#Hash Table Data Structure

Hashing in Data Structure #day21 #100dayscodingchallenge What is Hashing in Data Structure? Hashing is a technique used in data structures to store and retrieve data efficiently. It involves using a hash function to map data items to a fixed-size array which is called a hash table. Hash Table in Data Structure A hash table is also known as a hash map. It is a data structure that stores key-value pairs. It uses a hash function to map keys to a fixed-size array, called a hash table. This allows in faster search, insertion, and deletion operations. #techreprogram #developers #codinglover #softwareengineer #codingquestions #software #softwaredeveloper #java #javascript #python #coding #programing #love #engineer #engineering #college #google #techworld #tech #technology #code #programmer #trendingreels #treading #microsoft #meta

A hashtable is a data structure that stores key–value pairs and enables fast data access. It uses a hash function to convert each key into an index in an underlying array, allowing average-case constant time complexity for insert, search, and delete operations. Collisions, where multiple keys map to the same index, are handled using techniques like chaining or open addressing. Hashtables are widely used for implementing dictionaries, caches, and lookup tables due to their efficiency.

Heap Data Structure Follow @ghazi_it Follow @ghazi_it Follow @ghazi_it A Heap is a complete binary tree data structure that satisfies the heap property: for every node, the value of its children is less than or equal to its own value. Heaps are usually used to implement priority queues, where the smallest (or largest) What is Heap Data Structure? A heap is a binary tree-based data structure that satisfies the heap property: the value of each node is greater than or equal to the value of its children. This property makes sure that the root node contains the maximum or minimum value (depending on the type of heap), and the values decrease or increase as you move down the tree. Types of Heaps There are two main types of heaps: Max Heap: The root node contains the maximum value, and the values decrease as you move down the tree. Min Heap: The root node contains the minimum value, and the values increase as you move down the tree. Heap Operations Common heap operations are: Insert: Adds a new element to the heap while maintaining the heap property. Extract Max/Min: Removes the maximum or minimum element from the heap and returns it. Heapify: Converts an arbitrary binary tree into a heap. Heap Data Structure Applications Heaps have various applications, like: Heaps are commonly used to implement priority queues, where elements are retrieved based on their priority (maximum or minimum value). Heapsort is a sorting algorithm that uses a heap to sort an array in ascending or descending order. Heaps are used in graph algorithms like Dijkstra’s algorithm and Prim’s algorithm for finding the shortest paths and minimum spanning trees. #programming #coding #programmer #python #developer #javascript #technology #code #java #coder #html #computerscience #software #tech #css #webdeveloper #webdevelopment #codinglife #softwaredeveloper #linux #programmingmemes #webdesign #programmers #programminglife #php #hacking #pythonprogramming #machinelearning #computer #softwareengineer

Types of Data Structure . Video by @codingwithjd . . . #coding #cppproject #cplusplusprogramming #codinglife #codingbootcamp #codingisfun #codingninjas #coder #coderlife #coderslife #codersofinstagram #programming #programmingproblems #programmers #codingdays #codingchallenge #assembly #instagramgrowth #asciiart #cmd #cmdprompt #batchprocessing #aiartcommunity #artificialintelligence #deepseek #openai #meta #metaverse

Day 20/200: How HashMap Works Internally HashMap is one of the most powerful data structures in coding interviews & real-world systems. Here’s how it works under the hood: ✅ Hashing: Key → Hash function → Index in array (bucket). ✅ Collision handling: Chaining (LinkedList/Tree in Java) or Open addressing. ✅ Time complexity: O(1) average for put/get, O(n) worst-case. ⚡ Mastering HashMap internals is a must for interviews — it shows depth, not just coding ability. Follow @codewith_govind for 200 days of DSA. Comment “CODE” if you want a detailed GitHub note with diagrams. #Day20DSA #HashMapInternals #Hashing #DataStructures #DSA #Algorithms #ProblemSolving #DSAforInterviews #InterviewPrep #CodingInterview #LeetCode #Codeforces #GeeksforGeeks #SystemDesign #200DaysDSA #CodeWithGovind

Data Structure is a way to organize data efficiently. 🔹 Linear Data Structure Data is stored in a sequence (one after another). Examples: Array, Stack, Queue, Linked List. 🔹 Non-Linear Data Structure Data is stored in a hierarchical or connected form. Examples: Tree, Graph. 👉 Linear = Straight structure 👉 Non-Linear = Branching structure. Understanding Types of Data Structures is the first step to mastering DSA 🚀 From Linear to Non-Linear structures — this is where real coding logic begins! Learn concepts clearly with THE IITIAN CODER and build your strong programming foundation ✨ #DataStructures #DSA #CodingLife #LearnToCode #ProgrammingReels

Same hash code… different keys. Does HashMap break? No. But performance can. Most developers think collision = problem. Actually, it’s handled… but not free. HashMap uses hashCode to find bucket Then uses equals to find the exact key So both keys can exist no data loss But inside the bucket: Before Java 8 Linked list → O(n) After Java 8 Balanced tree → O(log n) Too many collisions means slower lookups And if hashCode or equals is wrong you get incorrect behavior Key idea: Collision is handled but bad design hurts performance Comment HASH if you want more such questions Save this before your next interview

Follow @cogni.wonk to learn data science ✔️ NOTE - Get Handwritten DSA Notes (Link in bio) 1. Sorting Algorithm: - It is the algorithm that arranges the order of the items in a list. Bubble Sort Merge Sort Quicksort Heap Sort 2. Searching Algorithm: - It is the algorithm that finds an element in a data set. Binary Search Breadth-First Search(BFS) Depth-First Search(DFS) 3. Dynamic Programming: - DP is an algorithmic technique for solving an optimization problem by breaking it down into simpler sub-problems. 4. Recursion Algorithm: - Recursion is a problem-solving technique in which the solution is dependent on solutions to smaller instances of the same problem. 5. Divide and Conquer: - The Divide and Conquer algorithm consists of a dispute using the three steps listed below. * Divide the original problem into sub-problems. * Conquer: Solve each sub-problem one at a time, recursively. * Combine: Put the solutions to the sub-problems together to get the solution to the whole problem. 6. Hashing: - Hashing is a technique or process that uses a hash function to map keys and values into a hash table. It is done to allow for quicker access to elements. Follow @codingwithsagar for more! #codingwithsagar #algorithm #algorithms #datastructure #datastructures #datastructureandalgorithm

📊 Data Visualization Cheat Sheet | Complete Guide to Charts & Graphs In this guide, you’ll learn about: ✔️ Gantt Chart – Track project timelines and scheduling ✔️ Bar Chart – Compare categories easily ✔️ Highlight Table – Identify highs and lows quickly ✔️ Bubble Chart – Compare multiple variables visually ✔️ Line Chart – Show trends over time ✔️ Tree Map – Display hierarchical data ✔️ Scatter Plot – Find relationships and correlations ✔️ Pie Chart – Show proportions and percentages ✔️ Box & Whisker Plot – Understand distribution and outliers ✔️ Maps – Visualize geographic data ✔️ Waterfall Chart – Track cumulative values ✔️ Bullet Chart – Measure performance vs targets This cheat sheet is perfect for: 🎯 Data Analysts 🎯 Business Analysts 🎯 Excel & Power BI Users 🎯 Tableau Beginners 🎯 Students & Professionals #DataVisualization #DataAnalytics #ChartsAndGraphs #BusinessAnalytics #ExcelTips PowerBI Tableau DataAnalyst DashboardDesign AnalyticsLearning ExcelBooster

Comenta HASH y te mando el vídeo ✅ Un hash es una representación matemática única de datos, como contraseñas, que se utiliza para verificar la integridad sin revelar la información original. #ciberseguridad #hackingetico #hash #seguridadinformatica

🚀 Ever wondered how databases find your data so fast? That’s the power of Indexing — B-Tree for ranges, Hash for IDs, Bitmap for categories, and Inverted Index for text search 🔍 From age BETWEEN 20–40 to status=‘active’ to Google-style keyword search, indexing makes queries fly! ⚡ Master this → and you master database performance 💪 #DatabaseIndexing #DBMS #SQLPerformance #BackendDevelopment #SoftwareEngineering #DataEngineering #ProgrammingTips #BTreeIndex #HashIndex #BitmapIndex #InvertedIndex #DatabaseDesign #SystemDesign #TechReels #CodeWithKundan #LearnWithKundan #DataStructures #ComputerScience #CodingReels #PerformanceTuning #DevLife

a video on hashing and hashmaps! sorry been busy with exams and uni! hope all you guys are well💙 Hash maps feel like magic because they promise fast lookups—often described as O(1) time. But that speed depends entirely on how well the hash function distributes data. A hash function takes a key and converts it into an index in memory, ideally spreading values evenly so each lookup goes straight to the right spot. When that happens, everything feels instant and efficient. The problem is that collisions are unavoidable. Different keys can produce the same index, which means the hash map has to handle multiple values in one place. This is where strategies like chaining (storing multiple items in a list at the same index) or open addressing (finding another spot using methods like linear or quadratic probing) come in. These methods keep things working—but they introduce extra steps. That’s why hash maps aren’t truly constant time in all cases. Their performance depends on the quality of the hash function and how collisions are handled. In the best case, lookups are fast and smooth—but in worse cases, they can degrade toward O(n). So O(1) isn’t a guarantee—it’s an expectation based on good design. #bocchitherock #computerscience #coding #dsa #math