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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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📈 Аналитический обзор Telegram-канала Data Science

Канал Data Science (@sql_databases) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 70 805 подписчиков, занимая 2 274 место в категории Образование и 4 582 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 70 805 подписчиков.

Согласно последним данным от 25 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -309, а за последние 24 часа — -33, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 12.05%. В первые 24 часа после публикации контент обычно набирает 2.78% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 8 533 просмотров. В течение первых суток публикация набирает 1 972 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как database, learning, linkedin, udemy, 029k|.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

Благодаря высокой частоте обновлений (последние данные получены 26 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

70 805
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Архив постов
🔅 Introduction to PostgreSQL 📝 Get an introduction to PostgreSQL—what it is, what it can do, and how to start using it. 🌐
🔅 Introduction to PostgreSQL 📝 Get an introduction to PostgreSQL—what it is, what it can do, and how to start using it. 🌐 Author: Sarah Conway Schnurr 🔰 Level: Beginner ⏰ Duration: 48m 📋 Topics: PostgreSQL 🔗 Join Data Analysis for more courses

🔰 Math Topics every Data Scientist should know
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🔰 Math Topics every Data Scientist should know

🔗 YouTube Channels to learn Data Analysis
🔗 YouTube Channels to learn Data Analysis

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Unlocking the power of data analysis starts with understanding its foundation. Dive deep with me into the most pivotal distri
Unlocking the power of data analysis starts with understanding its foundation. Dive deep with me into the most pivotal distributions every data scientist should have in their toolkit. From Gaussian to Binomial, knowing these distributions is a game-changer in the realm of Data Science.

📱Data Analysis 📱Top Five Things to Know in Advanced SQL

🔅 Top Five Things to Know in Advanced SQL 📝 Learn advanced SQL concepts and practice them with hands-on exercises. 🌐 Autho
🔅 Top Five Things to Know in Advanced SQL 📝 Learn advanced SQL concepts and practice them with hands-on exercises. 🌐 Author: Kendall Ruber 🔰 Level: Advanced ⏰ Duration: 1h 35m 📋 Topics: SQL 🔗 Join Data Analysis for more courses

📖 SQL data types
+1
📖 SQL data types

📖 Top 10 Database Scaling Techniques You Should Know: 1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to s
📖 Top 10 Database Scaling Techniques You Should Know: 1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to speed up data retrieval. 2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Upgrade your database server by adding more CPU, RAM, or storage to handle increased load. 3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time. 4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance. 5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability. 6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load. 7. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠: Reduce the overhead of opening/closing database connections by reusing existing ones, improving performance under heavy traffic. 8. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Split large tables into smaller, more manageable parts (partitions), each containing a subset of the columns/features from the original table. 9. 𝐃𝐞𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Store data in a redundant but structured format to minimize complex joins and speed up read-heavy workloads. 10. 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬: Pre-compute and store results of complex queries as separate tables to avoid expensive recalculation, reducing database load and improving response times.

📁 The in demand skills of a data analytics
📁 The in demand skills of a data analytics

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📱Data Analysis 📱Python Data Structures: Dictionaries

🔅 Python Data Structures: Dictionaries 📝 Learn how to use dictionaries to store and retrieve unordered data in Python. 🌐 A
🔅 Python Data Structures: Dictionaries 📝 Learn how to use dictionaries to store and retrieve unordered data in Python. 🌐 Author: Deepa Muralidhar 🔰 Level: Beginner ⏰ Duration: 57m 📋 Topics: Data Structures, Python 🔗 Join Data Analysis for more courses

🔰 The 4 Types of SQL Joins SQL joins combine rows from two or more tables based on a related column. Here are the different
🔰 The 4 Types of SQL Joins SQL joins combine rows from two or more tables based on a related column. Here are the different types of joins you can use: 1⃣ Inner Join Returns only the matching rows between both tables. It keeps common data only. 🔢 Left Join Returns all rows from the left table and matching rows from the right table. If a row in the left table doesn’t have a match in the right table, the right table’s columns will contain NULL values in that row. 🔢 Right Join Returns all rows from the right table and matching rows from the left table. If no matching record exists in the left table for a record in the right table, the columns from the left table in the result will contain NULL values. 🔢 FULL OUTER JOIN Returns all rows from both tables, filling in NULL for missing matches.

📖 SQL cheat sheet - Every JOIN explained
📖 SQL cheat sheet - Every JOIN explained

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📦 Exercise Files

📱Data Analysis 📱Advanced NoSQL for Data Science

🔅 Advanced NoSQL for Data Science 📝 Explore the fundamentals of NoSQL. Learn the differences between NoSQL and traditional
🔅 Advanced NoSQL for Data Science 📝 Explore the fundamentals of NoSQL. Learn the differences between NoSQL and traditional relational databases, discover how to perform common data science tasks with NoSQL, and more. 🌐 Author: Dan Sullivan 🔰 Level: Advanced ⏰ Duration: 1h 54m 📋 Topics: Data Science, NoSQL 🔗 Join Data Analysis for more courses