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Coding Projects

Coding Projects

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Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data

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๐Ÿ“ˆ Analytical overview of Telegram channel Coding Projects

Channel Coding Projects (@programming_experts) in the English language segment is an active participant. Currently, the community unites 65 997 subscribers, ranking 1 980 in the Technologies & Applications category and 5 218 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 65 997 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 716 over the last 30 days and by 20 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 4.00%. Within the first 24 hours after publication, content typically collects 1.25% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 637 views. Within the first day, a publication typically gains 823 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
  • Thematic interests: Content is focused on key topics such as |--, algorithm, array, framework, javascript.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œChannel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_dataโ€

Thanks to the high frequency of updates (latest data received on 12 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

65 997
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+2024 hours
+1347 days
+71630 days
Posts Archive
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months ### Week 1: Introduction to Python Day 1-2: Basics of Python - Python setup (installation and IDE setup) - Basic syntax, variables, and data types - Operators and expressions Day 3-4: Control Structures - Conditional statements (if, elif, else) - Loops (for, while) Day 5-6: Functions and Modules - Function definitions, parameters, and return values - Built-in functions and importing modules Day 7: Practice Day - Solve basic problems on platforms like HackerRank or LeetCode ### Week 2: Advanced Python Concepts Day 8-9: Data Structures in Python - Lists, tuples, sets, and dictionaries - List comprehensions and generator expressions Day 10-11: Strings and File I/O - String manipulation and methods - Reading from and writing to files Day 12-13: Object-Oriented Programming (OOP) - Classes and objects - Inheritance, polymorphism, encapsulation Day 14: Practice Day - Solve intermediate problems on coding platforms ### Week 3: Introduction to Data Structures Day 15-16: Arrays and Linked Lists - Understanding arrays and their operations - Singly and doubly linked lists Day 17-18: Stacks and Queues - Implementation and applications of stacks - Implementation and applications of queues Day 19-20: Recursion - Basics of recursion and solving problems using recursion - Recursive vs iterative solutions Day 21: Practice Day - Solve problems related to arrays, linked lists, stacks, and queues ### Week 4: Fundamental Algorithms Day 22-23: Sorting Algorithms - Bubble sort, selection sort, insertion sort - Merge sort and quicksort Day 24-25: Searching Algorithms - Linear search and binary search - Applications and complexity analysis Day 26-27: Hashing - Hash tables and hash functions - Collision resolution techniques Day 28: Practice Day - Solve problems on sorting, searching, and hashing ### Week 5: Advanced Data Structures Day 29-30: Trees - Binary trees, binary search trees (BST) - Tree traversals (in-order, pre-order, post-order) Day 31-32: Heaps and Priority Queues - Understanding heaps (min-heap, max-heap) - Implementing priority queues using heaps Day 33-34: Graphs - Representation of graphs (adjacency matrix, adjacency list) - Depth-first search (DFS) and breadth-first search (BFS) Day 35: Practice Day - Solve problems on trees, heaps, and graphs ### Week 6: Advanced Algorithms Day 36-37: Dynamic Programming - Introduction to dynamic programming - Solving common DP problems (e.g., Fibonacci, knapsack) Day 38-39: Greedy Algorithms - Understanding greedy strategy - Solving problems using greedy algorithms Day 40-41: Graph Algorithms - Dijkstraโ€™s algorithm for shortest path - Kruskalโ€™s and Primโ€™s algorithms for minimum spanning tree Day 42: Practice Day - Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms ### Week 7: Problem Solving and Optimization Day 43-44: Problem-Solving Techniques - Backtracking, bit manipulation, and combinatorial problems Day 45-46: Practice Competitive Programming - Participate in contests on platforms like Codeforces or CodeChef Day 47-48: Mock Interviews and Coding Challenges - Simulate technical interviews - Focus on time management and optimization Day 49: Review and Revise - Go through notes and previously solved problems - Identify weak areas and work on them ### Week 8: Final Stretch and Project Day 50-52: Build a Project - Use your knowledge to build a substantial project in Python involving DSA concepts Day 53-54: Code Review and Testing - Refactor your project code - Write tests for your project Day 55-56: Final Practice - Solve problems from previous contests or new challenging problems Day 57-58: Documentation and Presentation - Document your project and prepare a presentation or a detailed report Day 59-60: Reflection and Future Plan - Reflect on what you've learned - Plan your next steps (advanced topics, more projects, etc.) Best DSA RESOURCES: https://topmate.io/coding/886874 Credits: https://t.me/free4unow_backup ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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List of Python Project Ideas๐Ÿ’ก๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป๐Ÿ - Beginner Projects ๐Ÿ”น Calculator ๐Ÿ”น To-Do List ๐Ÿ”น Number Guessing Game ๐Ÿ”น Basic Web Scraper ๐Ÿ”น Password Generator ๐Ÿ”น Flashcard Quizzer ๐Ÿ”น Simple Chatbot ๐Ÿ”น Weather App ๐Ÿ”น Unit Converter ๐Ÿ”น Rock-Paper-Scissors Game Intermediate Projects ๐Ÿ”ธ Personal Diary ๐Ÿ”ธ Web Scraping Tool ๐Ÿ”ธ Expense Tracker ๐Ÿ”ธ Flask Blog ๐Ÿ”ธ Image Gallery ๐Ÿ”ธ Chat Application ๐Ÿ”ธ API Wrapper ๐Ÿ”ธ Markdown to HTML Converter ๐Ÿ”ธ Command-Line Pomodoro Timer ๐Ÿ”ธ Basic Game with Pygame Advanced Projects ๐Ÿ”บ Social Media Dashboard ๐Ÿ”บ Machine Learning Model ๐Ÿ”บ Data Visualization Tool ๐Ÿ”บ Portfolio Website ๐Ÿ”บ Blockchain Simulation ๐Ÿ”บ Chatbot with NLP ๐Ÿ”บ Multi-user Blog Platform ๐Ÿ”บ Automated Web Tester ๐Ÿ”บ File Organizer

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Java vs Python ๐Ÿ‘†
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months ### Week 1: Introduction to Python Day 1-2: Basics of Python - Python setup (installation and IDE setup) - Basic syntax, variables, and data types - Operators and expressions Day 3-4: Control Structures - Conditional statements (if, elif, else) - Loops (for, while) Day 5-6: Functions and Modules - Function definitions, parameters, and return values - Built-in functions and importing modules Day 7: Practice Day - Solve basic problems on platforms like HackerRank or LeetCode ### Week 2: Advanced Python Concepts Day 8-9: Data Structures in Python - Lists, tuples, sets, and dictionaries - List comprehensions and generator expressions Day 10-11: Strings and File I/O - String manipulation and methods - Reading from and writing to files Day 12-13: Object-Oriented Programming (OOP) - Classes and objects - Inheritance, polymorphism, encapsulation Day 14: Practice Day - Solve intermediate problems on coding platforms ### Week 3: Introduction to Data Structures Day 15-16: Arrays and Linked Lists - Understanding arrays and their operations - Singly and doubly linked lists Day 17-18: Stacks and Queues - Implementation and applications of stacks - Implementation and applications of queues Day 19-20: Recursion - Basics of recursion and solving problems using recursion - Recursive vs iterative solutions Day 21: Practice Day - Solve problems related to arrays, linked lists, stacks, and queues ### Week 4: Fundamental Algorithms Day 22-23: Sorting Algorithms - Bubble sort, selection sort, insertion sort - Merge sort and quicksort Day 24-25: Searching Algorithms - Linear search and binary search - Applications and complexity analysis Day 26-27: Hashing - Hash tables and hash functions - Collision resolution techniques Day 28: Practice Day - Solve problems on sorting, searching, and hashing ### Week 5: Advanced Data Structures Day 29-30: Trees - Binary trees, binary search trees (BST) - Tree traversals (in-order, pre-order, post-order) Day 31-32: Heaps and Priority Queues - Understanding heaps (min-heap, max-heap) - Implementing priority queues using heaps Day 33-34: Graphs - Representation of graphs (adjacency matrix, adjacency list) - Depth-first search (DFS) and breadth-first search (BFS) Day 35: Practice Day - Solve problems on trees, heaps, and graphs ### Week 6: Advanced Algorithms Day 36-37: Dynamic Programming - Introduction to dynamic programming - Solving common DP problems (e.g., Fibonacci, knapsack) Day 38-39: Greedy Algorithms - Understanding greedy strategy - Solving problems using greedy algorithms Day 40-41: Graph Algorithms - Dijkstraโ€™s algorithm for shortest path - Kruskalโ€™s and Primโ€™s algorithms for minimum spanning tree Day 42: Practice Day - Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms ### Week 7: Problem Solving and Optimization Day 43-44: Problem-Solving Techniques - Backtracking, bit manipulation, and combinatorial problems Day 45-46: Practice Competitive Programming - Participate in contests on platforms like Codeforces or CodeChef Day 47-48: Mock Interviews and Coding Challenges - Simulate technical interviews - Focus on time management and optimization Day 49: Review and Revise - Go through notes and previously solved problems - Identify weak areas and work on them ### Week 8: Final Stretch and Project Day 50-52: Build a Project - Use your knowledge to build a substantial project in Python involving DSA concepts Day 53-54: Code Review and Testing - Refactor your project code - Write tests for your project Day 55-56: Final Practice - Solve problems from previous contests or new challenging problems Day 57-58: Documentation and Presentation - Document your project and prepare a presentation or a detailed report Day 59-60: Reflection and Future Plan - Reflect on what you've learned - Plan your next steps (advanced topics, more projects, etc.) Best DSA RESOURCES: https://topmate.io/coding/886874 Credits: https://t.me/free4unow_backup ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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Master Javascript : The JavaScript Tree ๐Ÿ‘‡ | |โ”€โ”€ Variables | โ”œโ”€โ”€ var | โ”œโ”€โ”€ let | โ””โ”€โ”€ const | |โ”€โ”€ Data Types | โ”œโ”€โ”€ String | โ”œโ”€โ”€ Number | โ”œโ”€โ”€ Boolean | โ”œโ”€โ”€ Object | โ”œโ”€โ”€ Array | โ”œโ”€โ”€ Null | โ””โ”€โ”€ Undefined | |โ”€โ”€ Operators | โ”œโ”€โ”€ Arithmetic | โ”œโ”€โ”€ Assignment | โ”œโ”€โ”€ Comparison | โ”œโ”€โ”€ Logical | โ”œโ”€โ”€ Unary | โ””โ”€โ”€ Ternary (Conditional) ||โ”€โ”€ Control Flow | โ”œโ”€โ”€ if statement | โ”œโ”€โ”€ else statement | โ”œโ”€โ”€ else if statement | โ”œโ”€โ”€ switch statement | โ”œโ”€โ”€ for loop | โ”œโ”€โ”€ while loop | โ””โ”€โ”€ do-while loop | |โ”€โ”€ Functions | โ”œโ”€โ”€ Function declaration | โ”œโ”€โ”€ Function expression | โ”œโ”€โ”€ Arrow function | โ””โ”€โ”€ IIFE (Immediately Invoked Function Expression) | |โ”€โ”€ Scope | โ”œโ”€โ”€ Global scope | โ”œโ”€โ”€ Local scope | โ”œโ”€โ”€ Block scope | โ””โ”€โ”€ Lexical scope ||โ”€โ”€ Arrays | โ”œโ”€โ”€ Array methods | | โ”œโ”€โ”€ push() | | โ”œโ”€โ”€ pop() | | โ”œโ”€โ”€ shift() | | โ”œโ”€โ”€ unshift() | | โ”œโ”€โ”€ splice() | | โ”œโ”€โ”€ slice() | | โ””โ”€โ”€ concat() | โ””โ”€โ”€ Array iteration | โ”œโ”€โ”€ forEach() | โ”œโ”€โ”€ map() | โ”œโ”€โ”€ filter() | โ””โ”€โ”€ reduce()| |โ”€โ”€ Objects | โ”œโ”€โ”€ Object properties | | โ”œโ”€โ”€ Dot notation | | โ””โ”€โ”€ Bracket notation | โ”œโ”€โ”€ Object methods | | โ”œโ”€โ”€ Object.keys() | | โ”œโ”€โ”€ Object.values() | | โ””โ”€โ”€ Object.entries() | โ””โ”€โ”€ Object destructuring ||โ”€โ”€ Promises | โ”œโ”€โ”€ Promise states | | โ”œโ”€โ”€ Pending | | โ”œโ”€โ”€ Fulfilled | | โ””โ”€โ”€ Rejected | โ”œโ”€โ”€ Promise methods | | โ”œโ”€โ”€ then() | | โ”œโ”€โ”€ catch() | | โ””โ”€โ”€ finally() | โ””โ”€โ”€ Promise.all() | |โ”€โ”€ Asynchronous JavaScript | โ”œโ”€โ”€ Callbacks | โ”œโ”€โ”€ Promises | โ””โ”€โ”€ Async/Await | |โ”€โ”€ Error Handling | โ”œโ”€โ”€ try...catch statement | โ””โ”€โ”€ throw statement | |โ”€โ”€ JSON (JavaScript Object Notation) ||โ”€โ”€ Modules | โ”œโ”€โ”€ import | โ””โ”€โ”€ export | |โ”€โ”€ DOM Manipulation | โ”œโ”€โ”€ Selecting elements | โ”œโ”€โ”€ Modifying elements | โ””โ”€โ”€ Creating elements | |โ”€โ”€ Events | โ”œโ”€โ”€ Event listeners | โ”œโ”€โ”€ Event propagation | โ””โ”€โ”€ Event delegation | |โ”€โ”€ AJAX (Asynchronous JavaScript and XML) | |โ”€โ”€ Fetch API ||โ”€โ”€ ES6+ Features | โ”œโ”€โ”€ Template literals | โ”œโ”€โ”€ Destructuring assignment | โ”œโ”€โ”€ Spread/rest operator | โ”œโ”€โ”€ Arrow functions | โ”œโ”€โ”€ Classes | โ”œโ”€โ”€ let and const | โ”œโ”€โ”€ Default parameters | โ”œโ”€โ”€ Modules | โ””โ”€โ”€ Promises | |โ”€โ”€ Web APIs | โ”œโ”€โ”€ Local Storage | โ”œโ”€โ”€ Session Storage | โ””โ”€โ”€ Web Storage API | |โ”€โ”€ Libraries and Frameworks | โ”œโ”€โ”€ React | โ”œโ”€โ”€ Angular | โ””โ”€โ”€ Vue.js ||โ”€โ”€ Debugging | โ”œโ”€โ”€ Console.log() | โ”œโ”€โ”€ Breakpoints | โ””โ”€โ”€ DevTools | |โ”€โ”€ Others | โ”œโ”€โ”€ Closures | โ”œโ”€โ”€ Callbacks | โ”œโ”€โ”€ Prototypes | โ”œโ”€โ”€ this keyword | โ”œโ”€โ”€ Hoisting | โ””โ”€โ”€ Strict mode | | END __

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Here are some project ideas to Practice C++ Programming - 1. Calculator Application: Create a command-line calculator with support for basic arithmetic operations and more advanced features like square roots and trigonometric functions. 2. Student Record System: Build a program to manage student records, including details like names, grades, and attendance. 3. Task Tracker: Create a task tracker application that allows users to add, view, and complete tasks. Use file handling to store task data. 4. Simple Game: Develop a text-based or console-based game like Tic-Tac-Toe, Hangman, or a simple maze solver. 5. Inventory Management System: Build an inventory management system for a small store. Track product details, prices, and stock levels. 6. Chat Application: Create a basic command-line chat application that allows users to send messages to each other using sockets for networking.

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Web Development Project Ideas Beginner-Level Projects (Focus: HTML, CSS, basic JavaScript) 1. Calculator 2. Quiz App 3. Rock Paper Scissors 4. Note App 5. Stopwatch App 6. QR Code Reader 7. Weather App 8. Landing Page 9. Password Generator 10. Tic Tac Toe Game 11. Drawing App 12. Meme Generator 13. To-Do List App 14. Typing Speed Test 15. Random User API Intermediate-Level Projects (Focus: JavaScript, basic backend, APIs, local storage, UI/UX) 1. Link Shortener Website 2. Portfolio Website 3. Food Order Website 4. Movie App 5. Chat App 6. Twitter Clone 7. Survey App 8. E-Book Site 9. File Sharing App 10. Parallax Website 11. Tracker App 12. Memory App 13. Giphy Clone 14. Chess Game 15. Music Player Advanced-Level Projects (Focus: Full Stack, authentication, real-time, complex logic, deployment) 1. Ecommerce Website 2. Instagram Clone 3. Whatsapp Clone 4. Netflix Clone 5. Job Search App 6. Pinterest Clone 7. Dating App 8. Social Media Dashboard 9. User Activity Tracker 10. Stock-Trading App React โค๏ธ for more Join our WhatsApp channel for more: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z

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โŒจ๏ธ JavaScript Neat Tricks you should know
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โŒจ๏ธ JavaScript Neat Tricks you should know

Coding and Aptitude Round before interview Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking. Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round. Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you. Resources for Prep: For algorithms and data structures prep,Leetcode and Hackerrank are good resources. For aptitude prep, you can refer to IndiaBixand Practice Aptitude. With respect to data science challenges, practice well on GLabs and Kaggle. Brilliant is an excellent resource for tricky math and statistics questions. For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself. Things to Note: Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do! In case, you are finished with the test before time, recheck your answers and then submit. Sometimes these rounds donโ€™t go your way, you might have had a brain fade, it was not your day etc. Donโ€™t worry! Shake if off for there is always a next time and this is not the end of the world.

7 Beginner Machine Learning Projects To Complete This Weekend ๐Ÿ“Š 1. Predicting Titanic Survival Predict who survived using age, gender & class. Learn data cleaning + logistic regression basics. Click here ๐Ÿ“ˆ 2. Predicting Stock Prices Forecast stock trends with ARIMA or LSTM. Practice feature engineering & MSE evaluation. click here ๐Ÿ“ง 3. Email Spam Classifier Build an NLP model to detect spam. Use TF-IDF + Naive Bayes/SVM for accuracy. Click here ๐Ÿ”ข 4. Handwritten Digit Recognition Train a CNN on MNIST to identify digits (0โ€“9). Explore image preprocessing & deep learning. Click here ๐ŸŽฌ 5. Movie Recommendation System Suggest movies using collaborative filtering (SVD) & MovieLens dataset. Measure with RMSE. Click here ๐Ÿ”„ 6. Customer Churn Prediction Find which customers may leave. Handle imbalanced data & use logistic regression/random forest. Click here ๐Ÿ‘ค 7. Face Detection Use OpenCV Haar cascades to detect faces in images/videos. Learn filtering & edge detection. Find here