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📈 Análisis del canal de Telegram Data Science

El canal Data Science (@sql_databases) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 71 033 suscriptores, ocupando la posición 2 273 en la categoría Educación y el puesto 4 764 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 71 033 suscriptores.

Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -54, y en las últimas 24 horas de 6, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 12.21%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.97% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 8 672 visualizaciones. En el primer día suele acumular 2 110 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
  • Intereses temáticos: El contenido se centra en temas clave como database, learning, linkedin, udemy, 029k|.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 06 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 Educación.

71 033
Suscriptores
+624 horas
+237 días
-5430 días
Archivo de publicaciones
🖥Type of Databases
🖥Type of Databases

@LearnPython3 - Python Data Science Handbook 2nd ed.pdf19.70 MB

🔰 📙 Python Data Science Handbook 2nd Edition
🔰 📙 Python Data Science Handbook 2nd Edition

📱Data Science 📱Decision Intelligence: Data Stories

🔅 Decision Intelligence: Data Stories 📝 Learn how to use key lessons from famous data stories around the world to improve d
🔅 Decision Intelligence: Data Stories 📝 Learn how to use key lessons from famous data stories around the world to improve decision-making, interpret data effectively, and communicate insights responsibly. 🌐 Author: Franz Buscha 🔰 Level: Beginner ⏰ Duration: 45m 📋 Topics: Data Science, Decision Sciences, Data-driven Decision Making 🔗 Join Data Science for more courses

🖥 8 Common database types explained
🖥 8 Common database types explained

📖 Learn Database Databases power everything from websites and apps to enterprise systems. Here’s a learning map that can hel
📖 Learn Database Databases power everything from websites and apps to enterprise systems. Here’s a learning map that can help you master databases: 1 - Database Fundamentals This includes topics like “What is a database”, RDBMS, SQL vs NoSQL, ACID vs BASE, OLTP vs OLAP, Transactions, and Isolation Levels. 2 - Data Models and Types Consists of topics like Relational Databases, Non-Relational Databases, and Data Types (Integer, String, Boolean, Date, JSON, etc). 3 - Querying and Language This includes topics like SQL Basics (SELECT, INSERT, etc), Advanced SQL (Views, Indexes, CTEs, etc), and NoSQL Querying (Aggregation and Key-Value Lookups). 4 - Indexing and Optimization Consists of topics like Indexing (B-Tree, Hash, and Bitmaps), Query Execution Plans, Denormalization vs Normalization, Sharding, Connecting Pooling, and Query Batching. 5 - Security, Backups, and Scaling This includes topics like User Roles, Permissions, Encryption, SQL Injection, High Availability (Replication and Failover), Horizontal vs Vertical Scaling. 6 - Tools and Ecosystem Consists of topics like Popular SQL Databases, NoSQL Database, GUI Tools, ORMs, Cloud DB services (RDS, DynamoDB, Google Cloud SQL, etc.)

Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts?
Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts?

📱Data Science 📱The 80/20 Rule of Data Science

🔅 The 80/20 Rule of Data Science 📝 Explore the core concepts of the 80/20 rule for data science and how to get most of the
🔅 The 80/20 Rule of Data Science 📝 Explore the core concepts of the 80/20 rule for data science and how to get most of the value with minimal effort. 🌐 Author: Howard Friedman 🔰 Level: Intermediate ⏰ Duration: 1h 26m 📋 Topics: Data Science, Project Engineering, Team Management 🔗 Join Data Science for more courses

📖 What are DDL Commands in SQL? They don’t touch your data — they shape where your data lives. Use CREATE, ALTER, and DROP t
📖 What are DDL Commands in SQL? They don’t touch your data — they shape where your data lives. Use CREATE, ALTER, and DROP to define and change your database structure. 💡 Powerful, essential — and should be used with care!

🖥 Tableau vs. Power BI
🖥 Tableau vs. Power BI

🖥 4 main database types
🖥 4 main database types

📱Data Science 📱How To Be a Lead Data Scientist

🔅 How To Be a Lead Data Scientist 📝 Build a foundation and develop skills for seasoned data scientists to level up from mod
🔅 How To Be a Lead Data Scientist 📝 Build a foundation and develop skills for seasoned data scientists to level up from model builders to AI leaders. 🌐 Author: Matthew Blasa 🔰 Level: Advanced ⏰ Duration: 1h 6m 📋 Topics: Data Science, Team Management 🔗 Join Data Science for more courses

😉 A list of the best YouTube videos To learn data science 1️⃣ SQL language ⬅️ Learning 💰 4-hour SQL course from zero to one hundred 💰 Window functions tutorial ⬅️ Projects 📎 Starting your first SQL project 💰 Data cleansing project 💰 Restaurant order analysis ⬅️ Interview 💰 How to crack the SQL interview? ➖➖➖ 2️⃣ Python ⬅️ Learning 💰 12-hour Python for Data Science course ⬅️ Projects 💰 Python project for beginners 💰 Analyzing Corona Data with Python ⬅️ Interview 💰 Python interview golden tricks 💰 Python Interview Questions ➖➖➖ 3️⃣ Statistics and machine learning ⬅️ Learning 💰 7-hour course in applied statistics 💰 Machine Learning Training Playlist ⬅️ Projects 💰 Practical ML Project ⬅️ Interview 💰 ML Interview Questions and Answers 💰 How to pass a statistics interview? ➖➖➖ 4️⃣ Product and business case studies ⬅️ Learning 💰 Building strong product understanding 💰 Product Metric Definition ⬅️ Interview 💰 Case Study Analysis Framework 💰 How to shine in a business interview?

🖥 Data Analyst Roadmap
🖥 Data Analyst Roadmap

📖 Merging and Joining Data Working with multiple datasets? Combine them just like SQL: # Inner join (default) merged = pd.me
📖 Merging and Joining Data Working with multiple datasets? Combine them just like SQL:
# Inner join (default)
merged = pd.merge(df_sales, df_customers, on='customer_id')

# Left join
pd.merge(df_sales, df_customers, on='customer_id', how='left')

# Concatenate vertically
all_data = pd.concat([df_2023, df_2024], ignore_index=True)

# Join on index
df1.join(df2, on='date')
This wraps up our Data Manipulation Using Pandas Series.

📱Data Science 📱Ethical Hacking: SQL Injection

🔅 Ethical Hacking: SQL Injection 📝 Learn about the SQL command language and SQL injections. Examine SQL injections in MySQL
🔅 Ethical Hacking: SQL Injection 📝 Learn about the SQL command language and SQL injections. Examine SQL injections in MySQL, SQL Server, and Oracle XE, and discover how attackers defeat web application firewalls. 🌐 Author: Malcolm Shore 🔰 Level: Intermediate ⏰ Duration: 1h 45m 📋 Topics: Ethical Hacking, SQL Injection 🔗 Join Data Science for more courses