have at most n children and at least n/2 children, except the root node. The leaf nodes of the tree have the same depth. The root node will have a minimum of 1 key and at least two children. If n ≥ 1, then for any n-key B-tree of height h and minimum degree t ≥ 2, h ≥ logt (n+1)/2. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Applications Binary Search tree can be applied for searching an element in a set of elements. Heap trees are used for heap sort. Modern routers use a type of tree called Tries for storing routing information. The B-Trees and the T-Trees are mostly used by popular databases to store their data. A syntax tree is used by compilers to validate the syntax of every written program. Conclusion In my experience. Data structures offer an organized approach to storing data, ensuring efficient management. They cater to various types of data, selected based on user requirements.  Types of trees in Data Structures represent hierarchical data storage, with nodes containing data and references to other nodes, known as children nodes.  These tree structures vary based on the number and arrangement of child nodes, each carrying associated properties. They find applications in sorting algorithms and data storage.  Considering my expertise, I highly recommend exploring an Executive PG Programme in Software Development – Specialisation in Full Stack Development, offered by upGrad & IIIT-Bangalore, which can enhance your profile and open doors to better job opportunities in programming. 

by Rohit Sharma

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31 May 2024

Searching in Data Structure: Different Search Methods Explained
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40500

Searching in Data Structure: Different Search Methods Explained

The communication network is expanding, and so the people are using the internet! Businesses are going digital for efficient management. The data generated on the internet is rising, and thus datasets are becoming complex. It is essential to organise, manage, access and analyse the data carefully and efficiently, a data structure is the most helpful technique, and the article focuses on the same! Data Structure In computer science, data structures are the basis for abstract data types (ADT), where ADT are the logical form of the data type. The physical layout of the data type is implemented using the data structure. Different data structure types are used for different kinds of applications; some are specialised in particular tasks.  The data structure is a collection of data values and relationships among them, operations and functions applicable to the data.  It assists in organising, managing and storing data in a particular format. Thus, users can have easy access and modify the data efficiently.  Data structures help to manage large amounts of data, such as massive databases. Efficient algorithms are built based on efficient data structures. Besides efficient storage, data structures are also responsible for the efficient retrieval of information from stored memory. It includes an array, Linked List, Pointer, Searching, Stack, Graph, Queue, Structure, Programs, Sorting and so forth. The article covers the concept of Searching in Data Structure with example and its methods. Two examples of algorithms are explained in detail to understand the concept clearly. To gain further knowledge, skills and expertise, online courses on data structure are available, mentioned at the end of the article.  What is Searching in Data Structure? The process of finding the desired information from the set of items stored in the form of elements in the computer memory is referred to as ‘searching in data structure’. These sets of items are in various forms, such as an array, tree, graph, or linked list. Another way of defining searching in the data structure is by locating the desired element of specific characteristics in a collection of items.  Our learners also read: Data structures and Algorithms free course! Searching Methods in Data Structures Searching in the data structure can be done by implementing searching algorithms to check for or retrieve an element from any form of stored data structure. These algorithms are categorised based on their type of search operation, such as: Sequential search The array or list of elements is traversed sequentially while checking every component of the set. For example, Linear Search. Interval Search Algorithms designed explicitly for searching in sorted data structures are included in the interval search. The efficiency of these algorithms is far better than linear search algorithms. For example, Binary Search, Logarithmic Search. These methods are examined based on the time taken by an algorithm to search an element matching the search item in the data collections and are given by, The best possible time The average time The worst-case time The primary concerns are regarding worst-case times that lead to guaranteed predictions of the algorithm’s performance and are also easy to calculate compared to average times.  upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 To illustrate examples and concepts in this article, ‘n’ items in the data collection in any data format are considered. Dominant operations are used to simplify analysis and algorithm comparison. For searching in a data structure, a comparison is a dominant operation, which is denoted by O() and pronounced as “big-Oh” or “Oh”.    Must read: Learn excel online free! There are numerous searching algorithms in a data structure such as linear search, binary search, interpolation search, jump search, exponential search, Fibonacci search, sublist search, the ubiquitous binary search, unbounded binary search, recursive function for substring search, and recursive program to search an element linearly in the given array. The article is restricted to linear and binary search algorithms and their working principles. Let’s get detailed insight into the linear search and binary search in the data structure. Linear Search The linear search algorithm searches all elements in the array sequentially. Its best execution time is one, whereas the worst execution time is n, where n is the total number of items in the search array. It is the most simple search algorithm in data structure and checks each item in the set of elements until it matches the search element until the end of data collection. When data is unsorted, a linear search algorithm is preferred. Linear search has some complexities as given below: Space Complexity Space complexity for linear search is O(n) as it does not use any extra space where n is the number of elements in an array. Time Complexity *Best- case complexity = O(1) occurs when the search element is present at the first element in the search array. *Worst- case complexity = O(n) occurs when the search element is not present in the set of elements or array. *Average complexity = O(n) is referred to when the element is present somewhere in the search array. Example, Let’s take an array of elements as given below: 45, 78, 12, 67, 08, 51, 39, 26 To find ‘51’ in an array of 8 elements given above, a linear search algorithm will check each element sequentially till its pointer points to 51 in the memory space. It takes O(6) time to find 51 in an array. To find 12, in the above array, it takes O(3), whereas, for 26, it requires O(8) time.   Binary Search This algorithm finds specific items by comparing the middlemost items in the data collection. When a match occurs, it returns the index of the item. When the middle item is greater than the item, it searches for a central item of the left sub-array. In contrast, if the middle item is smaller than the search item, it explores the middle of the item in the right sub-array. It continues searching for an item until it finds it or until the sub-arrays size becomes zero. Binary search needs sorted order of items. It is faster than a linear search algorithm. It works on the divide and conquers principle. Run-time complexity = O(log n)  The binary search algorithm has complexities as given below: Worst-case complexity = O (n log n) Average complexity = O (n log n) Best case complexity = O (1) Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Example, Let’s take a sorted algorithm of 08 elements: 08, 12, 26, 39, 45, 51, 67, 78 To find 51 in an array of the above elements, The algorithm will divide an array into two arrays, 08, 12, 26, 39 and 45, 51, 67, 78 As 51 is greater than 39, it will start searching for elements on the array’s right side. It will further divide the into two such as 45, 51 and 67, 78 As 51 is smaller than 67, it will start searching left of that sub-array. That subarray is again divided into two as 45 and 51. As 51 is the number matching to the search element, it will return its index number of that element in the array. It will conclude that the search element 51 is located at the 6th position in an array. Binary search reduces the time to half as the comparison count is reduced significantly than the linear search algorithm.  Read: Types of Data Structures in Python Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Interpolation Search It is an improved variant of the binary search algorithm and works on the search element’s probing position. Similar to binary search algorithms, it works efficiently only on sorted data collection.  Worst execution time = O(n) When the target element’s location is known in the data collection, an interpolation search is used. To find a number in the telephone directory, if one wants to search Monica’s telephone number, instead of using linear or binary search, one can directly probe to memory space storage where names start from ‘M’.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Hashing One of the most widely used searching techniques in data structure, the underlying method of hashing transforms how we access and retrieve data. Fundamental to hashing are hash functions, which convert input data into fixed-size values called hashes. Hashing allows constant access, providing a direct path to the element of interest. To explain searching in data structure, let’s examine the intricacies of creating hash functions and overcoming potential obstacles like collisions as we go into the principles of hashing. Understanding Hashing Hashing is essentially the same as a secret code for data. An input (or key) is passed to a hash function, which converts it into a fixed-length string of characters—typically a combination of integers and letters. The generated hash is then used to search data structures, usually an array, as an index or address to find the corresponding data. Compromises in hashing Hashing has trade-offs, even if it has constant-time access appeal. The quality of the hash function determines how efficient hashing is; poorly constructed methods can increase collisions and reduce performance. Furthermore, overly complicated hash functions could introduce computational costs. Selecting the best hash function and collision resolution plan requires considering the dataset’s unique properties and anticipated usage patterns. One must strike a balance between simplicity, efficiency, and uniform distribution. Depth-First Search (DFS) When we move from what is searching in data structure in linear structures to the more complex domain of trees, Depth-First Search (DFS) becomes a key method to investigate tree branch searching in DS.  The structural diversity of trees and graphs is easily accommodated by DFS in algorithms for searching. The implementation is elegant because of its recursive nature, which mimics the innate recursive structure of trees. The traversal’s depth-first design is advantageous when focusing on taking a path and working your way to the end rather than examining other options. Let’s examine the versatility and effectiveness of DFS for searching operation in data structure by exploring its uses in various tree-based structures, such as binary trees and graphs.  How Depth-First Search Works DFS investigates as thoroughly as possible along one branch, starting with the root of a tree or a selected node in a graph, and then turns around to examine other branches. The process continues till every node has been visited. Applications of DFS in binary trees DFS fits in well with the structure of binary trees. It performs well when the objective is to search for a specific element or navigate the whole depth of the tree. Pre-order, in-order, and post-order DFS variations provide flexibility in capturing various facets of the tree’s contents and structure. Preorder DFS: Visits the live node before any offspring. To create a sorted list for binary search trees, in-order DFS visits the left child first, then the current node, and lastly, the right child. Post-order DFS: Frequently employed to remove nodes from a tree, it visits the children before the current node. Breadth-First Search (BFS) Breadth-First Search (BFS) is a logical and systematic way to explore a tree’s levels. In contrast to Depth-First Search (DFS), BFS chooses a different approach by focusing on the shallowest levels before going deeper.  Let’s examine the complexities of BFS, how to use it for search in data structure, its benefits, and applications. How Breadth-First Search Works BFS goes through a tree or graph level by level, methodically investigating every node at each level before going on to the next. The method ensures a thorough examination of the entire structure by gradually covering each level, starting from the root (or a selected node). BFS uses a queue data structure to keep track of the node processing order, which promotes a systematic and well-organized traversal. Applications of BFS Shortest Path Finding: BFS works exceptionally well when determining the shortest path is essential. BFS determines the shortest path from the root to any reachable node by methodically investigating levels. Because of this feature, BFS is an excellent option for applications such as navigation systems and network routing. Least Spanning Trees: BFS helps determine a graph’s least spanning trees. By methodically examining the graph, BFS finds the edges that make up the minimum spanning tree—a tree that spans all nodes with the lowest feasible total edge weight. Connected Components: BFS is skilled at locating connected components while working with undirected graphs. BFS assists in classifying nodes into discrete connected components by beginning at a node and investigating every reachable node. Benefits of BFS Optimal Path Finding: BFS ensures that the shortest path will always be used to reach a target node first. Because of its optimality, it is recommended in situations where accuracy and efficiency are crucial. Whole Investigation: BFS ensures that every level in a tree or graph is thoroughly and methodically investigated. When processing or analyzing every node in an organized way is the objective, this feature is helpful. Easily Implemented: Compared to more intricate traversal algorithms, BFS is comparatively simple to build due to its simplicity. Its popularity across various applications can be attributed in part to its simplicity. Future Trends in Data Structure Emerging technologies are changing the way we define searching in data structure in response to our increasingly digitally complex environment, which has seen an increase in demands driven by data. Future trends are set to transform data generation, organization, and consumption, as demonstrated by a 45% increase in AI integration.  Data structures have several uses in the rapidly changing technological scene, ranging from decentralized methods to quantum-inspired systems. Let’s explore some of these trends: Quantum Realm Navigation via Quantum Data Structures Designing quantum data structures becomes important as quantum computing gets closer to being a reality. Quantum algorithms may prove challenging for use in conventional data structures. Quantum data structures are expected to rise, using the concepts of superposition and entanglement. These structures will transform the effectiveness of quantum algorithms for information processing by optimizing data representation and manipulation in quantum computing environments. Decentralized Information Structures Decentralized data structures are now possible thanks to blockchain technology, which puts the traditional centralized methods of the types of searching in data structure to the test.  Decentralized data structures provide improved ownership, security, and transparency. They are frequently built on distributed ledger technology. Users now have more control over the data they own, and the immutability of blockchain guarantees data integrity. Decentralized data structures might play a key role in applications in the future, such as transparent supply chain tracking and safe identity management. Self-Correcting Data Structures Modern data is dynamic, necessitating any type of searching in data structures to be flexible. Self-adjusting data structures are modeled after self-healing systems and can optimize and rearrange their configurations in response to environmental changes, usage patterns, or data properties.  By increasing system flexibility and efficiency, these structures should lessen the requirement for human involvement in data structure management. Adaptive Memory Allocation Frameworks It is frequently difficult for traditional memory allocation architectures to handle dynamic workloads effectively. The creation of dynamic memory allocation structures capable of deallocating and allocating memory intelligently in response to real-time demand will likely be a trend in the future for searching in DSA.  These architectures are designed to minimize memory fragmentation, maximize resource utilization, and enhance overall system performance, especially in workload-varying applications. Data Structures for Privacy The development of data structures that put data safety and protection first is driven by privacy concerns. Private-preserving algorithms for searching, homomorphic encryption methods, and cryptographic data structures are becoming essential for protecting confidential data.  Privacy preservation will be given top priority in the future for search in data structure, enabling people to keep control over their data while also taking advantage of cutting-edge computational capabilities. Conclusion Searching in data structures refers to finding a given element in the array of ‘n’ elements. There are two categories, viz. Sequential search and interval search in searching. Almost all searching algorithms are based on one of these two categories. Linear and binary searches are the two simple and easy-to-implementing algorithms in which binary works faster than linear algorithms. Though linear search is most straightforward, it checks each element until it finds a match to the search element, thus efficient when data collection is not sorted correctly. But, if the data collection is sorted and the length of an array is considerable, then binary search is faster. The data structure is an essential part of computer programming while dealing with datasets. Programmers and developers need to keep updating and upskilling themselves with basics and updates in computer programming techniques. Programmers dealing with data structure should opt for courses often.   If you are curious to learn more about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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29 May 2024

What is Linear Data Structure? List of Data Structures Explained
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54843

What is Linear Data Structure? List of Data Structures Explained

Data structures are the data structured in a way for efficient use by the users. As the computer program relies hugely on the data and also requires a large volume of data for its performance, therefore it is highly important to arrange the data. This arrangement of data in organized structures is known as a data structure. Storing of the data in data structures allows the access, modifications, and other operations that can be carried over the data elements. The arrangement of the data is mainly done in a computer and therefore proper algorithms are required to carry on operations with the data structures. Reducing space and decreasing the time complexity of different tasks is the main aim of data structures. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. The most important points in a data structure are: A large amount of data is organized through every type of data structure. A particular principle is followed by every data structure. The basic principle of the data structure should be followed even if any operations are carried out over the data structure.  Arrangement of the data within a data structure can follow different orders. A data structure is therefore classified according to the way of arrangement of the data. Basically, there are two types of data structure. Primitive data structure Non-primitive data structure  The primitive type of data structure includes the predefined data structures such as char, float, int, and double. The non-primitive data structures are used to store the collection of elements. This data structure can be further categorized into Linear data structure Non-Linear data structure.  Read: Learn the differences between linear and non linear data structure What is Linear Data Structure: Definition and Characteristics It is a type of data structure where the arrangement of the data follows a linear trend. The data elements are arranged linearly such that the element is directly linked to its previous and the next elements. As the elements are stored linearly, the structure supports single-level storage of data. And hence, traversal of the data is achieved through a single run only. Characteristics It is a type of data structure where data is stored and managed in a linear sequence.  Data elements in the sequence are linked to one after the other. Implementation of the linear structure of data in a computer’s memory is easy as the data is organized sequentially. Array, queue. Stack, linked list, etc. are examples of this type of structure. The data elements stored in the data structure have only one relationship. Traversal of the data elements can be carried out in a single run as the data elements are stored in a single level. There is poor utilization of the computer memory if a structure storing data linearly is implemented. With the increase in the size of the data structure, the time complexity of the structure increases. Must read: Learn excel online free! These structures can therefore be summarized as a type of data structure where the elements are stored sequentially and follow the order where: Only one first element is present which has one next element. Only one last element is present which has one previous element. All the other elements in the data structure have a previous and a next element Our learners also read: Data structures and Algorithms free course! upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses You can use linear data structure in C, C++, Python, JavaScript, or any other programming language you are familiar with. If you get baffled when given a list of linear structures and wonder which of the following is a linear data structure, here’s a rundown of the types.  Types of Linear Data Structures Operations performed on linear data structure include insertion, deletion, searching, traversing, and sorting. All these operations serve as the foundation for linear data structures. Discussed below are the linear data structure types and the corresponding operations on linear data structures that can be performed. You can also look for what is linear data structure with  examples to develop a robust idea.  1. Array The array is that type of structure that stores homogeneous elements at memory locations which are contiguous. The same types of objects are stored sequentially in an array. The main idea of an array is that multiple data of the same type can be stored together. Before storing the data in an array, the size of the array has to be defined. Any element in the array can be accessed or modified and the elements stored are indexed to identify their locations. An array can be explained with the help of a simple example of storing the marks for all the students in a class. Suppose there are 20 students, then the size of the array has to be mentioned as 20. Marks of all the students can then be stored in the created array without the need for creating separate variables for marks for every student. Simple traversal of the array can lead to the access of the elements. Arrays incorporate the use of zero-based indexing techniques. This means users can access the first element with an index of 0, the second with an index of 1, and so on. Another remarkable feature of this linear data structure is that arrays provide a constant time of O(1) to access the elements, which means that it takes equal time to access any element in the series, disregarding the size of the array. Types of array: One-dimensional array: This is a simple form of array that contains elements, all of which are the same type of data, in a single row.  Two-dimensional array: This is also known as a matrix. This type of data structure has rows and columns and appears like a grid. The elements can be accessed using two indices- one for column and one for row.  Multi-dimensional array: These arrays have more than two dimensions.  Operations Performed on Arrays: The following operations can be performed on arrays: Accessing an element: Accessing an element by its index is an essential operation that can be performed on arrays. It is a constant time operation with a time complexity of O(1).  Inserting or deleting elements: Inserting elements at the end of an array is a constant-time operation having complexity O(1). But inserting an element at the beginning takes O(n) time since all the elements have to be shifted.  The same goes for deletion of elements in an array. Searching for elements: For unsorted data, linear search takes O(n) time, and for sorted data, binary search takes O(logn) time.  2. Linked list The linked list is that type of data structure where separate objects are stored sequentially. Every object stored in the data structure will have the data and a reference to the next object. The last node of the linked list has a reference to null. The first element of the linked list is known as the head of the list. There are many differences between a linked list to the other types of data structures. These are in terms of memory allocation, the internal structure of the data structure, and the operations carried on the linked list.  Getting to an element in a linked list is a slower process compared to the arrays as the indexing in an array helps in locating the element. However, in the case of a linked list, the process has to start from the head and traverse through the whole structure until the desired element is reached. In contrast to this, the advantage of using linked lists is that the addition or deletion of elements at the beginning can be done very quickly.  Our learners also read: Free Python Course with Certification There are three types of linked lists: Single Linked List: This type of structure has the address or the reference of the next node stored in the current node. Therefore, a node which at the last has the address and reference as a NULL. Example: A->B->C->D->E->NULL. A Double Linked List: As the name suggests, each node has two references associated with it. One reference directs to the previous node while the second reference points to the next node. Traversal is possible in both directions as reference is available for the previous nodes. Also, explicit access is not required for deletion. Example: NULL<-A<->B<->C<->D<->E->NULL. Linked List which is circular: The nodes in a circular linked list are connected in a way that a circle is formed. As the linked list is circular there is no end and hence no NULL. This type of linked list can follow the structure of both singly or doubly. There is no specific starting node and any node from the data can be the starting node. The reference of the last node points towards the first node. Example: A->B->C->D->E. Properties of a linked list are: Access time: O(n) Searching time: O(n) Adding element: O(1)  Deleting  an Element : O(1)  3. Stack The stack is another type of structure where the elements stored in the data structure follow the rule of LIFO (last in, first out) or FILO (First In Last Out). Two types of operations are associated with a stack i.e. push and pop. Push is used when an element has to be added to the collection and pop is used when the last element has to be removed from the collection. Extraction can be carried out for only the last added element. Types of stack: There are two types of stacks:  Fixed-size stack: This kind of stack does not grow or shrink. Once full, any attempt to add an element will lead to an overflow error. Similarly, an attempt to remove an element will also display an underflow error. Dynamic size stack: This kind of stack can grow or shrink. When a stack is full or empty, it can automatically resize to accommodate a new element or shrink in size.  Other operations are:  top(): This operation helps to return the element that has been inserted last and is at the top without removing it.  size(): This operation indicates the total number of elements the stack contains. isEmpty(): This operation helps to identify if a stack is empty.  Properties of a stack are: Adding element: O(1) deleting element:  O(1) Accessing Time: O(n) [Worst Case] Only one end allows inserting and deleting an element. Examples of the stack include the removal of recursion. In scenarios where a word has to be reversed, or while using editors when the word that was last typed will be removed first (using an undo operation), stacks are used. If you want to try interesting data structure projects, click to read this article. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 4. Queue Queue is the type of data structure where the elements to be stored follow the rule of First In First Out (FIFO). The particular order is followed for performing the required operations over the elements. The difference of a queue from that of a stack lies in the removal of an element, where the most recently added object is removed first in a stack. Whereas, in the case of a queue, the element that was added first is removed first. Following is a list of the different types of queues: Input restricted queue: In this kind of queue, one can only insert inputs from one end. Deletion, however, can be done from both ends.  Output restricted queue: This is just the reverse of input restricted queues. Here, the input can be taken from both ends, but deletion can only be done from one end.  Circular queue: In this kind of queue, the first and the last positions are connected to one another, resulting in a circular structure.  Double-ended queue: This kind of operation supports insertion and deletion from both ends.  Priority queue: In this kind of queue, elements can be accessed based on priority assigned to them. Both the end of the data structure is used for the insertion and the removal of data. The two main operations governing the structure of the queue are enqueue, and dequeue. Enqueue refers to the process where inserting an element is allowed to the collection of data and dequeue refers to the process where removal of elements is allowed, which is the first element in the queue in this case. Properties of a queue are: Inserting an element: O(1) Deleting an element: O(1) Accessing Time: O(n) Other queue operations are: peek() or front(): This helps to acquire the data element available at the queue’s front node without actually eliminating it.  rear(): This operation returns an element at the rear without it being removed.  ifNull(): Finds out if a queue is empty.  ifFull(): Finds out if a queue is full.  Have a look at this linear data structure example.  Example of the queue: Similar to those queues made while waiting for the bus or anywhere, the data structure too follows the same pattern. We can imagine a person waiting for the bus and standing at the first position as the person that came to the queue first. This person will be the first one who will get onto a bus, i.e. exit the queue. Queues are applied when multiple users are sharing the same resources and they have to be served on the basis of who has come first on the server. 5. String Although non-modifiable, Strings display characteristics similar to those that linear data structures possess. They allow operations such as searching, concatenation, and substring extraction.  Strings are used for input/output operations, data manipulation, and text processing. Differences Between Non-Primitive Data Structure: Linear and Non-Linear Now that you know the fundamentals of what is linear data structure, let’s look at the differences between linear and non-linear data structures: Linear Data Structure Non-linear Data Structure  The elements are arranged in a sequence. The elements are arranged in the form of a hierarchy. One can access the elements sequentially. One can access the elements randomly or based on relationships. Efficient memory usage. Complex memory usage. Searching and sorting operations can be performed simply. Searching and sorting operations are complex. Examples: Array, Lined List, Stack, Queue, String Examples: Tree, Graphs Pros and Cons of Linear Data Structures  Some advantages and disadvantages associated with linear data structures make them suitable or unsuitable for the scenarios in which they operate. Understanding these advantages and disadvantages helps to choose the most appropriate linear data structure.  The advantages of linear data structures are as follows: Linear data structures help to access the elements sequentially.  For example, arrays facilitate efficient transversal with the help of loops. This makes it suitable for tasks requiring data processing in a linear pattern.  Data can be inserted or deleted efficiently.  Arrays make it easy to access elements by index, and linked lists help insert or delete data at the beginning or end of a given list. Linear data structures are easy to understand and implement.  For instance, arrays help to store elements in a straightforward way, and linked lists use pointers that help to connect the nodes.  Linear data structures offer flexibility as they allow dynamic resizing.  This facilitates easy accommodation of the varying data amounts. It especially holds true for linked lists, which can easily grow or shrink in size by removing or adding the nodes as and when required.  Let us now have a look at the disadvantages associated with linear data structures:   During the initialization of arrays, they have a fixed size determined.  As a result, the flexibility is limited. If the size is too small to store any additional elements, the entire array may be resized.  There are insufficient search operations in linear data structures.  For instance, in the case of linked lists and arrays, one has to traverse the elements one by one until the desired result is obtained. The time complexity of O(n) in the case of linear search may lead to inefficient results if the data set is large.  Linked lists need more memory to store pointers than arrays.  This increases the overall memory usage of the data structure and causes the efficiency to decrease.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Real-world Applications of Linear Data Structures  The vast usage of linear data structures in the real world is noteworthy. Listed below are a few instances stating the use of linear data structures:  Database systems: Linear data structures like linked lists and arrays are essential to database systems. Arrays help in efficient indexing and make sequential access possible. Linked lists facilitate dynamic storage and allow efficient deletion and insertion of elements.  Text editors: Linear data structures incorporate features like redo and undo functionality. A stack data comes in handy in this case, as it stores the operations performed, allowing the users to revert the actions they performed initially.  Browser history: The array and linked list linear data structures are used to maintain a list of all the web pages a user visits. This makes it easy for users to navigate the web pages.  Task management: Queue is a linear data structure that helps in easy task management. As mentioned earlier, queues allow the tasks to be performed in a FIFO (First-In-First-Out) manner. This ensures that the oldest task is performed first.  Data storage: Arrays, the simplest data structure, can store items with the same data type. Due to this reason, they find widespread usage in arranging a game’s leader board that displays the rank of the players in descending order, cell phone contacts, online ticket booking systems, and many more.  Conclusion An increase in the size of the data has necessitated the efficient use of data structures in computer programs. Data if not organized in a structured manner, the performance of tasks over the elements becomes difficult. For a hassle-free operation, it is always important to organize it so that easy and effective operations can be carried out by computer programs. If the data elements are organized in sequential order then it is known as a linear data structure whereas if the data elements are arranged in a non-linear way, it is termed a non-linear structure.  Wide application of data structure has been observed in machine learning languages, real-life problems, etc. People, who are dreaming to work in this field, should be able to master these concepts. We hope this guide has been able to define linear data structure and the different types of linear data structures.  With companies taking a data-backed approach to decision-making, the demand for data scientists who can mine and analyze huge amounts of data will likely grow by 35% from 2022 to 2032. Therefore, a specialization in data science can help you enhance your knowledge and skillsets for the ever-evolving requirements of data science occupations. If you are want to learn more, then check out the upGrad Executive PG Programme in Data Science which provides a platform to transform you into successful data scientists. Designed for any mid-level professionals, the data science course will expose you to all the theoretical and practical knowledge required for your success. So why wait for other options, when success is just a click away. If any assistance is required, we will be happy to help you.

by Rohit Sharma

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28 May 2024

4 Types of Data: Nominal, Ordinal, Discrete, Continuous
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4 Types of Data: Nominal, Ordinal, Discrete, Continuous

Summary: In this Article, you will learn about what are the 4 Types of Data in Statistics. Qualitative Data Type Nominal Ordinal Quantitative Data Type Discrete Continuous The two types of data are qualitative and quantitative. Qualitative data is descriptive and conceptual, while quantitative data is numerical and can be measured statistically. Read more to know each in detail. Introduction Data science is all about experimenting with raw or structured data. Data is the fuel that can drive a business to the right path or at least provide actionable insights that can help strategize current campaigns, easily organize the launch of new products, or try out different experiments. All these things have one common driving component and this is Data. We are entering into the digital era where we produce a lot of Data. For instance, a company like Flipkart produces more than 2TB of data on daily basis.  In simple terms, data is a systematic record of digital information retrieved from digital interactions as facts and figures. Types of statistical data work as an insight for future predictions and improving pre-existing services. The continuous data flow has helped millions of organizations to attain growth with fact-backed decisions. Data is a vast record of information segmented into various categories to acquire different types, quality, and characteristics of data, and these categories are called data types. When this Data has so much importance in our life then it becomes important to properly store and process this without any error. When dealing with datasets, the category of data plays an important role to determine which preprocessing strategy would work for a particular set to get the right results or which type of statistical analysis should be applied for the best results. Let’s dive into some of the commonly used categories of data. Qualitative Data Type Qualitative or Categorical Data describes the object under consideration using a finite set of discrete classes. It means that this type of data can’t be counted or measured easily using numbers and therefore divided into categories. The gender of a person (male, female, or others) is a good example of this data type. These are usually extracted from audio, images, or text medium. Another example can be of a smartphone brand that provides information about the current rating, the color of the phone, category of the phone, and so on. All this information can be categorized as Qualitative data. There are two subcategories under this: Must read: Data structures and algorithms free course! Nominal These are the set of values that don’t possess a natural ordering. Let’s understand this with some examples. The color of a smartphone can be considered as a nominal data type as we can’t compare one color with others. It is not possible to state that ‘Red’ is greater than ‘Blue’. The gender of a person is another one where we can’t differentiate between male, female, or others. Mobile phone categories whether it is midrange, budget segment, or premium smartphone is also nominal data type. Nominal data types in statistics are not quantifiable and cannot be measured through numerical units. Nominal types of statistical data are valuable while conducting qualitative research as it extends freedom of opinion to subjects. Read: Career in Data Science Ordinal These types of values have a natural ordering while maintaining their class of values. If we consider the size of a clothing brand then we can easily sort them according to their name tag in the order of small < medium < large. The grading system while marking candidates in a test can also be considered as an ordinal data type where A+ is definitely better than B grade.  These categories help us deciding which encoding strategy can be applied to which type of data. Data encoding for Qualitative data is important because machine learning models can’t handle these values directly and needed to be converted to numerical types as the models are mathematical in nature. For nominal data type where there is no comparison among the categories, one-hot encoding can be applied which is similar to binary coding considering there are in less number and for the ordinal data type, label encoding can be applied which is a form of integer encoding. Difference Between Nominal and Ordinal Data Aspect Nominal Data Ordinal Data Definition Categories data into distinct classes or categories without any inherent order or ranking. Categories data into ordered or ranked categories with meaningful differences between them. Examples Colors, gender, types of animals Education levels, customer satisfaction ratings Mathematical Operations No meaningful mathematical operations can be performed (e.g., averaging categories). Limited mathematical operations can be performed, such as determining the mode or median. Order/ Ranking No natural or meaningful order exists. Categories have a specific order or ranking, but the magnitude of differences between ranks may not be uniform. Central Tendency Mode (most frequent category) Mode, median (middle category), but mean is not typically used due to lack of uniform interval between ranks. Example Use Case Classifying objects, grouping data Rating scales, survey responses, educational levels Quantitative Data Type This data type tries to quantify things and it does by considering numerical values that make it countable in nature. The price of a smartphone, discount offered, number of ratings on a product, the frequency of processor of a smartphone, or ram of that particular phone, all these things fall under the category of Quantitative data types. Also read: Learn python online free! The key thing is that there can be an infinite number of values a feature can take. For instance, the price of a smartphone can vary from x amount to any value and it can be further broken down based on fractional values. The two subcategories which describe them clearly are: Discrete The numerical values which fall under are integers or whole numbers are placed under this category. The number of speakers in the phone, cameras, cores in the processor, the number of sims supported all these are some of the examples of the discrete data type. Discrete data types in statistics cannot be measured – it can only be counted as the objects included in discrete data have a fixed value. The value can be represented in decimal, but it has to be whole. Discrete data is often identified through charts, including bar charts, pie charts, and tally charts. Our learners also read: Excel online course free! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Continuous  The fractional numbers are considered as continuous values. These can take the form of the operating frequency of the processors, the android version of the phone, wifi frequency, temperature of the cores, and so on.  Unlike discrete data types of data in research, with a whole and fixed value, continuous data can break down into smaller pieces and can take any value. For example, volatile values such as temperature and the weight of a human can be included in the continuous value. Continuous types of statistical data are represented using a graph that easily reflects value fluctuation by the highs and lows of the line through a certain period of time.  Difference between Discrete Data and Continous Data Aspect Discrete Data Continuous Data Definition Consists of distinct, separate values. It can take any value within a given range. Examples Number of students in a class, coin toss outcomes (1, 2, 3), customer count. Height, weight, temperature, time. Nature Usually involves whole numbers or counts. Involves any value along a continuous spectrum. Gaps in values Gaps between values are common and meaningful. Values can be infinitely divided without gaps. Measurement Often measured using integers. Measured with decimal numbers or fractions. Graphical representation Typically represented with bar charts or histograms. Represented with line graphs or smooth curves. Mathematical Operations Typically involves counting or summation. Involves arithmetic operations, including fractions and decimals. Probability Distribution Typically represented using probability mass functions Typically represented using probability density functions. Example Use Case Counting occurrences, tracking integers. Measuring quantities and analyzing measurements. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Importance of Qualitative and Quantitative Data Qualitative types of data in research work around the characteristics of the retrieved information and helps understand customer behavior. This type of data in statistics helps run market analysis through genuine figures and create value out of service by implementing useful information. Qualitative types of data in statistics can drastically affect customer satisfaction if applied smartly. On the other hand, the Quantitative data types of statistical data work with numerical values that can be measured, answering questions such as ‘how much’, ‘how many’, or ‘how many times’. Quantitative data types in statistics contain a precise numerical value. Therefore, they can help organizations use these figures to gauge improved and faulty figures and predict future trends. Must Read: Data Scientist Salary in India Can Ordinal and Discrete type overlap? If you pay attention to this, you can give numbering to the ordinal classes, and then it should be called discrete type or ordinal? The truth is that it is still ordinal. The reason for this is that even if the numbering is done, it doesn’t convey the actual distances between the classes. For instance, consider the grading system of a test. The respective grades can be A, B, C, D, E, and if we number them from starting then it would be 1,2,3,4,5. Now according to the numerical differences, the distance between E grade and D grade is the same as the distance between the D and C grade which is not very accurate as we all know that C grade is still acceptable as compared to E grade but the mid difference declares them as equal. You can also apply the same technique to a survey form where user experience is recorded on a scale of very poor to very good. The differences between various classes are not clear therefore can’t be quantified directly.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Different Tests We have discussed all the major classifications of Data. This is important because now we can prioritize the tests to be performed on different categories. Now it makes sense to plot a histogram or frequency plot for quantitative data and a pie chart and bar plot for qualitative data. Regression analysis, where the relationship between one dependent and two or more independent variables is analyzed is possible only for quantitative data. ANOVA test (Analysis of variance) test is applicable only on qualitative variables though you can apply two-way ANOVA test which uses one measurement variable and two nominal variables. In this way, you can apply the Chi-square test on qualitative data to discover relationships between categorical variables. Analysis of Variance (ANOVA) Test Utilizing ANOVA as a statistical method, we can analyze the variances among group means within a sample, its particular value lies in comparing three or more groups. This type of data allows us to determine if there exist statistically significant differences between them. Here’s an overview of the ANOVA test: 1. Purpose Employing ANOVA, we can assess for statistically significant differences between the means of three or more independent (unrelated) groups. This is achieved by a comparison – the variation between group means versus that within the groups. 2. Assumptions Independence Observations within each group must be independent. Homogeneity of Variance: The variances within each group should be roughly equal. 3. Types of ANOVA One-Way ANOVA: Compares means across three or more groups within a single independent variable. Two-Way ANOVA: Analyzes the influence of two different independent variables on a dependent variable. 4. Hypotheses Alternative Hypothesis (Ha): Suggests that at least one group means is different from the others. 5. Test Statistic ANOVA produces an F-statistic, which is the ratio of the variance among group means to the variance within the groups. The larger the F-statistic, the more likely it is that the group means are different. 6. Procedure Step 1: Collect and organize the type of data from the groups. Step 2: Calculate the mean, sum of squares, and degrees of freedom for both within-group and between-group variations. Step 3: Compute the F-statistic. Step 4: Determine the critical region and compare the calculated F-statistic to the critical value. Step 5: Make a decision about the null hypothesis. 7. Interpretation A p-value lower than the selected significance level (typically 0.05) leads to rejection of the null hypothesis, signifying differences among group means. Should the p-value surpass the significance level, we cannot reject the null hypothesis due to inadequate evidence. 8. Post-Hoc Tests ANOVA signaling significant differences triggers the need for post-hoc tests. These may include Tukey’s HSD or Bonferroni correction. Their purpose is to pinpoint specific group disparities. 9. Use Cases Various fields like psychology, medicine, finance and experimental sciences apply ANOVA to compare means across distinct groups. This application enables drawing population difference-based conclusions. 10. Limitations Assumes normality and homogeneity of variances. Sensitive to outliers. Understanding and appropriately applying ANOVA is crucial for researchers and analysts aiming to compare multiple groups efficiently and draw meaningful conclusions from their data.   Why Are Data Types Important in Statistics?  Data types play a crucial role in statistics for several reasons: 1. Data Understanding Data types provide information about the nature of the variables and the kind of values they can take, aiding in understanding the dataset. 2. User Training and Adoption Educating users on data types fosters better understanding and utilization of analytical tools. Users can make informed decisions about which analyses and visualizations are suitable for their data. 3. Analysis Selection Different data types require different analysis techniques. Choosing the appropriate analysis method depends on the data types involved. 4. Data Exploration Efficiency Efficient exploration of datasets is facilitated by understanding data types. Analysts can quickly identify key variables, assess their distributions, and gain insights into the characteristics of the data. 5. Statistical Tests The choice of statistical tests depends on the data types of variables. Parametric tests are used for continuous data, while non-parametric tests are suitable for categorical or ordinal data. 6. Data Treatment Understanding data types helps decide how to effectively handle missing values, outliers, and other data anomalies. 7. Visualization Data types determine the visualizations most appropriate for conveying insights, such as bar charts for categorical data and histograms for continuous data. 8. Data Transformation Data types influence the need for data transformation, such as normalizing or standardizing continuous variables for certain analyses. 9. Sampling Strategies When designing sampling methodologies, understanding types of data in data science aids in creating representative samples. This is especially important in stratified sampling, where different strata may have distinct data characteristics. 10. Error Identification Recognizing and addressing errors in data entry or measurement is easier when the expected data types are known. Inconsistencies can be identified by comparing actual data types with the anticipated ones. 11. Model Building In machine learning and regression analysis, the type of dependent and independent variables affects the choice of algorithms and the model’s assumptions. 12. Interpretation Data types impact how results are interpreted. The meaning of statistical measures like mean, median, and mode varies based on whether the data is continuous, discrete, or categorical. 13. Accuracy and Validity Misidentifying data types can lead to incorrect analyses, invalid conclusions, and inaccurate predictions. 14. Customized Data Processing Data and its types may require unique preprocessing steps. Tailoring data processing workflows to the specific characteristics of each type enhances the accuracy and relevance of analytical outcomes. 15. Data Governance Establishing and enforcing types of data in computer governance policies involves defining and adhering to standards for data types. This ensures consistency, quality, and compliance within an organization. 16. Data Integration Understanding data types ensures consistency and compatibility between datasets when combining data from different sources. 17. Data Validation Properly identifying and assigning data and its types helps ensure data accuracy and validity. Validation processes rely on understanding the nature of variables, ensuring that data conforms to expected formats and ranges. 18. Feature Engineering In machine learning, selecting and transforming features (variables) is crucial. Knowledge of types of data in management information system guides the creation of meaningful features, improving model performance and interpretability. 19. Data Privacy and Security Sensitivity to data types helps preserve data privacy by ensuring that the appropriate anonymization techniques are applied based on the data’s nature. 20. Reporting and Communication Accurate identification of data types ensures that findings are communicated clearly and accurately to stakeholders and decision-makers. 21. Efficient Storage Understanding data types helps in efficient data storage and retrieval, optimizing database performance. 22. Data Cleaning Different data types may require specific cleaning approaches. Handling missing values, outliers, and inconsistencies is more effective when considering the unique characteristics of each types of data in computer. 23. Resource Allocation Data types affect memory and processing requirements. The efficient allocation of resources depends on accurate knowledge of data types. 24. Algorithm Compatibility Certain algorithms are designed for specific data types. Matching the algorithm to the data type enhances computational efficiency and the overall performance of the analysis. 25. Cross-Domain Collaboration In collaborative environments, where individuals from diverse domains work with data, a shared understanding of data types promotes effective communication and collaboration. It reduces ambiguity and ensures a common language for discussing data-related concepts. 26. Adaptation to Evolving Technologies As data storage and processing technologies evolve, understanding data types becomes crucial for adopting and adapting to new platforms. It ensures seamless migration and utilization of emerging tools and frameworks. 27. Facilitating Data Integration Integrating data from various sources becomes smoother when data types are well-defined. Consistent data types across sources enhance interoperability and prevent complications during the integration process. 28. Data Lifecycle Management Throughout the data lifecycle, from collection to archiving, considering data types is essential. It influences decisions about retention periods, archival formats, and the overall management strategy for different types of data science. Learn Data Science Courses online at upGrad Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 29. Enhancing Data Exploration Tools Data exploration tools and platforms benefit from a clear understanding of data types. Features like automated visualizations, descriptive statistics, and recommendations can be optimized based on the characteristics of the data types present in a dataset. Emerging Trends in Data Types Some of the types of data in management information system are: – 1. Graph Data Especially in social networks, recommendation systems, and fraud detection graph databases and graph data models are gaining traction. Representing relationships and connections graph data proves its worth by uncovering patterns within complex networks. 2. Experiential Data As user interactions become more immersive, capturing experiential data beyond traditional metrics is crucial. This includes user sentiments, emotions, and experiences, providing a more holistic understanding of user behavior. 3. Exo-Structural Data With data expanding beyond organizational boundaries, exo-structural data involves integrating external data sources like open data, social media feeds, and third-party APIs. This enhances the context and depth of analysis. 4. Explainable Data AI is not the sole focus. Instead, an increasing emphasis is being placed on comprehensible data. To foster trust and compliance particularly in regulated industries understanding the reasons behind results, rather than merely accepting them, has become indispensable for us. 5. Synthetic Data To address privacy concerns, synthetic data generation is on the rise. Simulated datasets that retain statistical properties of real data but without sensitive information are used for testing algorithms and models. 6. Dark Data The term “dark data” denotes the amount of unstructured, untapped information that resides within organizations. As we focus on unlocking concealed value, applying advanced analytics and machine learning to extract insights from this dark data is becoming increasingly imperative. 7. Hybrid Data Types Blurring the lines between traditional categories, hybrid types of data involve combinations of structured and unstructured data. This poses new challenges and opportunities in terms of storage, processing, and analysis. 8. Exogenous Variables Analyses that incorporate external factors such as weather conditions, economic indicators, or social events as exogenous variables offer a more comprehensive understanding of the influences on outcomes. 9. Blockchain Data Structures Blockchain data structures are being incorporated into more and more industries outside of cryptocurrency. The implementation of smart contracts and immutable ledgers has a big impact on supply chain management, healthcare, and finance. It produces tamper-proof, clear data records. 10. Biometric Data Biometric authentication systems, which are increasingly used, generate biometric data such as fingerprints, facial recognition and voiceprints. This data crucial for security applications and user identification requires thorough analysis. 11. Augmented Reality (AR) Data AR data involves information captured from augmented reality experiences. This could include user interactions in AR environments, contributing to personalized marketing strategies and user engagement analysis. 12. Spatial-Temporal Data Fusion Combining spatial and temporal dimensions in data analysis is becoming more prevalent. This fusion is especially relevant in applications like smart cities, where understanding both location and time is vital. 13. Neurological Data The progression of neuroscience generates neurological data, such as brain activity patterns. Analysis of this information aids in the understanding of cognitive processes, mental health, and development in brain-computer interfaces. 14. Robotic Sensor Data As robotics and automation proliferate, data from sensors on robots provide insights into their movements, interactions, and operational efficiency. This is crucial for optimizing robotic systems in various industries. 15. Quantum Data With the development of quantum computing, quantum data types are emerging. These types of data leverage the principles of quantum mechanics to represent and process information, potentially revolutionizing data processing capabilities. Professionals dealing with a variety of datasets, including data scientists and analysts, need to stay up to date on these new developments in data types. In addition to creating opportunities for innovation, this approach improves decision-making skills and promotes a deeper comprehension of intricate data environments. Conclusion In this article, we discussed how the data we produce can turn the tables upside down, how the various categories of data are arranged according to their need. We also looked at how ordinal data types can overlap with the discrete data types. What type of plot is suitable for which category of data was also discussed along with various types of test that can be applied on specific data type and other tests that uses all types of data examples.  If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Advanced Certification in Data Science The program comes with an in-demand course structure created exclusively under industry leaders to deliver sought-after skills.  With the Big Data industry experiencing a surge in the digital market, job roles like data scientist and analyst are two of the most coveted roles. The course prepares learners with the right set of skills to strengthen their skillset and bag exceptional opportunities. Explore upGrad courses to learn more!

by Rohit Sharma

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28 May 2024

Binary Tree in Data Structure: Properties, Types, Representation &#038; Benefits
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Binary Tree in Data Structure: Properties, Types, Representation & Benefits

Data structures serve as the backbone of efficient data organization and management within computer systems. They play a pivotal role in computer algorithms and software development, contributing significantly to designing streamlined and effective programs. Data structures are indispensable across various domains in computer science, ranging from Artificial Intelligence to Operating Systems.  One of the most widely used non-linear data structures is trees. Trees portray a hierarchical arrangement, rendering ordering information irrelevant, unlike their linear counterparts, such as arrays, stacks, queues, and linked lists.  Comprising nodes and pointers, a tree’s structure involves parent nodes with left and right children, presenting a versatile and fundamental concept in data organization. This brings us to the topic we will discuss extensively in this blog – binary tree representation in data structure. Amongst different types of data structures are binary trees that come with more uses than most of the other types. Their most notable applications include peer-to-peer programming, search, cryptography, network routers with higher bandwidth than others, and 3D video games. We will now discuss in detail what binary trees in data science are, what are their types, and how are they represented.  What are binary trees? If you have worked on normal trees before or even know about their basics, you would know that there are no restrictions when it comes to the number of children that different nodes are allowed to have in these trees. Binary trees are a little different in this sense. Every parent or node in binary trees can have a maximum of only two children.  In binary tree representation, this limitation, however, simplifies the structure, making binary trees highly efficient in scenarios where quick data retrieval is essential. The binary nature allows for straightforward decision-making processes commonly used in searching algorithms. Despite the restriction on the number of children, the versatility and speed of binary trees make them crucial elements in various computer science applications. All nodes in a binary tree have three primary components –  a data element  This component has the actual information or value held by the node. It could represent various types of data, such as numbers, strings, or other relevant content, depending on the context of the tree. a right reference  The right reference is a pointer or reference that directs to the node’s right child. The right child is a node positioned to the right of the current node within the binary tree hierarchy. This reference helps traverse and explore the tree’s right branch. a left reference  Similar to the right reference, the left reference is a pointer or reference that points to the node’s left child. The left child is positioned to the left of the current node, forming the left branch of the binary tree. This reference is important for navigating and analyzing the tree’s left subtree. In combination, these three components create a cohesive structure, allowing for efficient organization, storage, and data retrieval within the binary tree representation. The data element holds the pertinent information, while the right and left references guide the relationships between nodes, forming the foundation for the tree’s hierarchical arrangement. The node that lies at the top of the tree is referred to as the root node. Parent nodes are those that have children. Children nodes and parent nodes are connected to each other through references. Nodes that don’t have any children are referred to as leaf nodes. It is clearly evident that nodes in binary trees can have one child, two children, or no children at all. Binary trees aren’t linear data structures like queues, arrays, stacks, and linked lists. They are hierarchical data structures instead.  Check out: Data Science Project Ideas for Beginners Important properties of nodes in binary trees A better understanding of these properties will help you in making the most of this discussion on binary trees. The depth of different nodes is defined as the number of nodes that exist on the way that connects the root to a particular node. That is why the depth of the root node is 0. On the other hand, the height of different nodes in a binary tree is the number of nodes that lie in the path that connects a particular node with the root node. That is why the height of leaf nodes is 0.  As you can clearly see, the depth of a node is measured by starting from the root node and then going down to reach that node. On the other hand, when it comes to calculating the height, we start at the node in question and then journey towards the root node. Both the times, we start at 0. There are people who also measure height and depth from1 and not from 0, which isn’t wrong and is just what different people prefer.  Now the maximum depth of a node is defined as the depth of a binary tree. Similarly, the maximum height of a node is defined as the height of a binary tree. So the height and depth of a binary tree are always the same.  Learn more: Data Structures & Algorithm in Python What is a binary search tree?  A binary search tree is the most common of all the other types of binary trees. It is a specialized binary tree that comes with properties that are different and more useful than any other form of a binary tree. What exactly is a binary search tree or BST? Just as its name suggests, a binary search tree is used to search data in the tree. A BST comes with properties that allow it to facilitate efficient searches. A BST is a binary tree that has the key of the node that is smaller and greater than nodes in the right sub-tree and nodes in the left sub-tree respectively.  Our learners also read: Free excel courses! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Advantages of binary search tree There are several advantages of binary search tree that contribute to their widespread use in computer science and algorithm design: Efficient search operations: The binary search tree structure enables quick and efficient search operations. The binary nature of the tree ensures that at each node, the left subtree contains values smaller than the node, and the right subtree contains values greater. This property allows for a binary search algorithm, reducing the search space at each step. Simple insertion and deletion: Inserting and deleting elements in a binary search tree is quite straightforward. The binary structure allows for easy maintenance of the order, and the tree can be rebalanced if needed to maintain optimal search performance. Ordered data retrieval: In-order traversal of a binary search tree results in sorted data retrieval. This property is valuable in scenarios where data needs to be accessed or processed in sorted order without additional sorting operations. Space efficiency: Compared to other data structures like arrays, binary search trees are more space-efficient, especially when dealing with dynamic datasets. Nodes are allocated as needed, and memory is not pre-allocated, making it flexible for varying data sizes. Versatility in applications: Binary search trees find applications in various domains, including databases, symbol tables, and compilers. Their efficient search and ordered retrieval properties make them versatile where these operations are critical. Balanced binary search trees: When balanced, binary search trees ensure optimal performance in terms of search, insertion, and deletion operations. Balanced trees, such as AVL trees or Red-Black trees, maintain a balanced structure, preventing the tree from degenerating into a linked list and ensuring logarithmic time complexity for operations. Representation of binary trees Binary tree representation in data structure can be carried out in different ways, each with distinct pros and cons. The prominent methods are binary tree representation using linked list (Linked representation), storage representation of binary tree (Sequential representation), and linear representation. 1. Linked representation Binary trees in linked representation are stored in the memory as linked lists. These lists have nodes that aren’t stored at adjacent or neighboring memory locations and are linked to each other through the parent-child relationship associated with trees.  In this representation, each node has three different parts –  Pointer that points towards the right node This is the more common representation. All binary trees consist of a root pointer that points in the direction of the root node. When you see a root node pointing towards null or 0, you should know that you are dealing with an empty binary tree. The right and left pointers store the address of the right and left children of the tree.  Pointer that points towards the left node  The second part is another pointer pointing towards the left child node. Similar to the right pointer, this element establishes the connection between the current node and its left child, contributing to the hierarchical structure of the binary tree. Data element The third part is the actual data element or value associated with the node. Depending on the context of the binary tree’s application, this could be any type of data, such as a number, a string, or another relevant piece of information. This is the more common representation. All binary trees consist of a root pointer that points in the direction of the root node. When you see a root node pointing towards null or 0, you should know that you are dealing with an empty binary tree. The right and left pointers store the address of the right and left children of the tree.  The linked representation of binary tree in data structure offers dynamic memory allocation, making it adaptable to varying tree sizes. The linked representation of binary tree in memory offers flexibility and ease of manipulation, making it a popular choice in applications requiring dynamic data structures. Our learners also read: Free Python Course with Certification 2. Sequential representation Although it is simpler than linked representation, its inefficiency makes it a less preferred binary tree representation of the two. The inefficiency lies in the amount of space it requires for the storage of different tree elements. The sequential representation uses an array for the storage of tree elements. The number of nodes a binary tree has defines the size of the array being used. The root node of the binary tree lies at the array’s first index. The index at which a particular node is stored will define the indices at which the right and left children of the node will be stored. An empty tree has null or 0 as its first index.  Also read: Free data structures and algorithm course! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Despite its inefficiency, the sequential representation of binary tree in data structure is beneficial where memory allocation is a concern, as it uses a contiguous memory block. However, this memory representation of binary tree may become impractical for large or dynamic datasets due to its fixed array size. 3. Linear Representation of Binary Tree Amongst the various representations of binary trees the linear representation focuses on organizing its elements linearly or sequentially, often for ease of traversal or processing. Two common linear ways of representing binary tree in memory are array-based linearization and in-order linearization. Array-Based Linearization: In the linear representation of binary tree, the tree is linearized into an array by traversing it in a specific order, such as level order or inorder. The elements are placed in the array based on their order in the traversal, allowing for a linear structure that simplifies access and manipulation. This method is particularly useful when a sequential representation is required for efficient storage and retrieval. In-order Linearization: In in-order linearization, the binary tree is traversed in an in-order fashion, meaning left subtree, root, and right subtree. This linearization results in a sequence of elements that follows the sorted order of the binary search tree. In-order linearization is often employed when the goal is to process or display elements in a sorted manner. Types of binary trees Full binary trees: Full binary trees are those binary trees whose nodes either have two children or none. In other words, a binary tree becomes a full binary tree when apart from leaves, all its other nodes have two children.Understanding the nature of full binary trees is fundamental, as their balanced structure enhances the efficiency of various tree operations. Complete binary trees: Complete binary trees are those that have all their different levels completely filled. The only exception to this could be their last level, whose keys are predominantly on the left. A binary heap is often taken as an example of a complete binary tree.Complete binary trees, exemplified by binary heaps, showcase specific flexibility in their structure, optimizing memory use while facilitating efficient data retrieval. Perfect binary trees: Perfect binary trees are binary trees whose leaves are present at the same level and whose internal nodes carry two children. A common example of a perfect binary tree is an ancestral family tree.With their balanced and symmetrical layout, perfect binary trees provide an idealized model for understanding the structural efficiency of certain tree-based data structures. Pathological degenerate binary trees: Degenerate trees are those binary trees whose internal nodes have one child. Their performance levels are similar to linked lists. Learn more about the types of binary tree. Pathological degenerate binary trees serve as a reminder that not all tree structures are inherently advantageous, emphasizing the importance of selecting the appropriate type based on specific use cases. Read: The Six Most Commonly Used Data Structures in R Benefits of binary trees There are a myriad of advantages of binary tree that make them a valuable choice for storing and organizing data hierarchically.  Some of the major advantages of binary tree include: An ideal way to go with the hierarchical way of storing data.  The parent-child relationships among nodes create a natural hierarchy, facilitating efficient organization and retrieval of information. Reflect structural relationships that exist in the given data set.  The arrangement of nodes, with each node having at most two children, captures relationships and dependencies within the data, enhancing the representation’s meaningfulness. Make insertion and deletion faster than linked lists and arrays. The hierarchical structure allows for streamlined updates, making it easy to add or remove elements while maintaining the overall organization of the tree. A flexible way of holding and moving data. The dynamic nature of the structure, with nodes being added or removed as needed, enables adaptability to changing data requirements. This flexibility is particularly advantageous in dynamic or evolving datasets. Are used to store as many nodes as possible. The hierarchical organization ensures that the tree can accommodate a substantial volume of data while maintaining a balanced structure. Are faster than linked lists and slower than arrays when comes to accessing elements. In terms of access speed, binary trees strike a balance between linked lists and arrays. While they may not match the swift access times of arrays, they outperform linked lists in speed. This balanced performance makes binary trees the preferred choice where a compromise between insertion/deletion efficiency and access speed is crucial. Must Read: What is Linear Data Structure? Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion In this blog, we have discussed what binary trees in data structures are as well as talked about their types, their representations, and their benefits. The two major uses of the trees are for searching and storing data, and hence they are integral to the study of Data Science and its related fields.   Understanding the different types of binary trees helps us handle various data organization challenges, while the ways we represent them offer insights into storing and retrieving information dynamically. Recognizing the inherent benefits of binary trees, we see them not just as computer science structures but as crucial elements supporting the foundations of Data Science. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis If you are curious to learn about binary trees in data structures, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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21 May 2024

Python Free Online Course with Certification [2024]
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Python Free Online Course with Certification [2024]

Summary: In this Article, you will learn about python free online course with certification. Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries Read more to know each in detail. Want to become a data scientist but don’t know Python? Don’t worry; we’ve got your back. With our free online Python course for beginners, you can learn Python online free and kickstart your data science journey. You don’t have to spend a dime to enroll in this program. The only investment you’d have to make is 30 minutes a day for a few weeks, and by the end, you’d know how to use Python for data science.  To enroll in our Python course free, head to our upGrad free course page, select the “Python course, and register. This article will discuss the basics of python and its industrial application, our course contents, and what its advantages are. Let’s get started.  Why Learn Python? Python is among the most popular programming languages on the planet. According to a survey from RedMonk, a prominent analyst firm, Python ranked 2nd in their ranking of programming languages by popularity. Python became the first language other than Java or and JavaScript to enter the top two spots. You can see how relevant Python is in the current market. It’s a general-purpose programming language, which means you can use it for many tasks. Apart from data science, Python has applications in web development, machine learning, etc.  Python is one of the most popular programming languages. Python is used for web development, game development, language development, etc. It helps in conducting complex statistical complications and performing data visualisation. It is compatible with various platforms and has an extensive library. Top Python libraries are Numpy, Pandas, Scipy, Keras, Tensorflow, SciKit learn, Matplotlib, Plotly, Seaborn, Scrapy, and Selenium. These libraries serve different purposes such as some of them are for data processing, data modelling, data visualisation, and data mining. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. In data science, Python has many applications. It has multiple libraries that simplify various data operations. For example, Pandas is a Python library for data analysis and manipulation. It offers numerous functions to manipulate vast quantities of structured data. This way, it makes data analysis much more straightforward. Another primary Python library in data science is matplotlib, which helps you with data visualization. Python is one of the core skills of data science professionals. Learning it will undoubtedly help you in entering this field.  Read: Python Applications in Real World Python Installation and Setup Python installation is a simple procedure. Visit the Python website to get hold of the most recent version. Take care to add python to your system’s PATH during installation. You can look for a free python course with certificate online to gain practical experience. Many platforms provide thorough training to assist you in understanding the essentials. After installing python, create and run your code using an integrated development environment (IDE).  Don’t forget to look at python’s numerous libraries and frameworks, which can make development much simpler. As you advance through your python free course with certificate or python certification free put your newfound knowledge into practice by working on projects and practicing consistently. With perseverance, you’ll soon become an expert Python programmer, prepared to take on a variety of programming tasks. Check out all trending Python tutorial concepts in 2024 Basic Python Syntax and Data Types Any programming enthusiast must be familiar with the fundamental Python syntax and data structures. You will explore these fundamental ideas in your online python course free with certificate. Python is user-friendly for beginners because of its clear and accessible syntax. Line breaks are frequently used to end statements, and indentation is essential for code blocks. The python free certification course you have selected will walk you through variables, which are data storage units, and their naming conventions. Integers, floats, strings, and booleans are just a few of the different data types that python offers. In the python course online free with certificate, you’ll discover how to format and concatenate strings. Lists, another data type, are mutable and used to hold collections of elements. Dictionary entries are stored as key-value pairs, but tuples, like lists, are immutable. Conditional statements like if, else, and elif aid in regulating the program’s flow. Repetitive jobs are made possible via loops like for and while. The python free online course with certificate will place a strong emphasis on applying these ideas through exercises and projects as you progress through your learning process. By the end of the course, you’ll have a firm understanding of python’s syntax and data types and be prepared to go on to more advanced programming approaches. Control Flow and Loops In order to succeed as a programmer, you must master python’s control flow and loops. A thorough python certification course free will go through these topics in great detail. Your program can make decisions depending on conditions with the help of control flow structures like if, else, and elif. Another important idea is the use of loops, which let your code carry out repeated actions. The python full course free with certificate will guide you through the two main forms of loops: for and while. You can iterate over sequences like lists or strings with the “for” loop. At the same time, a condition is true; a ‘while’ loop, on the other hand, keeps repeating. By completing real-world examples and exercises in your chosen python free certification course, you’ll earn practical experience. Your comprehension of control flow and loops will become more robust as a result. By the end of the course, you’ll be able to design complex programs that efficiently make use of these structures. A solid understanding of control flow and loops is crucial when automating processes or creating intricate algorithms, and the correct course will provide you with these important skills. Why Choose Python free course from upGrad? There are many advantages to joining our Python free courses. Here are some of them: Expert Instructors At upGrad, our Python free course with certificate is given by a team of seasoned instructors, ensuring the best online learning experience for participants. Our instructors bring a wealth of industry knowledge and hands-on experience to the table, making the course not just educational but also practical and relevant to real-world scenarios. The experts leading the upGrad Python free course with certificate have a proven track record in Python programming, data science, and related fields. Their backgrounds span diverse industries, including web development, machine learning, and data analysis. This diversity ensures that learners gain insights into various applications of Python across different domains. The course content is designed by a collaborative effort of industry professionals and seasoned educators. This fusion of theoretical expertise and practical insights ensures that learners receive a well-rounded education in Python programming. 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Learners can progress through the Python course online free with certificate at their own pace, accessing materials and resources effortlessly. Cutting Edge Content upGrad’s professionally created content ensures that you get the best online learning experience. The curriculum of the course is industry relevant and focuses on practical concepts. To be able to learn the concepts a curriculum which is strong is recommended. This is what upGrad recommends. And after finishing a course, there are practice questions that one can solve in order to gauge retention. This free online python course for beginners is focused on the basics of python programming, It is a good opportunity for someone who is new to the field as it would take the learners on the journey step by step. It is also ideal for those learners who have been in the field for a long, so those candidates can brush up on their skills and revisit the concepts. Community and Networking Joining the upGrad Python community is not just about gaining a free Python certificate or completing a full course. It’s about becoming part of a strong network of learners, industry professionals, and alumni. Our platform thrives on developing connections that extend beyond the confines of the course, providing a comprehensive learning experience. The strong community of learners offers a supportive environment where participants can engage in discussions, share insights, and seek advice. This collaborative atmosphere enhances the overall learning journey, making the Python course free with certificate an interactive and enriching experience. Our platform facilitates interaction with industry professionals and alumni, providing unique insights into real-world applications and potential career paths. These connections go beyond the duration of the online Python course, serving as a valuable resource for ongoing learning and collaboration. 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This commitment reflects our dedication to offering a learning experience that remains at the forefront of technological advancements, making upGrad the ideal choice for those pursuing online Python courses with certificates that truly keep pace with the dynamic field of Python programming. Free Certificate After you complete our Python online course free, you’ll receive a certificate for completion. The certificate would enhance your CV substantially.  Apart from these benefits, the biggest one is that you can join the course for free. It doesn’t require any monetary investment. The free certificate is the validation of your knowledge. You could add the skill of knowing python to your CV and present the certificate in order to show authenticity. Also, the free certificate is shareable on LinkedIn. You could show your skill to potential recruiters. When you are appearing for any interview, or are looking to get promoted at your job these little things come to help where one can confidently show the document for the skillset that they have mentioned in the CV. It sets one apart from the rest of the candidates.  Access to Additional Resources In addition to our comprehensive Python programming online free course with certificate, upGrad provides participants with access to a wealth of supplementary resources, enriching the overall learning experience. Learners can benefit from webinars conducted by industry experts, offering insights into real-world applications and emerging trends in Python programming. Our dedication to providing a comprehensive educational experience is demonstrated by our workshops, which offer practical knowledge beyond the main curriculum. These courses provide participants with invaluable opportunities to improve their problem-solving abilities and gain a deeper understanding of particular Python ideas. Moreover, participants in the Python course online free with certificate gain access to additional reading materials curated to broaden their perspectives. By providing these supplementary resources, upGrad ensures that participants not only earn their free Python certificate but also gain a comprehensive understanding of the subject matter. Industry Recognition Earning a Python certification free from upGrad signifies industry recognition, validating your proficiency in Python programming. This Python free certification holds substantial weight in the job market, demonstrating your commitment to continuous learning and skill development. upGrad’s Python course free with certificate is meticulously designed to align with industry standards, ensuring that participants acquire practical, in-demand skills. As you complete online Python courses with certificates, you not only enhance your knowledge but also showcase your dedication to staying competitive in the ever-evolving tech landscape. This industry-recognized certification serves as a testament to your expertise, making you a sought-after candidate for Python-related roles and solidifying your position as a competent professional in the field. Additionally, the free Python course with certificate from upGrad adds significant value to your resume, opening doors to diverse opportunities. Let’s now discuss what the course is about and what it will teach you: Must read: Data structures and algorithms free course! Watch our Webinar on How to Build Digital & Data Mindset? Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis What Will You Learn? Learning Python is crucial for becoming a data scientist. It has many applications in this field, and without it, you can’t perform many vital operations related to data science. Because Python is a programming language, many students and professionals hesitate to study it. They read about Python’s various applications in data science, artificial intelligence, and machine learning and think it’s a highly complicated subject. However, Python is an elementary programming language that you can learn quickly.  Our free Python online course for beginners covers this prominent programming language’s basics and helps you understand its fundamental uses in data science. Below are the list of courses available in Python: Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries These sections allow you to learn Python in a stepwise manner. Let’s discuss each one of these sections in detail: Programming with Python: Python Free Online Course for Beginners In this course, you’ll get a stepwise python tutorial. It will familiarize you with Python’s fundamentals, what it is, and how you can learn this programming language. Apart from the basics, this section will explain the various jargons present in data science to you. You’ll get to know the meaning behind many technical terms data scientists usually use, including EDA, NLP, Deep Learning, Predictive Analytics, etc. Understanding what Python is will give you the foundation you need to study its more advanced concepts later on.  When you’d know the meaning behind data science jargon, you would understand how straightforward this subject is. It’s an excellent method to get rid of your hesitation in learning data science. By the end of this course, you would be able to use data science jargon casually like another data professional.  In the introduction, you will get to learn about the primary consoles, what are primary actions, what are statuses, and what important pointers. These topics will be covered in the introduction. The primary console is nothing but a media that takes the input front the user and then interprets it. In this opportunity to learn python online for free, you get to understand python programming from the basics. There is no compromise on imparting education. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Learn Basic Python Programming This section of our course will teach you Python’s basics from a coding perspective, including strings, lists, and data structures. Data structures are one of the essential concepts you can study in data science. The second topic would be concentrating on the basics of python that will be covering the introduction, history of python, how to do installation documentation, and what are arithmetic operations, and string operations. After the module would be over there would also be a focus on practice questions. These practice questions can be solved to understand how much understanding the learner has gotten. The learners upon answering will get the response to the questions on a real-time basis. Python online course free gives an opportunity to gain the skill of knowing python. They help in organizing data so you can access it and perform operations on it quickly. Understanding data structures is vital to becoming a proficient data scientist. Many recruiters ask the candidates about data structures and their applications in technical interviews. This module focuses on programming with Python in data science. So, it covers the basic concepts of many data structures, such as Tuples, sets, dictionaries etc.  The curriculum would also be focusing on dictionaries, and how to map, filter, and reduce functions. It also will focus on the OOPs, class and objects, methods, inheritance, and overriding. They are very important topics, for example, the OOPs is a computer programming model. It includes methods, classes, objects, etc. OOPs is useful for creating and developing real-life applications. Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. When you’re familiar with the basics, you can easily use them later in more advanced applications. For example, lists are among the most versatile data structures. They allow the storage of heterogeneous items (items of different data types) such as strings, integers, and even other lists. Another prominent property that makes lists a preferred choice is they are mutable. This allows you to change their elements even after you create the list. This course will cover many other topics similar like this. Our learners also read: Excel online course free! Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Learn Python Libraries: NumPy, Matplotlib and Pandas Python is popular among data scientists for many reasons. One of those reasons is its large number of libraries. There are more than 1,37,000 Python libraries. This number should give you an idea of how valuable these libraries are. These libraries simplify specific processes and make it easier for developers to perform related functions. In this course for beginners, you’ll learn about multiple Python libraries data scientists use, such as NumPy, matplotlib, and Pandas.  A Python library contains reusable code that helps you perform specific tasks with less effort. Unlike C or C++, its libraries don’t focus on a context. They are collections of modules. You can import a module from another program to use its functionality. Every Python library simplifies certain functions. For example, with NumPy, you can perform mathematical operations in Python smoothly. It has many high-level mathematical functions and support for multi-dimensional matrices and arrays. Understanding these libraries will help you in performing operations on data.   Pandas are used for better representation of the data, more work can be done with less coding in Pandas. It is a library of python for data analysis purposes. Pandas can be used for neuroscience, analytics, statistics, data science, advertising, etc.   Matplotlib is a library for Python. It is used for data visualisation and graphical plotting. The APIs (Application Programming Interfaces) of the matplotlib can also be used to plot in GUI applications.  Must Read: Python Project Ideas & Topics for Beginners How to Start To join our free online courses on python, follow the below mentioned steps: Head to our upGrad Free Courses Page Select the Python course Click on Register Complete the registration process That’s it. You can learn python for free with upGrad’s Free Courses and get started with your data science journey. You’d only have to invest 30 minutes a day for a few weeks. This program requires no monetary investment.  Sign up today and get started.  If you have any questions or suggestions regarding this topic, please let us know in the comments below. We’d love to hear from you.  If you are curious to learn about Python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

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20 May 2024

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