اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Coding_knowledge
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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
نمایش بیشتر📈 تحلیل کانال تلگرام Coding_knowledge
کانال Coding_knowledge (@coding_knwledge01) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 81 608 مشترک است و جایگاه 1 582 را در دسته فناوری و برنامهها و رتبه 3 899 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 81 608 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 11 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 3 614 و در ۲۴ ساعت گذشته برابر 49 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 6.93% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.95% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 5 652 بازدید دریافت میکند. در اولین روز معمولاً 2 409 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 14 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند q&a, goody, api, stack, analyst تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“💡 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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 12 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
81 608
مشترکین
+4924 ساعت
+4547 روز
+3 61430 روز
آرشیو پست ها
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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)
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81 602
7 Days = 7 Certificates 🎯
1/ Google Certifications: https://developers.google.com/certification
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6/ Meta Certifications: https://www.facebook.com/business/learn/certification
7/ Microsoft: https://learn.microsoft.com/en-us/shows/intro-to-python-development/
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Neon Cursor 🚀
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Project :- Escape-road-game 🚀
instagram Reel :- https://www.instagram.com/reel/DRo_kJIEj1H/?utm_source=ig_web_copy_link&igsh=MzRlODBiNWFlZA==
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81 602
Hey guys, only 2 hours are left to register for the free Python course. Register now! 🚀👇
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Hey Folks, Join my coding whatsapp group 👇
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SQL + NoSQL Databases
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Build real-world scalable projects
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✅ Deployment Skills
Docker & Kubernetes
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✅ Crack High-Level Interviews
Low Level Design (LLD)
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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
- Brush up on SQL, Python, and data wrangling
2️⃣ Know Your Projects Deeply
- 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
- Clarify objective, constraints, and assumptions
7️⃣ Stay Updated & Curious
- Follow latest tools (like LangChain, LLMs)
- Share your learning journey on GitHub or blogs
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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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