uk
Feedback
Coding Projects

Coding Projects

Відкрити в Telegram

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

Показати більше

📈 Аналітичний огляд Telegram-каналу Coding Projects

Канал Coding Projects (@programming_experts) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 66 108 підписників, посідаючи 1 981 місце в категорії Технології та додатки та 5 203 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 66 108 підписників.

За останніми даними від 13 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 783, а за останні 24 години на 43, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 3.54%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.30% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 2 336 переглядів. Протягом першої доби публікація в середньому набирає 857 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 8.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як |--, algorithm, array, framework, javascript.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data

Завдяки високій частоті оновлень (останні дані отримано 14 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

66 108
Підписники
+4324 години
+1637 днів
+78330 день
Архів дописів
Want to get started with System design interview preparation, start with these 👇 1. Learn to understand requirements 2. Learn the difference between horizontal and vertical scaling. 3. Study latency and throughput trade-offs and optimization techniques. 4. Understand the CAP Theorem (Consistency, Availability, Partition Tolerance). 5. Learn HTTP/HTTPS protocols, request-response lifecycle, and headers. 6. Understand DNS and how domain resolution works. 7. Study load balancers, their types (Layer 4 and Layer 7), and algorithms. 8. Learn about CDNs, their use cases, and caching strategies. 9. Understand SQL databases (ACID properties, normalization) and NoSQL types (key–value, document, graph). 10. Study caching tools (Redis, Memcached) and strategies (write-through, write-back, eviction policies). 11. Learn about blob storage systems like S3 or Google Cloud Storage. 12. Study sharding and horizontal partitioning of databases. 13. Understand replication (leader–follower, multi-leader) and consistency models. 14. Learn failover mechanisms like active-passive and active-active setups. 15. Study message queues like RabbitMQ, Kafka, and SQS. 16. Understand consensus algorithms such as Paxos and Raft. 17. Learn event-driven architectures, Pub/Sub models, and event sourcing. 18. Study distributed transactions (two-phase commit, sagas). 19. Learn rate-limiting techniques (token bucket, leaky bucket algorithms). 20. Study API design principles for REST, GraphQL, and gRPC. 21. Understand microservices architecture, communication, and trade-offs with monoliths. 22. Learn authentication and authorization methods (OAuth, JWT, SSO). 23. Study metrics collection tools like Prometheus or Datadog. 24. Understand logging systems (e.g., ELK stack) and tracing tools (OpenTelemetry, Jaeger). 25.Learn about encryption (data at rest and in transit) and rate-limiting for security. 26. And then practise the most commonly asked questions like URL shorteners, chat systems, ride-sharing apps, search engines, video streaming, and e-commerce websites Coding Interview Resources: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Here are some of the most popular python project ideas: 💡 Simple Calculator Text-Based Adventure Game Number Guessing Game Password Generator Dice Rolling Simulator Mad Libs Generator Currency Converter Leap Year Checker Word Counter Quiz Program Email Slicer Rock-Paper-Scissors Game Web Scraper (Simple) Text Analyzer Interest Calculator Unit Converter Simple Drawing Program File Organizer BMI Calculator Tic-Tac-Toe Game To-Do List Application Inspirational Quote Generator Task Automation Script Simple Weather App Automate data cleaning and analysis (EDA) Sales analysis Sentiment analysis Price prediction Customer Segmentation Time series forecasting Image classification Spam email detection Credit card fraud detection Market basket analysis NLP, etc These are just starting points. Feel free to explore, combine ideas, and personalize your projects based on your interest and skills. 🎯

𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍 From mastering C
𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍 From mastering Cloud Computing to diving into Deep Learning, Docker, Big Data, and IoT Blockchain IBM, one of the biggest tech companies, is offering 5 FREE courses that can seriously upgrade your resume and skills — without costing you anything. 𝗟𝗶𝗻𝗸:-👇 https://pdlink.in/44GsWoC Enroll For FREE & Get Certified ✅

Python Roadmap for 2025: Complete Guide 1. Python Fundamentals 1.1 Variables, constants, and comments. 1.2 Data types: int, float, str, bool, complex. 1.3 Input and output (input(), print(), formatted strings). 1.4 Python syntax: Indentation and code structure. 2. Operators 2.1 Arithmetic: +, -, *, /, %, //, **. 2.2 Comparison: ==, !=, <, >, <=, >=. 2.3 Logical: and, or, not. 2.4 Bitwise: &, |, ^, ~, <<, >>. 2.5 Identity: is, is not. 2.6 Membership: in, not in. 3. Control Flow 3.1 Conditional statements: if, elif, else. 3.2 Loops: for, while. 3.3 Loop control: break, continue, pass. 4. Data Structures 4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.). 4.2 Tuples: Immutability, packing/unpacking. 4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.). 4.4 Sets: Unique elements, set operations (union, intersection). 4.5 Strings: Immutability, methods (split(), strip(), replace()). 5. Functions 5.1 Defining functions with def. 5.2 Arguments: Positional, keyword, default, *args, **kwargs. 5.3 Anonymous functions (lambda). 5.4 Recursion. 6. Modules and Packages 6.1 Importing: import, from ... import. 6.2 Standard libraries: math, os, sys, random, datetime, time. 6.3 Installing external libraries with pip. 7. File Handling 7.1 Open and close files (open(), close()). 7.2 Read and write (read(), write(), readlines()). 7.3 Using context managers (with open(...)). 8. Object-Oriented Programming (OOP) 8.1 Classes and objects. 8.2 Methods and attributes. 8.3 Constructor (init). 8.4 Inheritance, polymorphism, encapsulation. 8.5 Special methods (str, repr, etc.). 9. Error and Exception Handling 9.1 try, except, else, finally. 9.2 Raising exceptions (raise). 9.3 Custom exceptions. 10. Comprehensions 10.1 List comprehensions. 10.2 Dictionary comprehensions. 10.3 Set comprehensions. 11. Iterators and Generators 11.1 Creating iterators using iter() and next(). 11.2 Generators with yield. 11.3 Generator expressions. 12. Decorators and Closures 12.1 Functions as first-class citizens. 12.2 Nested functions. 12.3 Closures. 12.4 Creating and applying decorators. 13. Advanced Topics 13.1 Context managers (with statement). 13.2 Multithreading and multiprocessing. 13.3 Asynchronous programming with async and await. 13.4 Python's Global Interpreter Lock (GIL). 14. Python Internals 14.1 Mutable vs immutable objects. 14.2 Memory management and garbage collection. 14.3 Python's name == "main" mechanism. 15. Libraries and Frameworks 15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn. 15.2 Web Development: Flask, Django, FastAPI. 15.3 Testing: unittest, pytest. 15.4 APIs: requests, http.client. 15.5 Automation: selenium, os. 15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch. 16. Tools and Best Practices 16.1 Debugging: pdb, breakpoints. 16.2 Code style: PEP 8 guidelines. 16.3 Virtual environments: venv. 16.4 Version control: Git + GitHub. 👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 https://t.me/dsabooks 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624 📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z Join What's app channel for jobs updates: t.me/getjobss

🔴 How to MASTER a programming language using ChatGPT: 📌 1. Can you provide some tips and best practices for writing clean and efficient code in [lang]? 2. What are some commonly asked interview questions about [lang]? 3. What are the advanced topics to learn in [lang]? Explain them to me with code examples. 4. Give me some practice questions along with solutions for [concept] in [lang]. 5. What are some common mistakes that people make in [lang]? 6. Can you provide some tips and best practices for writing clean and efficient code in [lang]? 7. How can I optimize the performance of my code in [lang]? 8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts? 9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively? 10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues? 11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]? 12. How can I effectively collaborate with other developers in [lang] on a project? 13. What are some common data structures and algorithms that I should be familiar with in [lang]? How to Create Resume using ChatGPT 👇👇 https://t.me/free4unow_backup/687 Master DSA 👇👇 https://t.me/dsabooks/156

Repost from Data Analytics
𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍 Power BI Isn’t Just a Tool—It’s a Career Game
𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍 Power BI Isn’t Just a Tool—It’s a Career Game-Changer🚀 Whether you’re a student, a working professional, or switching careers, learning Power BI can set you apart in the competitive world of data analytics📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3ELirpu Your Analytics Journey Starts Now✅️

🔰 Create a PDF file using Python
🔰 Create a PDF file using Python

Python Cheatsheet 👆
Python Cheatsheet 👆

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯😍 Curriculum designed and taught by Alumn
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯😍 Curriculum designed and taught by Alumni from IITs & Leading Tech Companies. 60+ Hiring Drives Every Month 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:-  🌟 500+ Hiring Partners 🤝Trusted by 7500+ Students 💼 Avg. Rs. 7.2 LPA 🚀 41 LPA Highest Package Eligibility: BTech / BCA / BSc / MCA / MSc 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇 :-  https://pdlink.in/4hO7rWY Hurry, limited seats available!🏃‍♀️

Project ideas for college students
+4
Project ideas for college students

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Want to kickstart your career in Data
𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Want to kickstart your career in Data Analytics but don’t know where to begin?👨‍💻 TCS has your back with a completely FREE course designed just for beginners✅ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jNMoEg Just pure, job-ready learning📍

10 Public APIs you can use for your next project 🌍 http://restcountries.com - Country data API 🌱 http://trefle.io - Plants data API 🚀http://api.nasa.gov - Space-related API 🎵 http://developer.spotify.com - Music data API 📰 http://newsapi.org - Access news articles 🌅 http://sunrise-sunset.org/api - Sunrise and sunset times API 🐲 http://pokeapi.co - Pokémon data API 🎥 http://omdbapi.com - Movie database API 🐈 http://catfact.ninja - Cat facts API 🐶 http://thedogapi.com - Dog picture API

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? 𝗦𝘁𝗮𝗿𝘁 𝗛𝗲𝗿𝗲!😍 Preparing for a Power BI interview? This reel is your ultimate sec
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? 𝗦𝘁𝗮𝗿𝘁 𝗛𝗲𝗿𝗲!😍 Preparing for a Power BI interview? This reel is your ultimate secret weapon!💼⚡ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3S1uouf Save it. Share it. Study it. And walk in prepared✅️

💡 Must Have Tools for Programmers
+7
💡 Must Have Tools for Programmers

𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝘄𝗶𝘁𝗵 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗮
𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝘄𝗶𝘁𝗵 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵𝘀😍 Want to level up your Data Analytics & Machine Learning game—for FREE?🔥 These official Microsoft learning paths are your shortcut to building practical, job-ready skills. 🧠💻 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4cIU9cc Because your future job in data isn’t going to wait. Why should you? 🔥

Call for papers on AI to AI Journey* conference journal has started! Prize for the best scientific paper - 1 million roubles!
Call for papers on AI to AI Journey* conference journal has started! Prize for the best scientific paper - 1 million roubles! Selected papers will be published in the scientific journal Doklady Mathematics. 📖 The journal: •  Indexed in the largest bibliographic databases of scientific citations •  Accessible to an international audience and published in the world’s digital libraries Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference. Prize for the best article - 1 million roubles! More detailed information can be found in the Selection Rules -> AI Journey *AI Journey - a major online conference in the field of AI technologies

Essential Programming Languages to Learn Data Science 👇👇 1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn). 2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization. 3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases. 4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems. 5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications. 6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations. 7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks. Free Resources to master data analytics concepts 👇👇 Data Analysis with R Intro to Data Science Practical Python Programming SQL for Data Analysis Java Essential Concepts Machine Learning with Python Data Science Project Ideas Learning SQL FREE Book Join @free4unow_backup for more free resources. ENJOY LEARNING👍👍

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍 - Amazon - Infosys - PwC - Genpact - Deloitte Qualification :- Any
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍 - Amazon - Infosys - PwC - Genpact - Deloitte Qualification :- Any Graduate  𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 & 𝐔𝐩𝐥𝐨𝐚𝐝 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞👇:-   https://pdlink.in/44qEIDu Enter your experience & Complete The Registration Process Select the company name & Apply for jobs💫

🚨 BE CAREFUL! BITCOIN WILL BE GONE SOON! Trader Lisa, who knew in advance about the fall of $LUNA now told about the fall of bitcoin. She opened her channel to everyone for a couple days, after that it will close and become a paid channel. Be sure to subscribe  👇 https://t.me/+nj9XEyP8fmMyYjMx https://t.me/+nj9XEyP8fmMyYjMx https://t.me/+nj9XEyP8fmMyYjMx

Random Module in Python 👆
+8
Random Module in Python 👆