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Coding_knowledge

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

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📈 Análisis del canal de Telegram Coding_knowledge

El canal Coding_knowledge (@coding_knwledge01) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 81 601 suscriptores, ocupando la posición 1 582 en la categoría Tecnologías y Aplicaciones y el puesto 3 899 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 81 601 suscriptores.

Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 3 614, y en las últimas 24 horas de 49, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.93%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.95% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 5 652 visualizaciones. En el primer día suele acumular 2 409 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 14.
  • Intereses temáticos: El contenido se centra en temas clave como q&a, goody, api, stack, analyst.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
💡 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

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 12 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

81 601
Suscriptores
+4924 horas
+4547 días
+3 61430 días
Archivo de publicaciones
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🚀 Why Java in 2025? Beginner’s Guide to Java Career, Projects & Roadmap 📅 Date: 27th July ⏰ Time: 5:00 PM 🎯 Perfect for to
🚀 Why Java in 2025? Beginner’s Guide to Java Career, Projects & Roadmap 📅 Date: 27th July ⏰ Time: 5:00 PM 🎯 Perfect for total beginners & future developers ✅ Why Java still dominates in 2025 ✅ Career path & job roles (Backend, Android, DevOps & more) ✅ Real-world Java projects to build ✅ Complete beginner-to-pro roadmap If you want 🎁 Free Certificate + Project Files + Live Q&A 🔗, then choose "Maharashtra" as your state. Register now for free :- https://forms.gle/WMDoM6xBMUQBAX1A6

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⏰ Last Day to Claim – Only 25 Seats Left! 🎓 Become a Certified Data Analyst for Just ₹399 Course by LearnTube | Backed by Google 🚀 Why Join? ✅ Recognized Certificate (Google, Amazon & More) ✅ 40+ Hours | 150+ Lessons ✅ No Experience Needed – Perfect for Beginners ✅ Real Projects: Predictive Analytics, Fraud Detection & More 📘 What You’ll Learn: 🔹 Data Visualization (Tableau, Power BI) 🔹 Machine Learning Basics 🔹 Big Data Tools (Hadoop, Spark) 🔹 Python, R, SQL, Excel 💥 Get Job-Ready + Lifetime Access 🎯 Enroll Now – Just ₹399! 🔥 Offer Ends Tonight! 📲 Tap Below to Join Now 👇 https://tinyurl.com/DataAnalystXCourseCK

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Visualizing SQL Queries A mental model can help visualize how SQL queries are executed. Conceptually, SQL statements execute
Visualizing SQL Queries A mental model can help visualize how SQL queries are executed. Conceptually, SQL statements execute in this sequence: 1. FROM: Tables are identified and joined to create the initial dataset. 2. WHERE: Filters are applied to the initial dataset based on specified criteria. 3. GROUP BY: The filtered rows are grouped according to the specified columns. 4. HAVING: Additional filters are applied to the grouped rows based on aggregate criteria. 5. SELECT: Specific columns are chosen from the resultant dataset for the output. 6. ORDER BY: The output rows are sorted by the specified columns in ascending or descending order. 7. LIMIT: The number of rows in the output is restricted. While the actual execution plan may vary due to query optimization, this mental model provides a valuable framework for understanding SQL logic.

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Coding_knowledge - Estadísticas y analítica del canal de Telegram @coding_knwledge01