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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 70 803 مشترک است و جایگاه 2 261 را در دسته آموزش و رتبه 4 562 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 70 803 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 26 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -310 و در ۲۴ ساعت گذشته برابر -15 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 11.21% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.74% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 7 934 بازدید دریافت می‌کند. در اولین روز معمولاً 1 943 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 27 اوت, 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.

70 803
مشترکین
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-1277 روز
-31030 روز
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📱Data Science 📱How To Be a Lead Data Scientist

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🖥 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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SQL
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SQL is way easier when you actually know what matters. These are the core basics every beginner needs to build projects, answ
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SQL is way easier when you actually know what matters. These are the core basics every beginner needs to build projects, answer real business questions, and stop feeling overwhelmed 📊 Master these first and everything else becomes 10x easier. Save this to review later ✅

📖 SQL Learning Roadmap — 8 Key Steps: 1. Basic: Start by understanding what SQL is, why it’s used, and the different types o
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📖 4 Main in Database Types
📖 4 Main in Database Types

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📖 Data Visualisation CheatSheet
📖 Data Visualisation CheatSheet

📖 Data Analyst
📖 Data Analyst

📖 MOST COMMON SQL INTERVIEW QUESTION Do you knew this? 👉 “What is the order of execution in an SQL query?” Don’t let the SE
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📦 Exercise Files

📱Data Science 📱PostgreSQL Essential Training

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