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Data Science

Data Science

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Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

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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 70 985 suscriptores, ocupando la posición 2 262 en la categoría Educación y el puesto 4 575 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 985 suscriptores.

Según los últimos datos del 26 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -11, y en las últimas 24 horas de -29, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 10.67%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.43% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 7 573 visualizaciones. En el primer día suele acumular 1 723 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 27 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.

70 985
Suscriptores
-2924 horas
-537 días
-1130 días
Archivo de publicaciones
Data Science
Data Science
70 984
📋 Checklist to become Data Analyst
📋 Checklist to become Data Analyst

Data Science
Data Science
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🔰 SQL CheatSheet 🔰
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🔰 SQL CheatSheet 🔰

Data Science
Data Science
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📖 Data Structure Cheat Sheet
📖 Data Structure Cheat Sheet

Data Science
Data Science
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📱Data Science 📱Advanced Python: Top Tools for Data Science and Engineering

Data Science
Data Science
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🔅 Advanced Python: Top Tools for Data Science and Engineering 📝 This comprehensive course is designed to equip you with the
🔅 Advanced Python: Top Tools for Data Science and Engineering 📝 This comprehensive course is designed to equip you with the essential skills for data analysis and application development using Python and popular data tools and libraries. 🌐 Author: Joe Marini 🔰 Level: Intermediate ⏰ Duration: 2h 5m 📋 Topics: Pandas, Data Engineering, Data Science 🔗 Join Data Science for more courses

Data Science
Data Science
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Key Pandas Functions for Data Importing, Cleaning, and Statistics. Boost your data analysis workflow with essential Python co
Key Pandas Functions for Data Importing, Cleaning, and Statistics. Boost your data analysis workflow with essential Python commands

Data Science
Data Science
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🔢 Data Cleaning Tips Every Analyst Should Know If your analysis feels off, it’s probably your data. These 5 tips will help y
🔢 Data Cleaning Tips Every Analyst Should Know If your analysis feels off, it’s probably your data. These 5 tips will help you clean your dataset like a pro: ✔️ Handle missing values ✔️ Remove duplicates ✔️ Fix data types ✔️ Standardize formats ✔️ Detect and remove outliers Clean data = better insights = better decisions.

Data Science
Data Science
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📖 Data Structures, you need to know for Coding interview
📖 Data Structures, you need to know for Coding interview

Data Science
Data Science
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📱Data Science 📱Data Science Reporting with Quarto for Python

Data Science
Data Science
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🔅 Data Science Reporting with Quarto for Python 📝 Leverage the power of Quarto to build publication-quality reports, engagi
🔅 Data Science Reporting with Quarto for Python 📝 Leverage the power of Quarto to build publication-quality reports, engaging presentation decks, and rich interactive webpages from Jupyter Notebook for Python. 🌐 Author: Charlie Joey Hadley 🔰 Level: Intermediate ⏰ Duration: 2h 26m 📋 Topics: Data Reporting, Data Science, Data Analytics 🔗 Join Data Science for more courses

Data Science
Data Science
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🔄 Life Cycle of a Data Analytical Project
🔄 Life Cycle of a Data Analytical Project

Data Science
Data Science
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🖥Type of Databases
🖥Type of Databases

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

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

Data Science
Data Science
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📱Data Science 📱Decision Intelligence: Data Stories

Data Science
Data Science
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🔅 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

Data Science
Data Science
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🖥 8 Common database types explained
🖥 8 Common database types explained

Data Science
Data Science
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📖 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.)

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