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Computer Notes in Hindi

Data Structure in Hindi & Types of Data structure in Hindi

हेल्लो दोस्तों! आज हम इस पोस्ट में बहुत ही आसान भाषा में Data Structure in Hindi (डाटा स्ट्रक्चर क्या है और इसके प्रकार कितने है?) के बारें में पढेंगे और इसके advantage को भी पढेंगे, तो चलिए शुरू करते हैं:-

• 1 Data Structure in Hindi – डाटा स्ट्रक्चर क्या है?
• 2 Types of Data Structure in Hindi
• 3 Advantage of Data structure in Hindi – डाटा स्ट्रक्चर के फायदे
• 4 इसके operations
• 5 Characteristics of Data Structure in Hindi – डाटा स्ट्रक्चर की विशेषताएं

Data Structure in Hindi – डाटा स्ट्रक्चर क्या है?

Data structure किसी कंप्यूटर सिस्टम में डेटा को स्टोर तथा व्यवस्थित (organize) करने का एक तरीका होता है। जिससे कि हम डेटा का आसानी से इस्तेमाल कर सकें। अर्थात डेटा को इस प्रकार स्टोर तथा organize किया जाता है कि उसको बाद में किसी भी समय आसानी से access किया जा सकें।

डाटा स्ट्रक्चर C, C++, Java की तरह कोई programming भाषा नहीं है बल्कि यह algorithms का एक set है जिसका प्रयोग हम programming languages में data को structure करने के लिए करते हैं.

Data structure बहुत सारें computer science algorithms का एक मुख्य भाग है जिसके द्वारा programmers डाटा को अच्छे ढंग से handle कर सकते हैं. यह program या software की performance को बेहतर करने में बहुत ही अहम भूमिका निभाता है.

Types of Data Structure in Hindi

data structure दो प्रकार के होते है:- 1. Primitive डेटा स्ट्रक्चर 2. Non-primitive डेटा स्ट्रक्चर

1:-Primitive डेटा स्ट्रक्चर:- primitive डेटा स्ट्रक्चर वह डेटा स्ट्रक्चर होता है जिसे direct ही मशीन instructions से operate किया जा सकता है। अर्थात यह सिस्टम तथा compiler के द्वारा डिफाइन होता है।

Non-primitive डेटा स्ट्रक्चर:- primitive डेटा स्ट्रक्चर वह डेटा स्ट्रक्चर होता है जिसे direct मशीन instructions से operate नही किया जा सकता है। ये डेटा स्ट्रक्चर primitive डेटा स्ट्रक्चर से derived होते है। Non-primitive डेटा स्ट्रक्चर दो प्रकार का होता है:- 1:-Linear डेटा स्ट्रक्चर 2:-Non-linear स्ट्रक्चर

1:- Linear data structure

linear एक ऐसा डेटा स्ट्रक्चर है जिसमें डेटा items को linear (रेखीय) रूप में संग्रहित तथा व्यवस्थित किया जाता है, जिसमें एक डेटा item दूसरे से एक रेखा के रूप में जुड़ा होता है। ex:- array , linked list , queue , stack .

2:- Non-linear data structure

Non-linear एक ऐसा डेटा स्ट्रक्चर है, जिसमें डेटा items को क्रमबद्ध (sequential) तरीके से व्यवस्थित नही किया जाता है। जिसमें एक डेटा item किसी भी अन्य डेटा items के साथ जुड़ा हुआ हो सकता है। ex:- tree , graph .

Advantage of Data structure in Hindi – डाटा स्ट्रक्चर के फायदे

इसके लाभ निम्नलिखित हैं.

• इसके द्वारा हम information को हार्ड डिस्क में store कर सकते हैं. और इस information का प्रयोग हम बाद में कर सकते हैं.
• बहुत बड़े database को हम आसानी से manage कर सकते हैं.
• यह algorithms को design करने के लिए बहुत ही जरुरी होता है.
• इसके द्वारा हम software system पर data का उपयोग कर सकते है और उसे process भी कर सकते हैं.
• यह data की processing को बहुत ही आसान बना देता है.
• हम internet का प्रयोग करके अपने laptop या mobile से data को किसी भी समय access कर सकते हैं.
• अगर programmer सही data structure का प्रयोग करे तो वह अपना बहुत सारा समय बचा लेता है और इसके साथ साथ storage और processing time को भी save कर पाता है.
• वह डाटा स्ट्रक्चर जो abstract data type (ADT) के द्वारा specify होता है वह abstraction प्रदान करता है. जिससे client डाटा स्ट्रक्चर के internal working को नहीं देख सकता है, इसलिए इसे working part के बारे में चिंता करने की आवश्यकता नहीं है। क्लाइंट केवल इंटरफ़ेस देख सकता है।
• डेटा स्ट्रक्चर reusability प्रदान करता है जिसका अर्थ है कि data structure को बहुत सारें clients प्रयोग कर सकते हैं.

Disadvantage of Data Structure in Hindi

इसकी हानियाँ निम्नलिखित हैं:-

• केवल advance users ही data structure में बदलाव कर सकते हैं.
• अगर कभी डाटा स्ट्रक्चर में कोई problem आ गयी तो उसे केवल expert ही solve कर सकता है. Basic users इसे solve नहीं कर सकते.
• अगर हमारा डाटा स्ट्रक्चर बहुत बड़ा है तो उसे maintain और create करने में बहुत सारें लोगो की जरूरत पड़ती है. जिससे इसका cost (मूल्य) बढ़ता है.

इसके operations

इसके मुख्य operations (कार्य) नीचे दिए गये हैं:-

• Searching – किसी element को find करने की प्रक्रिया searching कहलाती है. searching को पूरा करने की दो algorithms होती हैं पहला binary search और दूसरा linear search.
• Sorting – डाटा structure को किसी विशेष क्रम में arrange करने की प्रोसेस sorting कहलाती है. sorting को perform करने की बहुत सारीं algorithms है जैसे कि – insertion sort, selection sort, bubble sort, radix sort आदि. इसे पढ़ें:- Sorting क्या है और इसके प्रकार
• Insertion – किसी location में elements को add करने की प्रक्रिया insertion कहलाती है. यदि किसी डाटा स्ट्रक्चर का size n है तो हम उसमें केवल n-1 elements ही insert कर सकते हैं.
• Deletion – किसी element को remove करने की प्रोसेस deletion कहलाती है. हम किसी भी location से data को delete कर सकते है.
• Traversing – Traversing का मतलब है कि किसी विशेष कार्य को करने के लिए data structure के प्रत्येक element को traverse करना.
• Merging – दो ऎसी list जिनमें समान प्रकार के data elements हों, को एक साथ जोड़ देना merging कहलाती है. जिससे हमें एक तीसरी list प्राप्त होती है.

Characteristics of Data Structure in Hindi – डाटा स्ट्रक्चर की विशेषताएं

अब हम इसकी विशेषताओं को देख लेते है. जो कि नीचे दी गयी हैं:-

• यह operations का समूह होता है जिनको data items में परफॉर्म किया जाता है जैसे:- searching, sorting आदि.
• यह describe करता है कि data items एक दूसरे से किस प्रकार सम्बन्धित होते हैं.
• data structure के operations का execution time जितना संभव हो उतना कम होना चाहिए.
• इसके operations का memory usage जितना संभव हो उतना कम होना चाहिए.
• इसे interface को सही ढंग से implement करना चाहिए.

references:-

• https://www.javatpoint.com/data-structure-tutorial

निवेदन:- अगर आपके लिए डेटा स्ट्रक्चर की यह पोस्ट helpful रही हो तो हमें कमेंट के द्वारा बताइए तथा इसे अपने दोस्तों के साथ share करें. धन्यवाद.

39 thoughts on “Data Structure in Hindi & Types of Data structure in Hindi”

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DATA STRUCTURE DIGRAM IS INCORRECT , THREE ARE TWO TYPES OF DATA STRCTURE . 1 PRIMITIVE D S 2 NON -PRIMITIVE D S

1 PRIMITIVE D S ARE 2 TYPES := A .INTEGER B.FLOAT C.CHARCTER

2.NON PRIMITIVE D S ARE 2 TYPES := A.LINEAR B. NON LINEAR and plz change this digram

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Complete Data Structures in One Shot (4 Hours) in Hindi

Updated: August 24, 2024

5 Minutes Engineering

The video provides a comprehensive overview of fundamental data structure concepts, such as linear and non-linear structures, memory utilization, indexing, and efficient storage techniques. It explores practical examples using household items like refrigerators to simplify complex concepts. Topics covered include binary trees, hash functions, collision resolution, and traversal algorithms like BFS and DFS in graph structures. The speaker discusses the importance of maintaining balance in AVL trees and optimizing search operations using hash functions, while illustrating key data manipulation techniques like node insertion and deletion in linked lists.

TABLE OF CONTENTS

Introduction to Data Structures

Enhancing presentation with number of pages, visual aids and problem solving, understanding data structures with household examples, organizing items in refrigerator, storage manipulation and access, manipulation techniques and access efficiency, efficient storage and accessibility, data conversion and manipulation, data structures, hierarchical attachment, single run vs. multiple run, memory utilization, data representations, continuous memory allocation, introduction to indexing, data structure in linear data structure, insertion and deletion operations, calculation formula for data element addresses, understanding linear data structures, resolving indexing queries, application of address calculation formula, finding data element addresses, resolving address calculation queries, address verification approach, byte consideration, calculating addresses, column major order, column-wise representation, determining column entries, current column analysis, understanding respective columns, total elements calculation, element interests evaluation, order calculation, element numbering and address, basic operations on array elements, linear data structures, traversal and index manipulation, allocation and utilization management, insert operation and element shifting, shift operations and index management, shift operations and element adjustment, position and index management, understanding element position, position and index relationship, inserting elements, worst case scenario, left shift operation, understanding best case and worst case, effect of shift operations, considerations for insert and delete operations, search operation considerations, understanding array structure, navigation and structure understanding, navigating a guest house, searching for the watch, best case scenario, understanding memory locations, accessing memory nodes, moving to the next part, techniques in accessing memory, utilization of dynamic nature, transition to the next topic, exploring dynamic nature, variations in dimension, representation and access, linked representation, sequential representation, understanding pointers and traversal, implementing traversal and interpretation, exploring node relationships, utilizing head pointers, sequential traversal techniques, handling current and next nodes, navigating linked lists, interpretation and execution of traversal commands, printing data fields, managing current and next nodes, assignment of current pointer, traversing the list with initial head pointer, conditional execution and search function, inserting and searching nodes, traversal techniques and conditions, traversal completion and verification, variations in node insertion, pointer concepts and addressing, connection sequences and node addressing, connection verification and linking, explaining node insertion, shifting nodes, connecting nodes, finalizing node insertion, delete operation, cleaning up with delete pointers, completing deletion process, link management, head pointer maintenance, deleting respectful pointer name, updating pointers, deleting nodes, understanding flexibility in structures, inserting new nodes, maintaining pointers, incorporating new nodes, node insertion process, tuning the addresses, node address setup, final steps in node insertion, address correction and node deletion, deleting links and node pointer adjustment, head pointer adjustment, finalizing the head pointer, connection handling and node freeing, understanding node relationships, assigning addresses to nodes, establishing node connections, creating circular linked lists, handling node deletion, node deletion and connection assignment, managing circular linked lists, creating new head nodes, executing code and programming loop, changing traversal behavior, handling circular link lists, operations in data structures, understanding stack arrangement, importance of access points, exploring stack operations, top of the stack concept, direct data access, implementing static and dynamic nature in arrays and linked lists, understanding push and pop operations in a stack, understanding stack overflow condition, understanding stack underflow condition, assigning values in stack operations, handling initial stack conditions, final checks and assignments in stack, implementation of stack, evaluation of prefix and postfix expressions, infix, prefix, and postfix notations, conversion of infix to prefix and postfix, tree construction and expression evaluation, checking expression validity, traversal and expression construction, postfix expression generation, postfix expression evaluation, prefix expression generation, prefix expression evaluation, postfix expression construction, postfix expression evaluation continued, understanding stack fundamentals, operator rules and precedence, multiplication and division precedence, understanding operator interaction, operator hierarchy in a stack, finalizing stack operations, applying manual method for post-fix conversion, solving priority operations, handling operator precedence, understanding operator associativity, understanding postfix expressions, generating trees for arithmetic expressions, handling operators in trees, tree operations and evaluation, tree traversal and evaluation, precedence and associativity in operators, tree evaluation demonstration, evaluation using stack, introduction to operator stacks, handling plus operator, evaluation of multiplication, sequential operations, evaluation of expressions, correct order of evaluation, infix to postfix conversion, respectively first-in-first-out principle, understanding queue concepts, fundamental structure of queue, operation terminology in stack, insert and delete operations in stack, front and rear gates analogy, understanding initialization and conditions, overflow and underflow conditions, behavior during insertion, observing insert conditions, insert and rear behavior, underflow condition discussion, propagating front and rear points, understanding rear and front ends, insert and delete operations, deleting elements, memory utilization and circular queue, circular queue implementation, understanding circular queue operations, modulo operator usage, applying modulo operator, defining circular queue logic, understanding rear and front transitions, pointer manipulation in data structures, implementation of rear pointer, traversal and address assignment, incremental updates and address assignment, concept of trees in data structures, understanding tree structure, parent-child relationship in trees, traversal and node classification, parent-child relationship in tree structures, non-leaf nodes and paths, degree of nodes, ancestor and descendant nodes, depth and height of nodes, sibling nodes and heights, finding minimum depth and maximum height, understanding binary trees, maximum number of nodes in a binary tree, levels in binary trees, calculating nodes in binary trees, binary tree variations, complete binary trees, complexity of complete binary trees, full binary trees, perfect binary trees, tree traversal fundamentals, binary tree traversal, order in tree traversal, understanding child node ordering, node value determination, node ordering in traversal, traversal approach, traversing nodes continuously, sequential node traversal, implementing binary trees, parent-child relationships, index number identification, node data and structure, data pointers, implementation considerations, handling child nodes, understanding binary search trees, traversing binary search trees, benefits of binary search trees, optimizing searching with binary search trees, following bst rules for search, comparison and decision making in bst, rule-based searching, navigating bst with rule compliance, verification of bst rules, moving on to the next steps, importance of comparison in editing, node deletion in avl tree, hierarchy adjustment in tree, node reordering process, transformation in avl tree, concept of avl tree, understanding avl tree rules, introduction to node balance factor, demonstration of node rotation, node insertion impact on balance factor, node rotation to solve imbalance, complex node rotation scenario, final result of node rotation, conclusion and recap, identifying rotation issues, insertion and impact analysis, troubleshooting node insertion, understanding balancing in tree structures, applying rotation techniques, node insertion strategies, solving imbalance issues, ensuring tree balance, resolving node rotation problems, finalizing rotation solutions, explanation of impact, implementation process, node optimization, deletion and replacement, checking balance factors, completing binary trees, implementing properties in complete binary trees, converting binary trees with heapify, qualifying properties in max heap and min heap, applying min heap and max heap, resolving issues in min heap and max heap, generating max heap, inspecting max heap satisfaction, implementing max heap property at each level, decision making process, problem with alternate values, handling complex scenarios, threaded binary tree, in-order processor, non-linear data structure, graph structures, understanding cycles in graph, non-linear data structure concerns, circular loops in graphs, types of graphs, basic fundamentals in graph theory, weighted graphs, complete graphs, null graphs, adjacency matrix, directed graphs, multigraphs, singleton graphs, representation of absence of edges, example of direct graphs, traversing graphs, sparse matrices in graphs, graph traversal techniques, implementing bfs and dfs, graph traversal challenges, understanding connections, manipulating nodes in sequence, optimizing node sequences, sequential node analysis, efficient node ordering, node connectivity evaluation, optimizing node connectivity, strategizing node manipulation, effective node pop and push, efficient node sequence management, understanding hash function, application of hash function in real-life example, hash function complexity, understanding collision rate in hash functions, optimal hash function criteria, fundamentals of hash tables, effective application of hash functions, respective hash function implementation, understanding hash table sizing, implementing respective hash function, mid square method, folding method, collision handling, understanding collision resolution, open hashing, chaining technique, linear probing, collision handling with linear probing, understanding open hashing techniques, problem solving process with linear probing, incrementing probing values, quadratic probing technique, implementing quadratic probing, modification with quadratic probing, linear probing and clustering, second last topic: quadratic probing, implementation and verification, square patterns, collisions and index values, clustering and hashing, implementing hash functions, understanding modulus and reminder in hash function, handling collisions in hash tables, incrementing values in double hashing, finalizing reminder calculation in hashing, solving collision time efficiently, closing remarks and call to action.

Introduction to the basic concepts of data structures, including an overview of organizing and storing data in an efficient manner.

Exploring how to increase the length or size of pages in terms of content, paragraphs, and important points for better visual presentation.

Utilizing visuals, diagrams, and illustrations to enhance understanding of data structures and solving numerical problems efficiently.

Explaining data structures using familiar household items like refrigerators to simplify complex concepts and aid in comprehension.

Demonstrating the organization of items inside a refrigerator to highlight efficient storage and easy accessibility for better management.

Discussing the manipulation and efficient access to stored data, emphasizing the importance of organized storage and easy accessibility.

Highlighting techniques to manipulate and update stored data efficiently, ensuring easy access and management of structured information.

Emphasizing the importance of efficient storage techniques and easy accessibility to stored data, providing tips for organized and accessible data management.

Exploring data conversion and manipulation methods, focusing on efficient storage, organization, and manipulation of data for better management.

Discussion on linear and non-linear data structures, linear data structures arrange items linearly while non-linear structures arrange items non-linearly, explaining the concept of adjacency and hierarchical attachment.

Difference between linear and non-linear structures in terms of hierarchical attachment, operating at single level vs. multiple levels, with examples illustrating the complexity of non-linear structures.

Comparison between linear and non-linear data structures in terms of single run and multiple run functionalities, highlighting the efficiency of linear structures in utilizing memory space effectively.

Explanation of memory utilization in linear and non-linear data structures, emphasizing the importance of efficiently utilizing memory without wastage for memory-friendly data structures.

Discussion on data representations in linear and non-linear structures, showcasing examples like trees, graphs, and non-linear fashion data structures along with correlations and similarities in respective areas.

Explanation of continuous memory allocation in linear data structures, highlighting the storage of data through adjacency attachment arrangement and ensuring data consistency for different types of data.

Explaining how to declare and utilize an index in a specific area, including data type, size, and indices for data manipulation.

Discussing the advantages and disadvantages of random accessing data in linear data structure, addressing the use of indices for data retrieval and understanding the specific data structure.

Exploring the issues related to insert and delete operations, highlighting the shifting of elements in the array when performing these operations.

Demonstrating the formula (u - l + 1) used to calculate upper and lower index addresses in data elements for easy access and manipulation.

Addressing the importance of proper utilization of memory and avoiding wastage in data manipulation operations such as insert, delete, and move operations.

Solving queries related to indexing, including addressing elements from specific areas and determining the address of a specific element.

Applying the formula u - l + 1 to calculate the address of elements in various dimensions and understanding the concept of starting indexing from different values.

Explaining the process of finding the address of a data element in a one-dimensional array and addressing the indexing from zero and non-zero values.

Solving queries related to calculating data element addresses in different dimensions, focusing on the concept of starting indexing and addressing specific elements.

Explaining the process of address verification and the importance of base address in an array.

Discussing the consideration of byte size and its significance in determining element addresses in an array.

Demonstrating the calculation of addresses for elements in an array based on index values and byte sizes.

Illustrating the concept of column major order and how to determine column numbers in a matrix.

Explaining the representation of columns and entries in a matrix for better clarity and understanding.

Explaining the calculation of column entries and the total elements in a column for proper organization and storage.

Analyzing the current column for entries and determining the size based on respective elements.

Explaining the concept of respective columns and determining the number of elements in each column for organization.

Calculating the total elements in a matrix based on the number of rows and columns for proper indexing and storage.

Evaluating the number of elements and entries in a current column for better understanding and analysis.

Demonstrating the proper calculation of orders and elements in a column for accurate representation and organization.

Explanation of element numbering and address representation with examples like 1000, 10001, and 10007. Discusses column-wise and row-wise navigation in data to find the correct answer.

Demonstration of basic operations on array elements, emphasizing understanding the dimensions and traversal of respective elements.

Introduction to linear data structures and practical illustrations showcasing the allocation of space and utilization challenges like underutilization and overallocation.

Explanation of traversal techniques and index manipulation in data structures using real-world analogies like visiting cinema halls and shift operations during insertions.

Discussion on allocation and utilization management in data structures, highlighting issues related to supply-demand imbalances and the consequences of fixed element sizes.

Explanation of insert operation and element shifting within arrays, emphasizing the concept of shift operations and encountering index changes during insertions.

Detailed discussion on shift operations and index management during insertions, focusing on the importance of maintaining proper indexing to avoid data loss or misalignment.

Explanation of shift operations and element adjustment within arrays, illustrating how elements are rearranged during insertions and highlighting the challenges of data consistency.

Explanation of position and index management in arrays, emphasizing the significance of maintaining correct indexing for efficient data manipulation and retrieval.

Detailed explanation of element positions and indices in arrays, emphasizing the relationship between position and index in data structures.

Explanation of the relationship between position and index in the context of coding. Position is always equal to index + 1, providing clarity on their interplay.

Demonstration of inserting elements in the context of coding, with examples of inserting and shifting elements in an array or list.

Discussion on the worst case scenario in coding, highlighting the challenges and shift operations involved in insert and delete operations.

Explanation of the left shift operation in coding, demonstrating how elements shift in an array or list when performing insert or delete operations.

Comparison between the best case and worst case scenarios in coding, emphasizing the importance of the last element and the implications of insert and delete operations.

Illustration of the effects of shift operations in coding, showcasing how elements are rearranged in memory during insert and delete operations.

Discussion on the considerations for insert and delete operations in coding, highlighting the advantages and disadvantages of different scenarios.

Explanation of search operations in coding, using a building floor scenario to illustrate the concept of indexes and element positions in an array or list structure.

Illustration of array structure in coding, using a floor building analogy to explain the relationship between positions, indexes, and elements in an array or list.

Demonstration of navigating and understanding the structure of arrays in coding, emphasizing the importance of indexes and element positions for efficient coding practices.

The speaker shares a story about navigating a guest house and the challenge of finding a misplaced expensive watch. The process of searching through different floors and the decision-making involved is highlighted.

The speaker describes the meticulous search for the watch on various floors, emphasizing the importance of systematic searching and decision-making in such situations.

Exploration of the best and worst-case scenarios while searching for the watch, emphasizing the thrill of finding the watch in the best-case scenario and the disappointment in the worst-case scenario.

Explanation of continuous memory locations and the concept of linked memory locations. The speaker discusses the sequential access to memory locations and the importance of understanding these concepts.

Detailed explanation of accessing memory nodes in a linked list, including sequential fashion starting from the start node. The speaker highlights the sequential nature of accessing memory nodes for effective processing.

The speaker discusses random access in memory nodes and the limitations in shifting between memory locations. The importance of following a sequential sequence in accessing memory nodes is emphasized.

Explanation of dynamic and static nature of memory locations and the challenges in utilization. The contrast between dynamic and static memory locations is highlighted for better understanding.

The speaker explores the utilization and challenges of dynamic memory locations compared to static memory locations. The benefits and drawbacks of dynamic memory locations are discussed.

The speaker transitions to the next topic, emphasizing the importance of understanding the insert and delete operations in text formatting and the concept of dynamic nature in memory locations.

Exploration of dynamic nature in memory locations, focusing on fixed and static properties. The speaker discusses the benefits and limitations of dynamic memory locations in various scenarios.

Discusses the variations in dimensions from single link list to multi-dimensional array and the different views like single link list, double link list, circular link list, and their nature and implications.

Explains the difference between representation and access, highlighting how representation shows data in a sequence while representation in a linked list displays data in a linked format.

Illustrates linked representation in a linked format where notes are connected and highlights the end point of a linked list, pointing out nodes' linkage.

Describes sequential representation available in arrays and its contrast with linked representation, offering a view in array format.

In this section, the speaker explains the concept of pointers and traversal in detail, emphasizing the importance of understanding how nodes are linked in a list for effective traversal.

The speaker discusses the practical implementation of traversal and interpretation of nodes in a list, highlighting the significance of correctly interpreting the data fields and links between nodes.

This part covers exploring the relationships between nodes in a list, focusing on how pointers point to specific nodes and the data fields associated with these nodes.

The chapter delves into the usage of head pointers to navigate through linked lists, explaining how head pointers can be used to access and manipulate data fields and links between nodes effectively.

This chapter elucidates sequential traversal techniques, outlining how nodes are sequenced and traversed in a linked list to access and process data fields in a systematic manner.

The section details the handling of current and next nodes in a linked list, illustrating how navigation occurs based on the current and next node's addresses and the data contained in these nodes.

The speaker explains the process of navigating through linked lists by following the links between nodes, emphasizing the importance of understanding the sequence and relationships between nodes for effective traversal.

This chapter focuses on interpreting and executing traversal commands in linked lists, highlighting the crucial role of correctly interpreting and executing commands to access and manipulate data fields and links in nodes.

In this segment, the speaker demonstrates printing data fields in linked lists, showcasing how to access and print the data within nodes to understand the structure and content of the linked list.

The chapter discusses managing current and next nodes in linked lists, explaining the process of identifying and manipulating the current and next nodes to advance through the list effectively.

The speaker discusses assigning the current pointer to advance nodes in a loop, checking the current data, and moving ahead based on the data match.

Explains the concept of traversing a list using the initial head and checking data matches at each node before moving forward.

Details the process of conditional execution based on data matches at nodes and the usage of search functions to navigate through the list.

Covers the insertion of nodes in different positions in the list and the significance of searching for specific data in the nodes.

Explains traversal techniques in detail, including the conditions based on data matches and the iterative process of navigating through nodes.

Discusses the completion of traversal, verification of data matches at each node, and the process of ensuring successful traversal of the entire list.

Explains the variations in node insertion operations, such as inserting at the beginning or end of the list, and their importance in examinations.

Details the concepts of head and tail pointers, addressing nodes based on connections, and understanding node addresses within the list.

Explains the sequence of connections, addressing nodes within the list based on head addresses, and strategies for efficient traversal through the list.

Discusses the verification of connections between nodes, linking new nodes based on addresses, and the process of confirming connections for proper traversal.

The video discusses the process of inserting a new node in a linked list, starting with assigning the address and then pointing the head pointer to the new node.

After inserting a new node, the previous head node becomes the second node in the list through shifting. All nodes are shifted accordingly to accommodate the new node.

The process of connecting the new node to the respective nodes by assigning addresses and linking them in the correct order is explained in this segment.

The final steps of node insertion involve updating the addresses and pointers to ensure the new node is properly integrated into the linked list structure.

The deletion process is discussed, emphasizing the importance of managing temporary variables and pointers to ensure smooth removal of nodes in the linked list.

The detailed steps of cleaning up after node deletion, including freeing memory and adjusting pointers, are explained for maintaining the linked list integrity.

The final steps of the deletion process involve handling temporary variables, deleting nodes, and adjusting links to maintain the linked list structure.

Ensuring proper linking and address assignment during node deletion is crucial for maintaining the integrity and coherence of the linked list structure.

Explaining the importance of maintaining the head pointer and managing addresses during node deletion to ensure the linked list structure remains intact and functional.

The speaker explains the process of deleting a respectful pointer name and the implications of deleting nodes in the structure.

Details on updating pointers within the structure, including addressing previous and next nodes for proper maintenance.

Discussion on deleting nodes and managing connections within the structure to ensure smooth navigation through the nodes.

Exploration of the flexibility in structures when inserting and deleting nodes, with a focus on maintaining a link list efficiently.

Process of inserting new nodes at the beginning or end of the structure, with considerations for pointers and connections between nodes.

Explanation of maintaining pointers in a structure, including the concept of previous and next pointer relationships for seamless data management.

Discussion on incorporating new nodes in the structure and handling pointer assignments for effective node management.

The process of inserting nodes in a linked list is explained. The speaker discusses how to insert a new node after a specific node, the address assignments, and updating previous and next node links.

Address tuning in the linked list is elaborated, focusing on updating addresses in the next and previous nodes for successful connection establishment.

The speaker covers the setup of node addresses, including updating the previous node's link address in the next node's field, ensuring proper establishment of connections.

The final steps in node insertion involve updating addresses in the new node's next and previous fields, ensuring correct link updates and the completion of the insertion process.

Address correction, deletion process, and the importance of updating addresses for proper node deletion are highlighted, emphasizing the deletion of nodes in the linked list.

The process of deleting links and adjusting node pointers is detailed, emphasizing the simplicity of the process and the importance of following specific fundaments.

The adjustment of the head pointer in a linked list is explained, focusing on updating the address in the head and subsequent nodes to facilitate proper traversal and operation.

The finalization of the head pointer involves pointing to the next and previous nodes correctly, ensuring smooth traversal and operation in the linked list structure.

The speaker discusses handling connections, cutting links, and freeing nodes to ensure efficient memory usage and structure management in the linked list.

Explains the process of creating relationships between nodes and the addresses involved in connecting nodes in a network.

Demonstrates how to assign addresses to nodes based on their connections and the concept of establishing connections between nodes.

Details the steps to establish connections between nodes by assigning addresses and linking nodes in a network.

Illustrates the creation of circular linked lists, understanding circular behavior, and establishing connections between nodes in a circular manner.

Discusses the process of deleting nodes in a network and assigning temporary connections to maintain network integrity.

Explains the procedure to handle node deletion by reallocating connections and establishing new head nodes in a network.

Demonstrates the deletion of nodes and reassigning connections to maintain network structure and integrity in a systematic manner.

Guidance on managing circular linked lists, understanding node pointers, and defining circularity in linked list structures.

Demonstrates the process of creating new head nodes in a network and justifies the use of current pointers and head nodes.

Finalizes the execution of code and programming loop operations while focusing on the current and next pointers in a network structure.

In this section, the traversal behavior in networks is discussed, highlighting the shift from mostly tail traversal to head traversal. The importance of the last node in pointing to the respective node is emphasized.

The process of updating pointers while traversing a network is explained with details on current-next and head-next pointers, emphasizing the iterative movement and node addressing.

The concept of circular link lists is introduced along with practical demonstrations, focusing on addressing and updating head pointers in linked lists.

Discussion on data structure operations like insert, delete, push, and pop, specifically related to stack implementations, and the significance of top stack interactions.

The analogy of stack arrangement with biscuit stacking is used to explain the nature of operations like insert and delete, emphasizing the top-end interaction and proper arrangement in data structures.

Importance of access points, particularly in stack data structures, is highlighted with examples of single-end access and the significance of top-of-stack interactions.

The intricacies of insert and delete operations in stack data structures are explored, focusing on the top-end interactions and the concept of last-in-first-out in stack operations.

Explanation of the top of the stack concept, highlighting its nature within data structures and the elements linked to the top of the stack.

Detailed explanation of direct data access points, emphasizing the limitations and precautions necessary to avoid data structure disruptions.

Explains the implementation of static and dynamic nature in arrays and linked lists, highlighting the considerations required for insert and delete operations.

Describes the concept of push and pop operations in a stack, illustrating the process with examples of inserting and deleting elements in the stack.

Explains the stack overflow condition that occurs when the stack is full, preventing further add operations and the implications of this condition.

Discusses the stack underflow condition that arises when attempting pop or insert operations on an empty stack, highlighting its significance in stack operations.

Illustrates the process of assigning values in stack operations, including scenarios such as pushing elements onto the top of the stack and checking the stack size and indices.

Explains how the stack handle initial conditions when no elements are present, leading to a completely empty stack and the assignment of values in this scenario.

Discusses the final checks and assignments in a stack, particularly when the stack has no elements initially, leading to the assignment of values to resolve the empty stack condition.

Explanation of the implementation of stack data structure using examples to demonstrate stack operations like push, pop, and decrement.

Understanding the evaluation of prefix and postfix expressions and the logic behind operator positioning in infix, prefix, and postfix notations.

Detailed explanation of infix, prefix, and postfix notations, highlighting the operator position and evaluation strategies in each notation.

The process of converting infix expressions to prefix and postfix notations, emphasizing the order of operations and operator positioning differences.

Explains different methods such as basic tree structure, stack implementation, and manual methods for expression evaluation. Recommends using the tree construction method via a binary tree for converting expressions to prefix or postfix notation.

Demonstrates how to check the validity of an expression by generating trees and verifying if they are valid expressions for infix, prefix, and postfix notation.

Explains the traversal process from top to bottom, left to right, and evaluates nodes for respective expressions in infix, prefix, and postfix notations.

Detailed explanation of generating postfix expressions using a tree structure and evaluating the operands and operators position wise for correct expression formation.

Shows the evaluation process for postfix expressions by focusing on the last operands and generating the final expression through proper traversal and operand positioning.

Discusses the process of generating prefix expressions using a tree structure and determining the correct placement of operators and operands for a valid expression.

Illustrates the evaluation procedure for prefix expressions by emphasizing the operator's position and evaluating expressions from left to right, top to bottom.

Demonstrates the steps to construct postfix expressions by evaluating operands and operators in reverse order, focusing on the last operands and operators for expression generation.

Continues the explanation of evaluating postfix expressions by emphasizing the last operands and operators position wise for correct evaluation and expression generation.

Explaining how stack fund works in traversing the tree of pre-order and post-order orders. Emphasizes the importance of following the same fund for better and quicker correct answers.

Discussing the operator rules and precedence in relation to left to right or right to left associative properties. Explaining the concept through examples and the importance of respecting rules like associativity.

Explaining the precedence of multiplication, division, addition, and subtraction operators. Discussing the order to follow when encountering these operators and the importance of understanding power dynamics in operations.

Explaining the interaction between operators in a stack and how to handle different operations like multiplication and addition in a stack. Illustrating the process step-by-step for clarity and comprehension.

Discussing the hierarchy of operators in a stack and the order to follow when dealing with multiple operators. Explaining the significance of operator dominance and the decision-making process in pushing operators in a stack.

Guiding on finalizing stack operations by understanding the sequence of operators and converting them into post-fix notation. Emphasizing the simplicity and basic rules to follow in applying post-fix operations through stacks.

Introducing a manual method for converting infix operations to post-fix notation. Explaining the simple steps involved in sharing and managing operations through stacks for post-fix application.

Demonstrating how to solve priority operations involving multiplication, addition, and precedence. Explaining the operator behavior and sequence to follow for accurate results in complex operations.

Explaining how to handle operator precedence in stack operations. Illustrating the process of solving operations sequentially and considering the left to right or right to left associative properties for accurate calculations.

Discussing the importance of operator associativity in determining the order of operations in stack calculations. Clarifying the decision-making process regarding operator associativity for efficient and accurate results.

Explanation of how postfix expressions work and the process of evaluating them using operators and operands.

Demonstration of generating trees for arithmetic expressions and evaluating them step by step.

Explanation of how to handle operators in trees by assigning precedence and associativity to operators.

Detailed process of performing tree operations, focusing on multiplication, division, addition, and subtraction.

Traversal and evaluation techniques for trees, emphasizing left-to-right and right-to-left traversal methods.

Understanding how precedence and associativity play a role in evaluating arithmetic expressions with operators.

Demonstration of evaluating arithmetic expressions using trees and following the precedence rules.

Explanation of evaluating arithmetic expressions using a stack and step-by-step demonstration of the evaluation process.

Explanation of how operators are arranged in a stack from left to right in postfix notation.

Demonstration of handling the plus operator in the postfix notation of an expression.

Illustration of the evaluation process of multiplication operations in postfix notation.

Explanation of the correct order of operations for postfix expressions.

Process of evaluating expressions using postfix notation and stack operations.

Discussion on the correct order of evaluation in postfix expressions and their corresponding results.

Conversion of infix expressions to postfix and evaluating them using stack operations.

Explanation of the first-in-first-out principle in postfix expressions and its significance.

Illustration of queue concepts in postfix expressions and their application in evaluating expressions.

Explanation of the fundamental structure of queues and their relevance in postfix expression evaluation.

Explanation of terms like front end, rear end, insert operation, delete operation, and how they are used in stack operations.

Details on how insert and delete operations work in a stack, considering rules like inserting from one end and deleting from the other end.

Analogizing stack operations with entering and exiting through different gates on a bus, explaining front gate for entry and rear gate for exit.

Explanation of initialization process, conditions like overflow and underflow, and understanding the behavior of stack elements during insert and delete operations.

Describing overflow and underflow conditions in a stack, where overflow occurs when the stack is full and underflow when trying to delete from an empty stack.

Illustrating the behavior of front and rear points during insertion in a stack, emphasizing their positions after each insertion operation.

Understanding various conditions and scenarios during the insertion process, like overflow and underflow, and analyzing the point of time during insertion.

Explaining the behavior of the rear point and conditions like overflow based on the index positions during insertion in a stack.

Discussing the underflow condition in a stack, where attempting to remove elements from an empty stack results in underflow, leading to specific conditions in front and rear points.

Explaining the propagation of front and rear points in a stack, depicting how the front propagates forward while the rear moves backward in various scenarios.

Explaining the concept of inserting elements in a respective stack and the behavior of rear and front ends during insert and delete operations.

Discussions on the increment behavior during insert and delete operations, along with insights on how the front and rear ends are affected.

Demonstrating the process of deleting elements from the front end and observing the increment behavior in the respective stack.

Exploring memory utilization challenges and introducing the concept of circular queue to overcome overflow issues during insertions.

Implementing circular queue logic to manage insertions efficiently and discussing the limitations of standard queues in certain scenarios.

Explaining the conversion of queue elements using the modulo operator to handle circular queue implementations effectively.

Illustrating the application of the modulo operator in circular queue operations to address index manipulation and data storage.

Demonstrating how the modulo operator facilitates operations in circular queues and its significance in managing indices and array elements effectively.

Defining the circular queue logic for inserting elements and explaining the role of the rear and front ends in the queue operations.

Discussing the transitions between rear and front ends during queue operations and emphasizing the importance of proper implementation for efficient data management.

The speaker discusses pointer manipulation within data structures, focusing on front and rear pointers and their respective implementations.

The concept of implementing a rear pointer in data structures is explained, highlighting the increments and updates required in the structure.

Details on insertion and deletion operations within linked lists, emphasizing the steps involved in manipulating pointers and updating node addresses.

Explanation on traversing through data structures and assigning addresses to nodes, showcasing the process of address assignment in a practical scenario.

The speaker demonstrates incremental updates and address assignments within linked lists, illustrating how pointers are adjusted for effective data structure management.

Introduction to tree data structures, discussing the hierarchical organization of data with root, leaf, and parent nodes.

Exploration of tree data structures, emphasizing non-linear data representation with multiple levels and terminologies like nodes, leaf nodes, and parent nodes.

Detailed explanation of parent-child relationships in tree structures, focusing on the hierarchy of nodes and the concept of leaf nodes at the bottom of the tree.

Discussion on tree traversal techniques and node classification, distinguishing between parent nodes, leaf nodes, and the traversal process.

Explanation of the relationship between parent and child nodes in tree structures, showcasing how nodes are interconnected within the tree hierarchy.

Explanation of non-leaf nodes in a tree and how to define paths using starting and ending nodes. The speaker discusses the path from one node to another, illustrating the concept of paths in trees.

Explanation of the degree of nodes in a tree, discussing how to determine the number of children for each node. The speaker explains the concept of degrees from one node to another within a tree structure.

Discussion on ancestor and descendant nodes in a tree, explaining how to identify respective nodes in terms of their relationships within the tree hierarchy.

Explanation of the concepts of depth and height of nodes in a tree structure. The speaker elaborates on finding the depth and height of nodes starting from the root or leaf nodes in a tree.

Explanation of sibling nodes in a tree and how to distinguish them based on parent-child relationships. The speaker discusses the concept of heights in sibling nodes.

Discussion on finding the minimum depth and maximum height of nodes in a tree structure. The speaker explains the process of determining depths and heights in a binary tree.

The speaker explains the concept of binary trees, where nodes have a maximum of two children. Different scenarios like binary trees with one, two, or zero children are discussed, emphasizing the structure and characteristics of binary trees.

Discussing the popular question of determining the maximum number of nodes in a binary tree for a given height. Explains how the number of nodes varies in binary trees depending on the height, highlighting the relationship between height and the number of nodes.

Explaining the concept of levels in binary trees where level zero represents the root node and subsequent levels branch out to child nodes. The equal levels principle is mentioned, emphasizing the level structure within binary trees.

A detailed discussion on calculating the number of nodes in binary trees based on specific heights. Explains the process of determining the total number of nodes in binary trees through examples and mathematical calculations.

Discussing variations in binary trees such as full binary trees, complete binary trees, and how to determine if a tree is a complete binary tree or not based on the number of children in each node. Explains the filing direction in trees for proper classification.

Exploration of the concept of complete binary trees, highlighting the specific filling direction from left to right for proper tree classification. Differentiates between complete and incomplete binary trees based on the filling method.

Delving into the complexity of complete binary trees and the significance of considering minimum to minimum node conditions in these trees. Discusses the calculation of total nodes in complete binary trees based on height and specific formulas.

Explaining the characteristics of full binary trees where each node has either zero or two children, discussing scenarios of determining full binary trees based on the number of children in each node. Emphasizes the structural completeness of full binary trees.

Exploring perfect binary trees and the concept of proper filling direction in the tree structure. Discusses the correct filling sequence and the implications of incorrect filling in binary tree classification.

Explanation of how binary trees work and the importance of having the correct structure with respect to child nodes.

Exploration of tree traversal and the various types of orders followed such as pre-order, in-order, and post-order traversal.

Discussion on the traversal of binary trees, focusing on pre-order, in-order, and post-order traversal methods.

Explanation of the order in tree traversal, including understanding pre-order, in-order, and post-order traversal sequences.

Explanation of how child nodes are ordered in tree traversal, highlighting the importance of following the correct sequence for child nodes.

Illustration of how node values are determined in tree traversal, emphasizing the significance of assigning values based on the order of traversal.

Discussion on the ordering of nodes during traversal, including the value assignment process for each node in tree traversal.

Explanation of the approach to traversal, detailing the sequence and value assignment for nodes during tree traversal.

Demonstration of continuous node traversal, focusing on the systematic progression of visiting and assigning values to nodes.

Illustration of sequential node traversal, emphasizing the step-by-step process of visiting and assigning values to nodes in a specified order.

Explanation of how to implement binary trees using pointers to left and right children of each node.

Details about maintaining parent-child relationships in a binary tree structure including pointers to left and right children of each node.

Demonstration of identifying index numbers in a binary tree and determining left and right children based on the node's position.

Explanation of node data representation in a binary tree structure, including center position, left pointer to left child, and right pointer to right child.

Explanation of how data pointers work in a binary tree structure, pointing to specific child nodes.

Considerations for implementing a binary tree, including maintaining a structure of pointers to left and right children of each node.

Instructions on handling child nodes when a node does not have children, guiding pointers to other nodes.

Explanation of how binary search trees work and the rules to follow when navigating them.

Illustration of traversing binary search trees and the order to follow to get sorted numbers.

Discussion on the advantages of using binary search trees for searching and sorting operations.

How binary search trees optimize searching operations and make searching easier and more efficient.

Importance of following binary search tree rules while searching in order to save time and make searching convenient.

Comparing elements in a binary search tree and making decisions based on the BST rules.

Explaining the ease of searching based on the rules and conventions of binary search trees.

Navigating a binary search tree while adhering to the rules for efficient searching and decision-making.

Verifying the BST rules through comparison and decision-making while navigating the tree.

Transitioning to the next steps after understanding and practicing the basics of binary search trees.

Discussion on the importance of comparing nodes during editing to avoid errors and the significance of editing from the top down for accuracy.

Explanation of node deletion in an AVL tree, including considerations for deleting a leaf node and the impact of deletion on the tree structure.

Guidance on adjusting the hierarchy of nodes in a tree structure, focusing on the proper sequence and replacement of nodes for maintaining the tree integrity.

A detailed walkthrough of the process of reordering nodes in a tree structure, emphasizing the importance of maintaining the order of nodes for stability and functionality.

Explanation of the transformation process in an AVL tree, including self-balancing factors and the concept of self-balancing to ensure stability and accuracy in the tree structure.

Discussion on AVL trees as a balanced version of binary search trees, highlighting the self-balancing property and its significance in tree operations.

Explanation of the rules and principles governing AVL trees, focusing on self-balancing features and factors that contribute to the tree's stability and performance.

Explanation of the concept of node balance factor and its importance in AVL trees.

Demonstration of how node rotation can correct imbalances in AVL trees using a specific scenario.

Explanation of how inserting nodes can impact the balance factor of nodes in AVL trees, leading to imbalances that need to be corrected through rotations.

Illustration of how node rotation can be used to solve imbalances caused by node insertions in AVL trees.

Exploration of a more complex scenario involving multiple rotations to address imbalance issues in AVL trees.

Final outcome of node rotations to resolve imbalances in AVL trees, ensuring balanced nodes and tree structure.

Recap of the importance of node rotations in maintaining balance in AVL trees and resolving imbalance issues efficiently.

Discusses problems faced while adding nodes to the right and left sub-trees, highlighting the need for proper rotation to maintain balance in the tree structure.

Explains the process of inserting nodes and analyzing the impact on the balance factor, emphasizing the importance of correct rotation to resolve balancing issues in the tree.

Addresses issues encountered during node insertion and rotation, demonstrating the impact on the structure's balance and the need for proper rotations to maintain balance in the binary search tree.

Explains the concept of balancing in tree structures and the significance of performing correct rotations to ensure proper balance in the binary search tree.

Demonstrates the application of rotation techniques to address balance issues in the binary search tree and ensure the correct alignment of nodes for balanced structure and proper functioning.

Discusses strategies for inserting nodes and analyzing the impact on balance, emphasizing the importance of correct rotations to maintain balance and proper structure in the binary search tree.

Addresses imbalance issues in tree structures, explaining the process of identifying and correcting imbalance through proper rotations and node adjustments in the binary search tree.

Focuses on ensuring balance in tree structures by performing appropriate rotations and adjustments to maintain the structural integrity and balance in the binary search tree.

Discusses resolving node rotation issues by applying the correct rotation techniques to maintain balance and alignment in the binary search tree for optimal performance and structure integrity.

Concludes the discussion on rotation solutions by emphasizing the importance of proper rotations and adjustments to resolve imbalance and ensure the balanced structure and effective functioning of the binary search tree.

Discussion on the impact of inserting nodes after positions 14 and 18 in a tree structure, highlighting potential problems and suggesting solutions through rotations and adjustments.

Details the implementation process of resolving node insertion problems in a tree structure, emphasizing the importance of following specific steps to address balance factors and ensure correct tree structure.

Explains the optimization of node placement and rotation techniques to maintain balance factors and resolve issues related to left and right node insertion in a tree structure.

Guidance on deleting nodes in a tree structure and replacing them systematically to maintain balance factors and ensure the correct order of nodes for a balanced tree structure.

Instructions on checking balance factors after deleting nodes and verifying the balance and order of nodes in a tree structure to address any imbalance or discrepancy.

The concept of completing binary trees and understanding heap properties like min heap and max heap in detail.

Explaining the properties of parent-child relationships such as min-heap and max-heap values in complete binary trees.

Discussing the process of converting binary trees by applying heapify to achieve the min-heap or max-heap requirements.

Explaining the properties of max heap and min heap, showcasing how the values are set to satisfy the respective properties.

Demonstrating the process of navigating through levels in binary trees and applying max heap and min heap properties to satisfy the requirements.

Resolving issues by swapping nodes to ensure the satisfaction of max heap and min heap properties at each level in the tree.

Illustrating the process of generating a max heap by traversing from the lowest to the highest point and checking the satisfaction of properties at each level.

Inspecting the satisfaction of max heap properties by ensuring that child values are greater than parent values at each level.

Demonstrating the process of setting and checking the satisfaction of max heap properties at each level in the tree to generate a max heap.

Discusses the decision-making process in a scenario where two values are compared to determine the maximum value to be chosen.

Explains the problem encountered when altering values in the decision-making process and the need to carefully choose the maximum value.

Exploration of handling complex scenarios where values need to be adjusted to fulfill conditions for minimum and maximum values.

Explanation of threaded binary tree structure and the considerations for threading nodes in single and double threading.

Discussion on the in-order processor in a tree structure and the significance of threaded binary tree in data structuring.

Introduction and discussion on non-linear data structures like graphs, focusing on the presence of cycles and loops in node relationships.

Explanation of graph structures, highlighting the possibility of creating cycles and improper loops leading to undesirable cycle formation.

Detailed discussion on cycles in graphs and the potential risks associated with cycle formation in graph structures.

Exploration of concerns in non-linear data structures, emphasizing the formation of cycles and loops, which can be improper and need careful consideration.

Discusses the creation of circular loops in graphs, pointing out the risks and issues related to improper loop formations in graph structures.

Explanation of directed and undirected graphs, including the concept of edges connecting nodes, and the difference between directed and undirected graphs.

Introduction to fundamental concepts in graph theory such as types of graphs (directed and undirected), basic graph edges, and how nodes are connected.

Discussion on weighted graphs, significance of weights in edges, importance in algorithms for finding shortest paths, and the notion of costs in graphs.

Explanation of complete graphs where all nodes are connected to each other with edges forming pairs, and how it differs from simple graphs.

Definition and characteristics of null graphs where vertices are mentioned without any edges, representing the absence of connections between nodes.

Explanation of adjacency matrix used to represent graphs, its application in graph representation, and understanding the structure of a graph through adjacency matrices.

Detailed discussion on directed graphs, edges indicating directionality between vertices, and the significance of representing graph structures using adjacency matrices.

Introduction to multigraphs, highlighting their difference from simple graphs by having multiple edges between pairs of vertices, and how they are represented using adjacency matrices.

Explanation of singleton graphs with only vertices represented, without any edges, emphasizing the simplicity and basic structure of such graphs.

Explaining how to denote the absence of edges in a graph and how it is represented visually using a graph matrix.

Discussing examples of direct graphs with edges connecting specific nodes and the concept of adjacency matrices.

Explaining graph traversal using algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS) and the implementation details of BFS and DFS.

Addressing the generation of sparse matrices in graphs and the need to minimize memory usage by reducing unnecessary entries in matrices.

Explaining techniques for graph traversal such as BFS (Breadth-First Search) and DFS (Depth-First Search) and how to implement these traversal methods effectively.

Detailing the implementation of Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in graph traversal, focusing on using stacks for DFS and queues for BFS.

Exploring challenges in graph traversal methods like BFS and DFS, discussing the differences and nuances in their implementations.

Explaining the process of connecting and disconnecting nodes in a sequence, determining visited and unvisited nodes for manipulation.

Demonstrating the removal and addition of nodes in a sequence based on their connections and visited status for effective sequence management.

Optimizing the sequence by strategically removing and adding nodes to efficiently navigate through the given set of connected nodes.

Analyzing and organizing nodes in a sequential order by considering their connections to enhance node manipulation efficiency.

Ordering nodes systematically by removing and adding them in a strategic manner to streamline the sequence for better node management.

Evaluating node connections to determine the sequence of manipulation for efficient node handling and analysis.

Optimizing connectivity by considering and manipulating nodes based on their connections to enhance sequence organization and manipulation.

Strategizing the manipulation of nodes by pushing and popping them in a predefined sequence to achieve an organized node structure for efficient processing.

Demonstrating the process of popping and pushing nodes in a sequence to manage node connections effectively and ensure optimal node arrangement for processing.

Streamlining the sequence by popping and pushing nodes systematically to maintain a well-organized and optimized node sequence for enhanced processing efficiency.

The concept of a hash function and its importance in optimizing searching operations. Hash functions help in speeding up searching processes by reducing the time required for searching while utilizing additional space.

Explaining the application of hash functions through a real-life example involving a chase scenario to catch a thief. The use of hash functions in identifying locations and optimizing search operations is highlighted through this story.

Discussing the importance of maintaining simplicity in hash functions to ensure ease of computation and fast processing speed. Complexity should be avoided in hash functions to maintain efficiency in search operations.

Explaining the collision rate in hash functions and emphasizing the need for a low collision rate to ensure accurate computation and fast processing. Collision incidents in hash functions are compared to scenarios like booking a hotel room to illustrate the concept.

Highlighting the criteria for an optimal hash function, including fast computation, ease of understanding, and efficiency in computation. The importance of selecting a hash function that provides fast and accurate results is emphasized.

Explaining the fundamentals of hash tables, including allocation of locations and dealing with collision scenarios. The analogy of booking a hotel room with pre-allocated spaces is used to elucidate the concept of collision in hash functions.

Discussing the effective application of hash functions in optimizing computation speed and ensuring easy understanding. The role of hash functions in reducing computational complexity and speeding up search operations is emphasized.

Explanation of implementing a respective hash function with variations in handling table size and key usage.

Details on hash table sizing including handling space allocation and the importance of key placement.

Guidance on implementing a respective hash function for inserting and searching keys efficiently in a hash table.

Explanation of the mid square method for hash table function implementation and key storage techniques.

Introduction to the folding method for storing and manipulating large numbers efficiently in hash functions.

Discussion on collision issues and examples to understand collision resolution in hash functions.

Explaining the importance of choosing a respectful and ideal function for collision resolution in programming. Discussing the different techniques for resolving collisions in hash tables.

Explaining the concept of open hashing and its techniques such as linear probing, quadratic probing, and double hashing. Discussing how open hashing handles collisions in hash tables.

Explaining the chaining technique in hash tables to resolve collisions. Discussing the categories and methods used in resolving collisions through distribution and category identification.

Introducing linear probing as a technique for searching and optimizing hashing in hash tables. Explaining the process of linear probing to efficiently handle collisions in the hash table.

Explaining the challenges and disadvantages of linear probing in handling collisions. Discussing the concept of searching optimization and the drawbacks of linear probing in certain scenarios.

Discussing the disadvantages of venturing outside the hash table for problem-solving. Explaining the concept of open hashing and its implications on search optimization in programming.

Explaining the process of problem-solving with linear probing method and how collisions are handled.

Demonstrating how probing values are incremented when collisions occur and the impact of collisions on the probing values.

Introducing the quadratic probing technique and how it differs from linear probing in handling collisions.

Detailing the implementation of quadratic probing method and its impact on problem-solving in a linear probing scenario.

Explaining a modification involving quadratic probing technique and its relevance in the problem-solving process.

Discussing the challenges of linear probing related to clustering and how it impacts search time and efficiency.

Exploring the concept of quadratic probing as the second last topic and its application in problem-solving scenarios.

Practical demonstration of implementing quadratic probing technique and verifying its effectiveness in solving problems efficiently.

Exploring the concept of square patterns where step size affects the increment of foot placement leading to long jumps.

Illustrating collisions and increment of index values in a coding scenario where collisions result in incrementing the value of i in a loop.

Discussing clustering and hashing techniques, focusing on primary and secondary clustering and their impact on patterns in coding.

Explaining the process of implementing hash functions in coding, including combining multiple hash functions to handle collisions effectively.

Explaining the concept of modulus and remainder in the context of a hash function with examples.

Discussing how collisions are handled in hash tables using index values and avoiding collisions through proper value allocation.

Demonstrating the process of incrementing values in double hashing and reflecting the changes in the hash table.

Wrapping up the reminder calculation in hashing and addressing collision issues to optimize the hash function.

Exploring efficient techniques to solve collision problems, focusing on quadratic probing and linear probing methods in hashing.

Ending the discussion with closing remarks on the hash functions and encouraging viewers to like, share, and subscribe to the channel for more valuable content.

Q: What is the concept of binary search trees?

A: Binary search trees are hierarchical data structures where each node has at most two children, with nodes containing values smaller than the parent on the left and larger values on the right, facilitating efficient searching and sorting operations.

Q: What is the purpose of hash functions in data structures?

A: Hash functions are utilized to optimize search operations in data structures by reducing search time through efficient computation and utilizing additional space smartly.

Q: How are cycles and loops in non-linear data structures like graphs characterized?

A: Cycles and loops in graph structures are characterized by their potential to create circular relationships between nodes, leading to improper loop formations and undesirable cycle generation.

Q: Explain the concept of linear probing in hash tables.

A: Linear probing is a collision resolution technique in hash tables where, upon encountering a collision, the algorithm searches for the next available slot linearly until an empty spot is found, ensuring efficient handling of collisions.

Q: What is the significance of traversal techniques like Breadth-First Search (BFS) and Depth-First Search (DFS) in graph theory?

A: Traversal techniques like BFS and DFS play a crucial role in graph theory by enabling systematic exploration of nodes and edges, helping to discover and process information efficiently through different traversal strategies.

Q: How do AVL trees ensure self-balancing in their structures?

A: AVL trees maintain self-balancing properties by utilizing rotation techniques to correct imbalances caused by node insertions, ensuring that the tree structure remains stabilized and performs optimally in terms of balance and efficiency.

Q: What is the defining characteristic of a complete binary tree?

A: A complete binary tree is structured such that all nodes are connected with edges forming pairs, ensuring a complete and balanced tree structure without any unfilled nodes.

Q: Explain the concept of parent-child relationships in tree structures like binary search trees.

A: In tree structures like binary search trees, parent-child relationships define the connections between nodes, where the parent node branches out to child nodes based on certain value criteria for efficient searching and sorting operations.

Q: How are hash tables optimized for search operations using hash functions?

A: Hash tables leverage hash functions to accelerate search operations by allocating locations for key storage and handling collisions through strategic hashing techniques, ensuring efficient and effective search processes.

Q: What role does collision resolution play in the effective implementation of hash tables?

A: Collision resolution is vital in hash tables as it addresses scenarios where multiple keys are mapped to the same location, ensuring accurate computation and fast processing by utilizing methods like open hashing and probing techniques.

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Data Structure in Java in Hindi

Learn key concepts like Array, LinkedList, Stack, and Queue via Java Data Structure Course in Hindi

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What you learn in Data Structure in Java in Hindi ?

About this Free Certificate Course

Data structure deals with structuring or organizing data in the computer system so that the data can be used in an effective manner. For example, linearly storing data - Array or Linked Lists, storing data one upon the other - Stack, Hierarchical manner - Trees, and Connecting Nodes - Graph. In this Data Structures in Java course in Hindi, you will learn and understand different data structures in Hindi, principles like Stack and Queue, and understand hierarchical data structures like Binary Tree, Binary Search Tree, and Graphs. 

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• Course Outline

Hive is a data warehouse used to support interaction between the user and HDFS. This course will give you a demonstration using sample problem statements for your better understanding.

A linked list is a linear type of data structure that comes in handy while solving complex problems. This module gives a brief on the linked list along with discussing its advantages, disadvantages, and applications. You also have a demonstration on a linked list with the help of a code example.

Stack comes under a linear data structure and is known for its FILO/LIFO property. The module focuses on introducing you to stack and goes through its several advantages, disadvantages, and applications. You will also learn about its demonstration using an array with the help of a code example.

A queue is a linear form of data structure and follows the FIFO/LILO property. The module starts by giving you a brief on queue, its advantages, disadvantages, and applications. Lastly, you will have a demonstration of the queue using an array with the help of a code example.

This comprehensive module on Organizational Behaviour, covers the concept, importance, elements, levels, types, and objectives of the field. It provides a deep understanding of the subject to enhance interpersonal and communication skills and create a positive work environment.

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Frequently Asked Questions

What are data structures in Java?

A data structure is a mechanism for storing data, keeping it structured, and allowing for simple adjustment and access. Simply put, a data structure is a set of data values, their relationships, and the operations or functions that may be performed on them. 

Data systems are made up of linked data and processes that can be performed on them. You'll learn about data structures all the time, whether you're new to computer programming or have been doing it for a long time. If you wish to become a decent programmer or simply have a programme that can perform simple operations, you must first understand data structures.

Programmes = Algorithms + data structures.

There is a great deal to learn about data systems. You'll want to understand what they are, why they're important, and which ones are the most important. This knowledge will benefit you if you're concerned about interview questions or just want to lay a strong basis for the rest of your coding education. The bottom line is that if you want to be a good programmer, you must first learn how to program Data Structures. In this course, you will learn, how to program all data structures in Java.

What are the types of data structures?

Depending on the form of data and what you want to do about it, data must be structured in a variety of ways. You must consider the various types of data structures and how they function in order to efficiently manage data and construct a programme. Data structures are often divided into linear and non-linear data structures in a general context. The following are some of the most popular data structures.

Linear Data Structures

• Linked Lists
• Hash Tables

Non-Linear data structures

Which data structure is best for Java?

Don't equate the data structure to the programming language you're using, such as java or Python.

There are several ways for storing data in a data system... that each has advantages and disadvantages, so you must evaluate and consider the time complexity and auxiliary space before making a decision based on the problem.

The fastest is determined by the time complexity.

Any data structure has four operations. Create, read, edit, and erase are all options.

Each data structure can succeed at some operations while failing at others.

O(1) is the quickest in the graph.

Is Java good for data structure?

It is preferable to study data structures in Java. The explanation is that... Java is a slow language. As a result, you'll attempt to improve the data structure in order to improve efficiency, and you'll learn more. The second thing to do is to continue to use courses to enforce them. Your morale will rise as a result of getting some professional taste.

And also, java has tons of build-in methods that simulate the logic of various operations on the data structure. Therefore, it is beginner-friendly to learn data structures in Java.

What is data structure and algorithm?

Data Structure

The data structure is a method of storing and organizing data in such a manner that we can effectively execute operations on it. Data Structures are used to organize and store data by making data components in terms of some relationship. For eg, we have information about a player named "Virat" who is 26 years old. The data types "Virat" and "26" are also String data types.

We will organize this information into a record called Match, which will include both the player's name and age. As a data system, we can now compile and archive player information in a file or folder. For instance, "Dhoni" is 30, "Gambhir" is 31, and "Sehwag" is 33.

Data Structures, in basic terms, are structures that are designed to contain structured data such that different operations can be done on it quickly. It reflects the data information that needs to be stored in memory. It should be planned and applied in a way that minimizes complexity while increasing performance.

An algorithm is a finite set of instructions or logic that must be written in a certain order to complete a predefined assignment. An algorithm is not a complete program or code; rather, it is the central logic (solution) of a query, which can be represented in a flowchart or as a high-level definition in pseudocode.

The following properties must be met for any algorithm:

External Inputs- The algorithm can have 0 or more external inputs.

Output - At the very least, one output should be achieved.

Definiteness- Each step of the algorithm should be clearly described and simple.

Finiteness- A finite number of steps should be present in the algorithm.

Correctness- Each step of the algorithm must produce a valid result.

How is data structure used in real life?

• You need to store interpersonal organization "takes care of". You don't have the foggiest idea about the size, and things may be powerfully added. - connected rundown or Hash table 
• You need to store fix/re-try activities in a word processor. – stack (utilize 2 stacks for including re-try activity) 
• You need to assess an articulation (i.e., parse). – stack or tree 
• You need to store the fellowship data on an informal communication site. I.e., who is companions with who – chart 
• You need to store a picture (1000 by 1000 pixels) as a bitmap. – ArrayList(java) or 2D cluster 
• To carry out printer spoilers so that positions can be imprinted in the request for their appearance. – Stack
• To execute back usefulness in the web program. – Stack 
• To store the potential moves in a chess game. – Tree 
• To store a bunch of fixed watchwords which are referred to every now and again. – dynamic programming or hash table 
• To store the client request data in a drive-in burger place. (Clients continue coming and they need to get their right foot at the installment/food assortment window.) – line/min Need Queue(if the request is basic and takes less time) 
• To store the family history data of natural species. – Tree

What are the most important data structures?

The importance of various data structures varies from application to application. But to be specific the following data structure must be known by each programmer.

Because trees are used in :

• Folders in the Operating system:
• in windows go to the command line and type tree. 
• HTML Document Object Model (DOM):
• Network Routing 
• Syntax Tree in Compiler 
• Autocorrect and spell checker 
• Next Move in games
• In an Artificial intelligence game (opponent is CPU), the next moves are stored using a tree data structure

Linked List

Because Linked List is used in :

• Picture viewer – Prior and next photos are connected, so the next and previous buttons can be used to access them.
• Previous and next page in a web browser – Since they are interconnected as a linked list, we can view previous and next urls searched in a web browser by clicking the back and next buttons.
• Music Player – The previous and next songs in the music player are related. You may perform songs from the beginning or the end.

Because Arrays are used in : 

• Creation of Stack
• Creation of Queue
• Creation of Hashtable
• Message Digest. A message digest is a cryptographic hash function containing a string of digits created by a one-way hashing formula.
• File System. 
• Password Verification
• Pattern Matching. 
• Programming Language. 
• Suggested Problems to solve in Hashing.

Heap is a way of optimization of complex algorithms that depend on finding the greatest to the smallest values in the Heap.

• Graph Algorithm:- Dijkstra’s Algorithm
• Priority Queue and many more

Will I get a certificate after completing this Data Structure in Java Hindi free course?

Yes, you will get a certificate of completion for Data Structure in Java Hindi after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.

How much does this Data Structure in Java Hindi course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Data Structure in Java Hindi can get started with this course.

Is there any limit on how many times I can take this free course?

Once you enroll in the Data Structure in Java Hindi course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Data Structure in Java Hindi course?

Great Learning Academy provides this Data Structure in Java Hindi course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

What are the steps to enroll in this Data Structure in Java Hindi course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.

Will I have lifetime access to this free Data Structure in Java Hindi course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 

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EnjoyMathematics

Steps of Problem Solving in Data Structures and Algorithms

Every solution starts with a strategy, and an algorithm is a strategy for solving a coding problem. So, we must learn to design an efficient algorithm and translate this 'algorithm' into the correct code to get the job done.

But there are many coding problems available in data structures and algorithms, and most of the time, these problems are new to us. So as programmers, we need to develop ourselves as confident problem-solvers who are not intimidated by the difficulty of the given problem. 

Our long-term goal should be simple: Learn to design correct and efficient code within a given time. As we practice more and more, we will gain experience in problem-solving, and our work will become easier. Here are some essential skills that we should practice for every DSA problem:

• Developing an approach to understanding the problem
• Thinking of a correct basic solution
• Designing step-by-step pseudocode solutions
• Analyzing the efficiency of a solution
• Optimizing the solution further
• Transforming pseudocode into correct code

Now, the critical question would be: Is there a well-defined, guided strategy to approach and solve a coding problem? If yes, then what are the critical steps? Let's think and explore!

Step 1: Understanding the problem

Solving a problem requires a clear understanding of the problem. Unfortunately, sometimes we read only the first few lines and assume the rest of the problem or ignore this step because we have seen something similar in the past. We should view these as unfair practices and develop a clear approach to understanding problems.

During problem-solving, every small detail can help us design an efficient solution. Sometimes, a small change in the question can alter the solution approach. Taking extra time to understand the problem will give us more confidence later on. The fact is, we never want to realize halfway through that we misunderstood the problem.

It doesn't matter if we have encountered a question before or not; we should read the question several times. So, take a paper and write down everything while going through the problem. Exploring some examples will also help us clarify how many cases our algorithm can handle and the possible input-output patterns. We should also explore scenarios for large input, edge cases, and invalid input.

Sometimes, it is common for problem descriptions to suffer from these types of deficiencies:

• The problem description may rely on undefined assumptions
• The problem description may be ambiguous or incomplete
• The problem description may have various contradictions.

These deficiencies may be due to the abstract explanation of the problem description in our natural languages. So, it is our responsibility to identify such deficiencies and work with the interviewer or problem provider to clarify them. We should start by seeking answers to the following questions:

• What are the inputs and outputs?
• What type of data is available?
• What is the size or scale of the input?
• How is the data stored? What is the data structure?
• Are there any special conditions or orders in the data?
• What rules exist for working with the data?

Step 2: Thinking of a correct basic solution

The best approach would be to think of a correct solution that comes immediately to our mind. It does not matter even if it is an inefficient approach. Having a correct and inefficient answer is much better than an incorrect solution or significant delay in finding the solution. This could help us in so many ways:

• Help us to build good confidence or motivation at the start.
• Provide an excellent point to start a conversation with the interviewer.
• Sometimes, it provides a hint to improve efficiency by reducing some loops, removing some intermediate steps, or performing some operations efficiently.

Here are some examples of brute force patterns: three nested loops, two nested loops, solution using extra memory, solution using sorting, double traversal in the binary tree, considering all sub-arrays or substrings, exhaustive search, etc.

After thinking and communicating the brute force idea, the interviewer may ask for its time and space complexity. We need to work on paper, analyze each critical operation, and write it in the form of Big-O notation. Clear conceptual idea of time and space complexity analysis is essential at this stage.

Step 3: Designing efficient solution with pseudocode

This is a stage to use the best experience of DSA problem-solving and apply various problem-solving strategies . One practical truth is: moving from a basic algorithm to the most efficient algorithm is a little difficult in a single step. Each time, we need to optimize the previous algorithm and stop when there is no further optimization possible. Revisiting the problem description and looking for some additional information can help a lot in further optimization. For example:

• If the input array is sorted or nearly sorted, we can apply optimized algorithms such as a single loop, two-pointer approach, or binary search.
• If we need to find a subarray of size k, we can use the sliding window technique, which involves maintaining a window of size k over the array and sliding it over the elements to find the desired subarray.
• When searching is a critical operation, we can use optimized search algorithms or data structures like binary search, BST, or hash table.
• For optimization problems, we can consider divide and conquer, dynamic programming, or greedy algorithm approaches.
• If we need to find a solution with a given constraint, we can use backtracking.
• When working with string data, direct address tables, hash tables, or trie data structures can be useful.
• To frequently access and process max or min elements, we can use a priority queue or heap data structure.
• For dictionary operations such as insert, search, and delete, we can use hash tables or BST.
• If we need to perform both dictionary and priority queue operations, a BST may be useful.
• For range query operations such as range max, range min, or range sum, we can use data structures like segment trees or Fenwick trees.
• To process binary tree data level by level, BFS or level-order traversal can be used.

The idea would be simple: we should learn the use case of efficient problem-solving patterns on various data structures. Continuously thinking, analyzing, and looking for a better solution is the core idea. 

Here are some best examples of problems where several levels of optimisations are feasible. Practicing such types of coding questions helps a lot in building confidence.

Find equilibrium index of an array

• Using nested loops: Time = O(n²), Memory = O(1)
• Using prefix sum array: Time = O(n), Memory = O(n)
• Using single scan: Time = O(n), Memory = O(1)

Trapping rain water

• Using Dynamic Programming: Time = O(n), Memory = O(n)
• Using Stack: Time = O(n), Memory = O(n)
• Using two pointers: Time = O(n), Memory = O(1)

Check for pair with a given sum

• Using sorting and binary search: Time = O(nlogn), Memory = O(1)
• Using sorting and Two Pointers: Time = O(nlogn), Memory = O(1)
• Using a Hash Table: Time = O(n), Memory = O(n)

Find the majority element in an array

• Using two nested loops: Time = O(n²), Memory = O(1)
• Using Sorting: Time = O(nlogn), Memory = O(1)
• Using the divide and conquer: Time = O(nlogn), Memory = O(logn)
• Using Bit Manipulation: Time = O(n), Memory = O(1)
• Using Randomisation: Time = O(nlogn), Memory = O(1) Note: If value of n is very large.
• Boyer-Moore Voting Algorithm: Time = O(n), Memory = O(1)

Maximum Subarray Sum

• Using three nested loops: Time = O(n^3), Memory = O(1)
• Using two nested loops: Time = O(n^2), Memory = O(1)
• Using divide and conquer: Time = O(nlogn), Memory = O(logn)
• Using dynamic programming: Time = O(n), Memory = O(n)
• Kadane algorithm: Time = O(n), Memory = O(1)

Before you jump into the end-to-end code implementation, it’s good practice to write pseudocode on paper. It would be helpful in defining code structure and critical operations. Some programmers skip this step, but writing the final code becomes easier when we have well-designed pseudocode.

Step 4: Transforming pseudocode into a clean, correct, and optimized code

Finally, we need to replace each line of pseudocode with actual code in our favorite programming languages like C++, Java, Python, C#, JavaScript, etc. Never forget to test actual code with sample test data and check if the actual output is equal to the expected output. When writing code in your interviews, discuss sample data or test cases with the interviewer.

Simplifying and optimizing the code may require a few iterations of observation. We need to ask these questions once we are done writing the code: 

• Does this code run for every possible input, including the edge cases?
• Can we optimize the code further? Can we remove some variables or loop or some extra space?
• Are we repeating some steps a lot? Can we define it separately using another function?
• Is the code readable or written with a good coding style?

Enjoy learning, Enjoy coding, Enjoy algorithms!

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• Boost your career prospects with our professional DSA with Java course

DSA using Java Course Objectives

The Information Technology Industry is growing at a fast pace and so is the demand for developing large and sizable software. To develop such complex software, one needs to be proficient with data structures and algorithms in Java. Java programming is very dynamic in nature and expects deep attention to the details of operations. It can be required in any system be it mobile application programming or internet programming.

The aim of this course is to provide all the essential learning material for DSA in Java, via practice material, quizzes and professional guidance. In this course, we will cover all the topics of data and algorithms to give the learner a practical knowledge of its use and needs. In this current scenario, the IT sector is flooding with job opportunities and is exhausting a lot of IT experts. With this course you will not only improve your skills and attain the problem-solving aptitude for data structures and algorithms, but also be able to utilize your skills to work as a professional in the IT sector.

The main goal of a Data Structures and Algorithms (DSA) course in Java is to give students the fundamental abilities and understanding needed to effectively manage and control data while resolving challenging computational issues. Such a course’s main objective is to provide a solid foundational understanding of the fundamental ideas and procedures behind efficient algorithmic problem-solving.

Students in this course learn to break down difficult real-world problems into manageable parts in order to acquire strong problem-solving skills. Students learn how to create effective algorithms that offer solutions to various problems by using a systematic approach. This advances the important goal of encouraging algorithmic thinking, a cognitive ability that is important not only in computer science but also in resolving a variety of complicated problems in numerous fields.

The DSA in Java course helps students get ready for technical interviews, which are an important part of many hiring procedures for software engineering jobs. The course gives students the knowledge and confidence they need to succeed in these high-stress circumstances by teaching advanced algorithmic approaches and problem-solving tactics.

In the end, a DSA in Java course has goals that go beyond only imparting technical knowledge. The course develops students into competent problem solvers and talented programmers who are well-prepared to manage the dynamic environment of software development and computer science by encouraging critical thinking, cooperation, and a desire for lifelong learning.

Why should you learn DSA with Java?

DSA with Java is quite in demand in the IT sector. Especially if you are into software creation, this course is a must. Programming is a complex skill to learn but here we make it all a cake walk for you. You will learn about data structures, stacks, arrays, queues and linked lists. The sole purpose of this data and algorithm course under Java is to understand the design techniques for any software and perform dynamic programming with exclusive algorithms. You will be introduced to problem-solving techniques to give you an overall experience of this industry.

• Java-oriented DSA includes easy concept’s implementations.
• Java’s memory management is up-to-standards and restrains leaks and faults.
• DSA with Java is a combination of high demand in the IT sector.
• Algorithms in Java have common conceptions and can be easily implemented.
• Since Java programs can run on any platform, learning DSA concepts under Java gives versatile advantages.

What is DSA using Java?

DSA in Java stands for Data Structures and Algorithms. It talks about the foundational ideas and methods used in computer science to efficiently organize and manipulate data as well as to solve challenging issues algorithmically. In the Java world, DSA entails using the programming language Java to implement and make use of various data structures and algorithms in order to address computational problems that arise in the real world.

To store and organize data for quick access and modification, we use data structures. With the help of the Java Collections Framework’s classes and interfaces, you may construct a variety of data structures in Java, including arrays, linked lists, stacks, queues, trees, graphs, and hash tables.

Algorithms are detailed instructions or processes for resolving particular problems. Java offers a platform for effectively developing and running algorithms. Java can be used to build a variety of algorithms, including bubble sort, merge sort, quicksort, binary search, Dijkstra’s algorithm for graphs, and more.

In conclusion, DSA in Java entails using the Java programming language to implement and interact with data structures and algorithms, optimizing code for effectiveness, and resolving computational issues using well-defined methods. Any Java developer who wants to create effective, maintainable, and scalable software solutions must have a firm grasp of DSA ideas.

What to do before you begin?

Before you enroll in this DSA with Java course, it is advisable for you to attain a basic understanding of programming and its fundamental concepts. This is professional advice since having a prior foundational knowledge of this concept will keep you more engaged and in-full command of your skill intake.

The basic knowledge of control flow, programming functions, data types and variables will come very handy for you. Please note that this advice is not mandatory. It is a recommendation so as to keep yourself up-to-date with this course and enlighten your problem-solving arena. This will give you the idea of what parts to work on and a sense of self confidence.

Who should go for this free DSA with Java course?

This DSA with Java course is designed for IT aspirants who wish to make their career as a professional Data Structures and Algorithms Analyst. With this DSA using Java course you open the doors to umpteen number of opportunities as the demand of IT specialists is ever-increasing. Join this course and bag all the opportunities to build an amazing career. This DSA using Java course is best suited for

• IT aspirants looking for a career in this domain
• Enthusiasts of Data Structure and Algorithms
• Aspirants who wish to become software engineers
• Professional Data Analysts
• Computer Science Researchers
• Learners of Java Programming

By enrolling in our DSA with Java course, you can expect the following benefits:

A Data Structures and Algorithms (DSA) training course designed exclusively for Java will help you advance your programming abilities and profession in a number of ways. The following are some benefits of taking Java DSA training:

• Solid Problem-Solving Skills DSA training aids in the development of solid problem-solving abilities. You’ll learn how to create effective algorithms to handle difficult issues by breaking them down into smaller, more manageable steps. In any technical function, these abilities are extremely valuable.
• Coding Effectiveness Knowing different data structures and algorithms enables you to select the most suitable ones for particular jobs, resulting in more effective and optimized code. Your Java applications’ performance may be significantly impacted by this.
• Algorithmic Thinking DSA training promotes algorithmic and abstract thought. This ability can be used to analyze and resolve issues in a variety of fields in addition to programming.
• Competitive Advantage Your competitive advantage in the employment market is your knowledge of DSA. Candidates with a strong understanding of fundamental computer science topics are highly valued by employers.
• Opportunities for Employment DSA proficiency opens up a wider number of employment possibilities, including positions in software development, system design, data analysis, and research.

When you choose this DSA in Java training programme, you will cover a wide range of topics which are up-to-date and have a lot of demand in the IT industry. You will become hands-on with coding exercises and open up to a lot of practical opportunities to apply.

We further advise the practice of coding and analysis on platforms such as HackerRank, Codeforces and Leetcode. This is to improve your skills and keep you acknowledged about your level of performance.

With this course you will grasp A to Z knowledge of DSA in Java and sit for any interview related to it. The concepts that you will learn will give you the aptitude of problem-solving which will further help you in every kind of Java project throughout your career. Our industry-ready training aims to create a beginner to an expert. You will get to learn the following concepts in this course-

• Basic Programming Concepts
• Arrays and Strings
• Linked Lists
• Stacks and Queues
• Trees and Binary Trees
• Sorting Algorithms
• Dynamic Programming

Jobs after Learning this DSA using Java Course

DSA in Java opens up a myriad of job opportunities. The IT sector has been growing in the past and requires a lot of human potential. Learning DSA in Java directly gives you umpteen number of job options that too with handsome salary. To know more about the job roles, read ahead-

• Software Engineer: A thorough knowledge of DSA is necessary for many software engineering professions. You can be requested to optimize code for performance or solve algorithmic puzzles.
• Backend Developer: Backend developers frequently work with database connections and server-side logic. It can be beneficial to optimize data retrieval and manipulation procedures by having a firm understanding of DSA.
• Java Developer: You will work on Java programmes in a variety of domains as a Java developer. Strong DSA abilities can aid with the writing of maintainable, effective code.
• Algorithm Engineer: Some businesses employ engineers expressly to create and put into practise sophisticated algorithms. Deep understanding of DSA ideas is frequently needed for this position.
• Technical Interviewer: Some seasoned engineers take on the role of technical interviewers to assist employers in evaluating applicants’ DSA abilities during the recruiting process.
• Computer science researcher: If you have a strong interest in algorithms and data structures, you can think about pursuing a research position in university or business.
• You can aspire to become a computer programmer. Many software engineering careers require a solid understanding of DSA. You can be asked to solve algorithmic challenges or optimize code for performance.
• There is wide demand for Backend Programmers in the IT sector. Backend engineers commonly work with server-side logic and database connections. Having a solid understanding of DSA can help you optimize your data retrieval and manipulation processes.
• Every other company requires a Java Programmer. As a Java developer, you will work on Java programmes in a number of fields. Coders that have strong DSA skills will have an easier time developing maintainable, efficient code.
• You can become an ace computer engineer. Some companies specifically hire engineers to develop and implement complex algorithms. For this position, a thorough understanding of DSA concepts is typically required.

Our students are working in leading organizations

Online DSA with Java Free Training Course Curriculum

• Sum and Max of Array Elements
• Reserve of Arrays
• Subarrays of Array
• Transpose of Matrix Course
• Rotation of 2D Array
• Recap of Arrays
• Operating Array List
• Removal of Even numbers
• Removal of Vowels from String
• Reverse of Vowels from String
• Conversion of Uppercase to Lowercase
• Operating String Code
• Selecting Sort Code
• Bubble Sort
• Coding of Bubble Sort
• Coding of Insertion Sort Code
• Arrays Sort
• Increasing Print
• Tracing Print Increase
• Display and Tracing of Array
• First Index Tracing in Array
• Factorial Tracing
• Add First and Add Last in Linked List
• Remove First and Remove Last in Linked List
• Linked List Print
• Operating Length of Linked List
• Search K in Linked List
• Operating Mid of Linked List
• Introduction to Stack and Queue
• Operations with Stack
• Operations with Queue
• Using Stack for LinkedList
• Using Queue for LinkedList
• Tree Nomenclature
• Traversal Tree Coding
• Sum of Tree
• Size of Tree
• Introduction to Heap
• Largest Element K
• Coding of Element K
• Introduction to HashMap
• Coding of Frequency Character
• Get Common Element
• Max Frequency Character
• Coding of First Repeating Character
• Classification
• Graph and its inputs
• Coding of Adjacency Matrix Representation
• Coding of Adjacency List Representation
• Implementation of Breadth First Search
• Coding of Rotting Oranges
• Implementation of Depth First Search
• Operating with Graph Practical
• Coding of Number of Island
• Introduction to Programming
• Fibonacci Number
• Top Down and Bottom up Approach
• Introduction to Climb Stairs
• Operating Climb Stairs Top Down
• Operating Climb Stairs Bottom Down
• Introduction to Minimum Path Sum
• Operating Minimum Path Sum Top Down

Features of DSA with Java Free Course

DSA using Java Online Training FAQs

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Python Certification Course [Hindi]

This Python course makes it easy to start coding, whether you’re a complete Python beginner or looking to sharpen your skills. Along with the online Python course, you will get study material, Python practical codes, quizzes, real-time python projects, interview series, industry-renowned python certificate, and more. Join the Python course today and get ready for top jobs with our premium placement assitance program.

What will you take home from this Python Course?

• 100+ hrs self-paced expert-led course
• 170+ hrs of comprehensive study material
• 130+ hrs of real-world practicals
• 110+ Interactive quizzes & assessments
• 570+ Interview questions for top MNCs
• 110+ Real-time projects with implementation
• 200+ Python Practical Code Examples
• 98% Positive reviews from learners
• 130+ Comprehensive assignments
• 30+ Real-time industry case-studies
• 400+ Python programming tutorials
• 1:1 Career counselling with expert
• Practical knowledge which industry needs
• Industry-renowned certification

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Master python from scratch.

Why should you enroll in this Python Course?

• You’ll develop a solid understanding of Python programming, which will enable you to write code and solve issues with ease
• Learning Python can provide you adaptable abilities that you may use for automation, data analysis, web development, and more
• To put your newfound knowledge to use, take part in practical coding activities and projects
• Learn about web development using Python frameworks to create dynamic websites and applications
• Master data analysis and visualization utilizing Matplotlib and Pandas, two potent Python packages
• Develop abilities to automate routine processes to increase productivity across a range of industries
• Python frameworks can be used to create interactive web apps
• Effective problem-solving is facilitated by Python’s simplicity
• We guarantee a complete mastery of Python by covering it from the ground up in our course
• Take advantage of the knowledgeable direction of professors who are fluent in Hindi and Python
• To solidify your abilities, take part in practical exercises and projects
• A lucrative profession in technology is accessible to anyone with Python skills
• Take your time studying and take into account your daily obligations
• You can always refer back to the course materials to improve your understanding
• Develop abilities that will enable you to find employment in the cutthroat tech sector
• After completing the course, you’ll be given a certification that attests to your competency with Python
• Language obstacles are removed when learning Python in Hindi, making it easier for you to understand ideas

Python Course Objectives

People seeking to learn programming have a fantastic option to do so by learning Python. The simplicity and adaptability of Python are well known, and when it is taught in Hindi, it becomes much more approachable. This online Python course with a certificate offers a thorough curriculum with explanations for everything from Python’s fundamentals to more complex subjects. This online Python course will provide you the knowledge and abilities you need to thrive at Python programming, whether you’re a novice or have some prior coding expertise.

Python is recognized for its ease of use, readability, and flexibility, making it a great option for both inexperienced and seasoned programmers. Participants in such a course often start with fundamental concepts like variables, data types, and control structures before moving on to more complex material like object-oriented programming, web development, and data analysis. Python courses frequently contain practical exercises and real-world projects to reinforce learning and provide practical experience.

These courses not only give students useful coding skills, but they also get them ready for a variety of job options in industries including software development, data science, web development, and automation. There are many advantages to taking a Python course. Participants develop a strong foundation in programming as well as analytical thinking and problem-solving skills. Python is a highly sought-after expertise because of its numerous uses in a variety of areas, including finance, healthcare, and artificial intelligence.

The goals of a Python course are varied and specifically designed to give students a thorough understanding of the Python programming language and its wide range of applications. participants with a strong foundation in Python language is one of the main goals. The objective entails teaching a thorough knowledge of the syntax, data types, control structures, and fundamental ideas of Python. Participants learn how to build Python code that is both efficient and clean by grasping the foundations.

The main goals of Python courses are data manipulation and analysis. Participants become proficient in data-manipulation and data-visualization libraries like Matplotlib and Pandas. This goal equips them for jobs in data science where they can clean, examine, and visualize data to derive insightful conclusions. In order to develop clear, understandable, and effective code, participants are urged to embrace best coding practices. This goal makes sure that graduates are well-equipped to participate successfully in software development teams and work on collaborative projects.

Through this Python course, participants gain knowledge of the complexities of routing and templating as well as how to build dynamic web apps and manage databases. In conclusion, the goals of a Python course are to give students a thorough education in Python programming so they can pursue a variety of careers and adjust to the changing needs of the IT sector.

Why should you learn Python?

The power and dominance of the Python programming language doesn’t need any explanation. It is a self-claimed robust language on which the IT industry depends a lot. Below are some factual reasons of why one should learn Python language-

• One of the most well-liked programming languages for novices is Python. – The TechCrunch
• Python’s popularity has been aided by its readability and simplicity of learning. – The Forbes
• The best language for machine learning and data science is Python. – Survey from Stack Overflow
• Python is a flexible language with a wide range of frameworks and packages. – Microsoft
• Python is a great choice for both beginners and experts due to its resources and supportive community.
• What is Python?

Invented by Guido van Rossum in 1991, Python is a robust programming language which is globally known for its easy readability and flexibility in applications. This programming language has become a sensation and is worldly used by the IT industries and other sectors as well. This programming language is especially famous in the software development arena. Working with Python benefits you with the clean and easy syntax in coding, thus making it easy and effective for both the beginners and professionals.

In many areas, including web development, data analysis, machine learning, artificial intelligence, scientific computing, automation, and scripting, this programming language is highly adaptable. Frameworks for building websites and online applications, such Django and Flask, make web development simpler. Data purification, analysis, visualization, and the development of machine learning models are all tasks that Python is widely used for in the field of data science.

Python is a practical choice for developing apps that can be deployed on Windows, macOS, Linux, and other operating systems due to its cross-platform compatibility, which enables developers to write code once and run it on several platforms. Python has established itself as one of the most well-liked and influential programming languages in the world. It appeals to both novice and seasoned programmers looking for a productive and adaptable tool for software development and problem-solving.

What to do before you begin?

It’s advantageous to have a basic understanding of computers and programming ideas before starting this Python course. Although this online Python course covers each and every aspect of the Python language, the prerequisites are not mandatory. Make sure you have access to a computer with an internet connection, and arrive with an open mind and a want to learn. With this Python course, you can explore your linguistic comfort while learning Python, which is an approachable language.

• Python must be installed on a computer. An integrated development environment (IDE) like PyCharm or Jupyter Notebook is suggested for coding exercises.
• Learn about forums, online sources, and Python documentation because they will be helpful tools for you as you start with your coursework.

Who should go for this Python course?

Learning the Python programming language has immense benefits. TechVidvan’s Python course is designed to convert you from a beginner into an expert. You will learn all the basic and advanced functioning of this language and be able to apply it into real world scenarios. To top that, this online Python course is curated to make you even more familiar with the idea and concepts.

Python is beneficial for everyone especially for a number of categories of people such as –

• Budding programmers
• Web developers
• IT Graduates
• Aspiring coders
• IT research enthusiast
• Software developers
• Aspiring data analysts

By enrolling in our Python course, you can expect the following benefits:

During the Python training, participants will develop a thorough understanding of Python programming and its various applications. We’ll start by going over the fundamentals of Python’s syntax, variables, data types, and control structures. This foundational knowledge serves as the foundation for more complicated issues.

As the class progresses, participants will learn web development techniques using Python frameworks like Django and Flask, enabling them to create dynamic and interactive online applications. They will also look into data manipulation and analysis, learning how to properly clean, examine, and visualize data with the help of programs like NumPy and Pandas. The fundamentals of object-oriented programming (OOP) will also become more familiar to participants, preparing them to write and use effective, modular programs.

Additionally, emphasis will be placed on Python’s prowess in data analysis and manipulation. Participants will learn how to manipulate data using tools like Pandas and visualize it using Matplotlib. In order to prepare them for careers in data science and business intelligence, they will learn how to clean, process, and analyze data. An all-encompassing education in programming, web development, and data management is offered by a Python course, providing doors to a variety of employment options in the computer sector.

For those hoping to become skilled programmers, taking a Python course might be quite advantageous. It gives students a strong foundation in coding, excellent problem-solving techniques, and the capacity to handle data. Graduates can pursue a variety of professional pathways, from web development and data science to automation and artificial intelligence, thanks to Python’s adaptability. Furthermore, beginners can rapidly pick up programming principles and write clear, maintainable code thanks to Python’s simplicity and readability, which makes it a perfect language for them.

Below are the top training benefits of Python course-

• Hindi speakers can more easily access Python by learning it in their native tongue, which removes any linguistic hurdles.
• The tech markets, both domestically and internationally, value Python expertise for job chances.
• Because of its flexibility, Python gets to be used for a variety of jobs, including data science and web development.
• Practical coding experience can be obtained through hands-on activities and projects.
• Because of Python’s widespread acceptance, the knowledge you gain in this course will be beneficial.
• Graduates are prepared for a range of employment in the tech sector with their skill set.
• A competitive employment market both locally and globally benefits from Python proficiency.
• Completing this online Python course successfully not only upgrades your skills but also provides you with a certificate which will benefit you in future job opportunities.
• Learning Python from the experts in this field has more advantages and scope of learning.

Jobs after Learning this Python Course

Since Python is so versatile and has such a wide range of applications, learning it offers up a ton of work options in numerous industries. Following completion of a Python course, you may be interested in the following positions-

• Engineer in artificial intelligence (AI)

Using Python, AI engineers create chatbots, computer vision programs, and speech recognition systems. They are engaged in cutting-edge AI research.

• Web developer

You can work as a web developer to build dynamic, interactive websites and web apps utilizing frameworks like Django or Flask. Python is utilized for web development.

• Python Developer

Python is a programming language that you will use to build systems, websites, and software applications. Using web development frameworks like Django or Flask, you’ll work on both front-end and back-end programming.

• Data Analyst

Python is frequently used for data analysis and manipulation, according to data analysts. You can work as a data analyst, gathering, cleaning, and analyzing data to draw forth insightful conclusions and make data-driven decisions.

• Machine Learning Engineer

Python is a programming language that data scientists use to create machine learning models and algorithms. To address complicated business issues, they use statistical analysis, data visualization, and predictive modeling.

• DevOps Engineer

DevOps engineers carry out deployment, continuous integration, and automation tasks using Python. They facilitate the operations and software development processes.

• Engineer in charge of quality assurance (QA)

QA engineers use Python to automate tests. To guarantee the excellence and dependability of software products, they create test scripts.

• Cybersecurity Analyst

Python is utilized in cybersecurity for tasks including penetration testing, vulnerability scanning, and security log analysis, according to a cybersecurity analyst. To safeguard digital assets, you could work in the cybersecurity industry.

• IT Scientist

Python is used in scientific research for data processing, simulations, and modeling, according to a research scientist. Python is frequently used by research scientists to carry out experiments and analyze data.

Our students are working in leading organizations

Online Python Training Course Curriculum

• Why you must learn Python?
• Python Application
• Difference between Platform Dependent and Independent
• Features of Python
• Limitations of Python
• History of Python
• Python installation
• Pycharm installation
• What is IDLE?
• Python code execution flow
• Hello World program
• Python Statement, Indentation and Comments
• How to print in Python
• How to take input from user
• Method, Identifier, Variable
• Data Types, Variables
• Rules to Declare Identifiers
• Input Output Functions
• How to Read Data in Single Line
• Print() Function
• Print Statement with Formatted String
• Replacement Operator
• How to Print using format() Method
• Types of Operators
• Bitwise Operators
• Arithmetic Operators, Assignment Operators
• Number System Conversion
• Identity Operators
• Python If Else
• Pyramid patterns
• Trasfer statements
• Break and Continue statement
• Pass statement
• What are loops?
• Types of loops
• Nested for loop
• What is String?
• String Built-in Functions
• String Class & Methods
• String Functions
• Type Casting in Python
• What are collections?
• List collection
• Frozen sets
• Dictionaries
• Bytes and Bytearray
• What are Functions?
• Types of Functions and Methods
• Creation of function
• Parameter and Arguments
• Function Arguments
• Call by Value in Function
• Types of Arguments in Functions
• Return by Value
• Function as an Arguments
• Passing Function as an Argument to Another Function in Python
• Global Variables and Global Keywords
• Global vs Globals
• eval method
• Use of eval() Function
• Factorial Program with recursion
• Reverse a Number using Recursion
• Program to Print Fibbonacci Series using Recursion
• Anonymous Functions / Lambda Functions
• Filter Function
• Filter Function With Lambda Function
• Lambda with map function
• What are Arrays
• Operations On Array
• Array Types and Concepts
• How to Use Array
• Methods of Array
• Creation of Arrays using NumPy
• Comparing Arrays
• Object reference
• Difference Between View & Copy
• NumPy Array Dimensions
• Python NumPy Array Attributes
• Multidimensional Array in Python with NumPy
• Matrices in Python using NumPy
• Binary Search
• Bubble Sort
• Procedure Oriented vs Object Oriented Programming
• What is OOPs?
• Classes and Objects
• Creation of Classes and Objects
• Self variable
• init method
• init() and str() method
• Constructor Overloading
• Polymorphism
• Operator Overloading
• Relational Operator Overloading
• Overloading vs Overriding
• Getters and Setters
• Static Variables
• Static Methods
• Inner Class
• Is-A vs Has-A relationship
• Inheritance
• Supet method
• Types of inheritance
• Constructor in Multiple Inheritance
• Role of Constructor in Multiple Inheritance
• Abstract Class and Method
• What is Exception Handling?
• Types of Exceptions
• Try Except finally
• Exception Handling Programs
• Finally Statement
• Assert Exception
• Raise Statement
• User Defined Exceptions
• File in Python
• Modes of File
• File Mode r+ w+ a+
• Read, Write, Append Modes
• Open File using with Statement
• File Exceptions
• Python Program to Write Data in File
• File I/O program
• readlines() and writelines() Methods of file
• Count Lines, Words, Characters from File
• How to Read One by One Character from File
• Read and write Files in Binary Mode
• Pickle in Python
• Python Pickle Module
• File Handling using Pickle
• tell() & seek() Methods in Python
• How to Open CSV File in Python
• Read Data from CSV File
• What is Multithreading?
• Life Cycle of Thread in Python
• Different ways to create threads
• Thread Class
• Main Thread
• join() method
• Daemon thread
• Exception in Multithreading
• Race condition
• Thread Synchronization
• Lock in Multithreading
• Difference between Lock & RLock
• RLock in Multithreading
• Semaphore in Python Synchronization
• GUI Applications using Tkinter
• Different Options of root in Tkinter GUI
• root Window Methods & Attributes
• Adding Frame to Python Program
• Inserting Images in Tkinter Widget
• Create Buttons With Python Tkinter
• Multiple Button in GUI Application
• Fonts in GUI Application
• Grid in Python
• Difference between grid(), pack() & place() methods
• Login Application
• GUI Application using OOPs
• CheckButton, RadioButton in Tkinter
• Enrty control with python events
• How to use Message Dialog Window Popup in Python using Tkinter
• MenuBar in GUI Application
• Notepad Project
• Connect Python with MySQL
• MySQL Server installation
• What is SQL?
• DDL Command
• DML Command
• Like Command
• Aggregate Functions
• Nested Queries
• Python MySQL Connection
• How to Store Input Data in Database using Parameterized Query
• Fetch Data from Database using Python
• Fetch one record from Database using Python
• Delete records from Database
• What is ORM Architecture in Python
• Step Towards Enterprise Applications
• How to Design ORM Architecture
• Develop Enterprise Applications
• Enterprise Application Development using ORM Architecture
• Python GUI Applications with Database Connection
• Difference Between C and Python
• Difference Between C++ and Python
• Difference Between Python and Java

Features of Python Course

Python Online Training FAQs

Projects may require you to create straightforward machine learning models, analyze data, or create web applications in order to put your knowledge into practice.

Python is a versatile programming language used, among other things, for automating processes, data analysis, and web development. Learning Python opens up a wide range of career possibilities.

No. Beginners should enroll in this online Python course. While it is advantageous, prior experience is not required.

Python must be installed on your PC (we’ll help you through setup). We suggest using an integrated development environment (IDE) for coding.

Although studying math can be helpful, our Python course provides explanations and tools that make complex math easier to understand for a variety of users.

Self-paced learning is available in our Python course, allowing you to advance at your own pace.

After a course is finished, you will receive a certificate of completion. This certificate will help you in many job prospects.

You will be doing Python real life projects in different domains like retail, banking, and finance.

Depending on how quickly you learn, this Python course is created according to your flexibility. You can cover this online Python course as per your time, pace and comfort.

Python developers are in high demand across many industries. Success in this course may lead to positions in web development, data analysis, machine learning, and other industries.

Course Content

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