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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 66 108 subscribers, ranking 1 981 in the Technologies & Applications category and 5 203 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 66 108 subscribers.

According to the latest data from 13 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 783 over the last 30 days and by 43 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.54%. Within the first 24 hours after publication, content typically collects 1.30% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 336 views. Within the first day, a publication typically gains 857 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
  • 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 14 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.

66 108
Subscribers
+4324 hours
+1637 days
+78330 days
Posts Archive
Want to get started with System design interview preparation, start with these ๐Ÿ‘‡ 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 (keyโ€“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 (leaderโ€“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: ๐Ÿ’ก 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. ๐ŸŽฏ

๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—œ๐—•๐—  ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—ฆ๐—ธ๐˜†๐—ฟ๐—ผ๐—ฐ๐—ธ๐—ฒ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ๐Ÿ˜ From mastering C
๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—œ๐—•๐—  ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—ฆ๐—ธ๐˜†๐—ฟ๐—ผ๐—ฐ๐—ธ๐—ฒ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ๐Ÿ˜ 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 โ€” without costing you anything. ๐—Ÿ๐—ถ๐—ป๐—ธ:-๐Ÿ‘‡ https://pdlink.in/44GsWoC Enroll For FREE & Get Certified โœ…

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. ๐Ÿ‘‡ Python Interview ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ https://t.me/dsabooks ๐Ÿ“˜ ๐—ฃ๐—ฟ๐—ฒ๐—บ๐—ถ๐˜‚๐—บ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ : https://topmate.io/coding/914624 ๐Ÿ“™ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z Join What's app channel for jobs updates: t.me/getjobss

๐Ÿ”ด How to MASTER a programming language using ChatGPT: ๐Ÿ“Œ 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 ๐Ÿ‘‡๐Ÿ‘‡ https://t.me/free4unow_backup/687 Master DSA ๐Ÿ‘‡๐Ÿ‘‡ https://t.me/dsabooks/156

Repost from Data Analytics
๐Ÿฒ ๐—•๐—ฒ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ง๐˜‚๐—ฏ๐—ฒ ๐—–๐—ต๐—ฎ๐—ป๐—ป๐—ฒ๐—น๐˜€ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ๐Ÿ˜ Power BI Isnโ€™t Just a Toolโ€”Itโ€™s a Career Game
๐Ÿฒ ๐—•๐—ฒ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ง๐˜‚๐—ฏ๐—ฒ ๐—–๐—ต๐—ฎ๐—ป๐—ป๐—ฒ๐—น๐˜€ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ๐Ÿ˜ Power BI Isnโ€™t Just a Toolโ€”Itโ€™s a Career Game-Changer๐Ÿš€ Whether youโ€™re a student, a working professional, or switching careers, learning Power BI can set you apart in the competitive world of data analytics๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3ELirpu Your Analytics Journey Starts Nowโœ…๏ธ

๐Ÿ”ฐ Create a PDF file using Python
๐Ÿ”ฐ Create a PDF file using Python

Python Cheatsheet ๐Ÿ‘†
Python Cheatsheet ๐Ÿ‘†

๐๐š๐ฒ ๐€๐Ÿ๐ญ๐ž๐ซ ๐๐ฅ๐š๐œ๐ž๐ฆ๐ž๐ง๐ญ - ๐—Ÿ๐—ฎ๐—ป๐—ฑ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฟ๐—ฒ๐—ฎ๐—บ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—๐—ผ๐—ฏ๐Ÿ˜ Curriculum designed and taught by Alumn
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Project ideas for college students
+4
Project ideas for college students

๐—ง๐—–๐—ฆ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ Want to kickstart your career in Data
๐—ง๐—–๐—ฆ ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ Want to kickstart your career in Data Analytics but donโ€™t know where to begin?๐Ÿ‘จโ€๐Ÿ’ป TCS has your back with a completely FREE course designed just for beginnersโœ… ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4jNMoEg Just pure, job-ready learning๐Ÿ“

10 Public APIs you can use for your next project ๐ŸŒ http://restcountries.com - Country data API ๐ŸŒฑ http://trefle.io - Plants data API ๐Ÿš€http://api.nasa.gov - Space-related API ๐ŸŽต http://developer.spotify.com - Music data API ๐Ÿ“ฐ http://newsapi.org - Access news articles ๐ŸŒ… http://sunrise-sunset.org/api - Sunrise and sunset times API ๐Ÿฒ http://pokeapi.co - Pokรฉmon data API ๐ŸŽฅ http://omdbapi.com - Movie database API ๐Ÿˆ http://catfact.ninja - Cat facts API ๐Ÿถ http://thedogapi.com - Dog picture API

๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„? ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—›๐—ฒ๐—ฟ๐—ฒ!๐Ÿ˜ Preparing for a Power BI interview? This reel is your ultimate sec
๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„? ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—›๐—ฒ๐—ฟ๐—ฒ!๐Ÿ˜ Preparing for a Power BI interview? This reel is your ultimate secret weapon!๐Ÿ’ผโšก ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3S1uouf Save it. Share it. Study it. And walk in preparedโœ…๏ธ

๐Ÿ’ก Must Have Tools for Programmers
+7
๐Ÿ’ก Must Have Tools for Programmers

๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ & ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—Ÿ๐—ฒ๐—ฎ
๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ & ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฃ๐—ฎ๐˜๐—ต๐˜€๐Ÿ˜ Want to level up your Data Analytics & Machine Learning gameโ€”for FREE?๐Ÿ”ฅ These official Microsoft learning paths are your shortcut to building practical, job-ready skills. ๐Ÿง ๐Ÿ’ป ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4cIU9cc Because your future job in data isnโ€™t going to wait. Why should you? ๐Ÿ”ฅ

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. ๐Ÿ“– The journal: โ€ข  Indexed in the largest bibliographic databases of scientific citations โ€ข  Accessible to an international audience and published in the worldโ€™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 ๐Ÿ‘‡๐Ÿ‘‡ 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 ๐Ÿ‘‡๐Ÿ‘‡ 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 LEARNING๐Ÿ‘๐Ÿ‘

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Random Module in Python ๐Ÿ‘†
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Random Module in Python ๐Ÿ‘†