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💡 Your Coding Journey Starts Here! Get free courses, coding resources, internships, job updates & much more. Stay ahead in tech with us! ❤️🚀 Join our WhatsApp group👇 https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D
Показати більше📈 Аналітичний огляд Telegram-каналу Coding_knowledge
Канал Coding_knowledge (@coding_knwledge01) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 81 608 підписників, посідаючи 1 582 місце в категорії Технології та додатки та 3 899 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 81 608 підписників.
За останніми даними від 11 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 3 614, а за останні 24 години на 49, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 6.93%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.95% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 5 652 переглядів. Протягом першої доби публікація в середньому набирає 2 409 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 14.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як q&a, goody, api, stack, analyst.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“💡 Your Coding Journey Starts Here!
Get free courses, coding resources, internships, job updates & much more.
Stay ahead in tech with us! ❤️🚀
Join our WhatsApp group👇
https://whatsapp.com/channel/0029Vaa7CVhCRs1rxJzy1n3D”
Завдяки високій частоті оновлень (останні дані отримано 12 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.
81 608
Підписники
+4924 години
+4547 днів
+3 61430 день
Архів дописів
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✅ Web Development Basics You Should Know 🌐💡
Understanding the foundations of web development is the first step toward building websites and web apps.
1️⃣ What is Web Development?
Web development is the process of creating websites and web applications. It includes everything from a basic HTML page to full-stack apps.
Types of Web Dev:
- Frontend: What users see (HTML, CSS, JavaScript)
- Backend: Server-side logic, databases (Node.js, Python, etc.)
- Full Stack: Both frontend + backend
2️⃣ How the Web Works
When you visit a website:
- The browser (client) sends a request via HTTP
- The server processes it and sends back a response (HTML, data)
- Your browser renders it on the screen
Example: Typing
www.example.com sends a request → DNS resolves IP → Server sends back the HTML → Browser displays it
3️⃣ HTML (HyperText Markup Language)
Defines the structure of web pages.
<h1>Welcome</h1>
<p>This is my first website.</p>h1 {
color: blue;
text-align: center;
}<button onclick="alert('Hello!')">Click Me</button>@media (max-width: 600px) {
body {
font-size: 14px;
}
}document.getElementById("demo").innerText = "Changed!";git init
git add .
git commit -m "First commit"
git push fetch('https://api.weatherapi.com')
.then(res => res.json())
.then(data => console.log(data));81 602
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Age of Programming Languages👨🏻💻
🦅 Swift (11 years old) (2014)
🚀 Kotlin (13 years old) (2011)
🦀 Rust (14 years old) (2010)
🐹 Go (15 years old) (2009)
🔷 TypeScript (12 years old) (2012)
🎸 C# (24 years old) (2000)
💎 Ruby (29 years old) (1995)
☕ Java (29 years old) (1995)
🌐 JavaScript (29 years old) (1995)
🐘 PHP (30 years old) (1994)
🐍 Python (34 years old) (1991)
🐪 Perl (37 years old) (1987)
🚀 C++ (39 years old) (1985)
📱 Objective-C (40 years old) (1984)
🔍 Prolog (52 years old) (1972)
🗣️ Smalltalk (52 years old) (1972)
🖥️ C (52 years old) (1972)
📝 Pascal (54 years old) (1970)
🎓 BASIC (60 years old) (1964)
💼 COBOL (65 years old) (1959)
🤖 Lisp (66 years old) (1958)
📜 Fortran (67 years old) (1957)
React ❤️ For More
81 602
7 Days = 7 Certificates 🎯
1/ Google Certifications: https://developers.google.com/certification
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7/ Microsoft: https://learn.microsoft.com/en-us/shows/intro-to-python-development/
81 602
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81 602
Neon Cursor 🚀
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81 602
Project :- Escape-road-game 🚀
instagram Reel :- https://www.instagram.com/reel/DRo_kJIEj1H/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==
React ❤️ For More
81 602
Hey guys, only 2 hours are left to register for the free Python course. Register now! 🚀👇
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81 602
Hey Folks , only 50 sheets are left!😟 If you haven’t enrolled yet to learn Python & machine learning for free and get a free certificate, do it quickly!
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81 602
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81 602
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81 602
Hey Folks, Join my coding whatsapp group 👇
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81 602
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✅ Crack High-Level Interviews
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✅ Personal Branding
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Technical Blogs
LinkedIn Optimisation
Mock Interviews + DSA Revision
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81 602
🚀 COMPLETE ROADMAP: FROM 4 LPA TO 40 LPA (STEP-BY-STEP PLAN)
PHASE 1: STRONG FOUNDATION (0 – 6 Months)
✅ Master DSA + Core Java
Arrays, Linked List, Stack, Queue, Trees, Graphs, DP
Daily problem solving on LeetCode / CodeStudio
Focus on writing clean & optimised code
PHASE 2: BACKEND DEVELOPMENT (6 – 12 Months)
✅ Become Backend Expert
Spring Boot + REST APIs
Microservices Architecture
SQL + NoSQL Databases
Authentication, JWT, Security Concepts
Build real-world scalable projects
PHASE 3: CLOUD & DEVOPS (12 – 15 Months)
✅ Deployment Skills
Docker & Kubernetes
AWS / GCP / Azure
CI/CD Pipelines
System Monitoring & Scaling
PHASE 4: SYSTEM DESIGN (15 – 20 Months)
✅ Crack High-Level Interviews
Low Level Design (LLD)
High Level Design (HLD)
Load Balancing, Caching, Database Scaling
Design systems like Netflix, Uber, WhatsApp
PHASE 5: ADVANCED ENGINEERING (20 – 30 Months)
✅ Become 10x Engineer
Concurrency & Multithreading
Performance Optimisation
Distributed Systems
Message Queues (Kafka, RabbitMQ)
PHASE 6: BRAND + INTERVIEW PREP (30 – 36 Months)
✅ Personal Branding
Strong GitHub Portfolio
Technical Blogs
LinkedIn Optimisation
Mock Interviews + DSA Revision
PHASE 7: TARGET HIGH-PAY COMPANIES (3 – 5 Years)
✅ Apply For:
MAANG Companies
Product-Based Giants
Remote International Roles
High Paying Startups
🎯 RESULT: 4 LPA ➝ 40 LPA CAREER TRANSFORMATION
Consistency + Smart Learning + Real Projects = High Salary Growth 💰
Reality Check
₹40 LPA is not magic. 🪄
It’s built with:
Deep skills
Smart job switches
High-leverage projects
Strategic career moves
81 602
🧠 7 Golden Rules to Crack Data Science Interviews 📊🧑💻
1️⃣ Master the Fundamentals
- Be clear on stats, ML algorithms, and probability
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- Be ready to explain models, metrics, and business impact
- Prepare for follow-up questions
3️⃣ Practice Case Studies & Product Thinking
- Think beyond code — focus on solving real problems
- Show how your solution helps the business
4️⃣ Explain Trade-offs
- Why Random Forest vs. XGBoost?
- Discuss bias-variance, precision-recall, etc.
5️⃣ Be Confident with Metrics
- Accuracy isn’t enough — explain F1-score, ROC, AUC
- Tie metrics to the business goal
6️⃣ Ask Clarifying Questions
- Never rush into an answer
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- Follow latest tools (like LangChain, LLMs)
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81 602
🔥 FINAL CALL - ONLY 12 SEATS LEFT
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81 602
✅ Python for Machine Learning – Beginner to Job-Ready Roadmap 🤖🐍
📍 1️⃣ Python Basics
– Variables, Data Types, Operators
– if-else, loops, functions
✅ Practice: Write a BMI calculator, number guessing game
📍 2️⃣ Data Structures & Libraries
– Lists, Dicts, Tuples, Sets
– NumPy: arrays, slicing, broadcasting
– Pandas: DataFrames, filtering, merging
✅ Practice: Analyze a CSV with Pandas
📍 3️⃣ Data Visualization
– Matplotlib, Seaborn basics
– Plotting histograms, boxplots, heatmaps
✅ Project: Visualize Titanic dataset insights
📍 4️⃣ Data Preprocessing
– Handling nulls, encoding, scaling
– Feature engineering & selection
✅ Practice: Clean a housing prices dataset
📍 5️⃣ Machine Learning with Scikit-learn
– Regression, Classification, Clustering
– Model training, prediction, evaluation
✅ Project: Predict student scores using Linear Regression
📍 6️⃣ Model Evaluation
– Accuracy, Precision, Recall, F1-Score
– Confusion Matrix, ROC-AUC
✅ Practice: Evaluate a classification model
📍 7️⃣ Model Tuning & Pipelines
– GridSearchCV, cross-validation
– Build ML pipelines for clean code
✅ Project: Optimize a Random Forest model
📍 8️⃣ Real-World ML Projects
– House price prediction
– Customer churn analysis
– Image classification
✅ Tip: Use datasets from Kaggle, UCI, or open APIs
💬 Tap ❤️ for more!
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Only 3 hours are left to register, and more than 3350+ people have already enrolled for the free Java course. The registration link is given below. 👇
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