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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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📈 تحلیل کانال تلگرام Data Science

کانال Data Science (@sql_databases) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 71 042 مشترک است و جایگاه 2 273 را در دسته آموزش و رتبه 4 764 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 71 042 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 05 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -54 و در ۲۴ ساعت گذشته برابر 6 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 12.21% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.97% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 8 672 بازدید دریافت می‌کند. در اولین روز معمولاً 2 110 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 0 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند database, learning, linkedin, udemy, 029k| تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 07 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 آموزش تبدیل کرده‌اند.

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🖥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

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🔅 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
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😉 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

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