ar
Feedback
Data Science

Data Science

الذهاب إلى القناة على Telegram

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

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Data Science

تُعد قناة Data Science (@sql_databases) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 70 985 مشتركاً، محتلاً المرتبة 2 262 في فئة التعليم والمرتبة 4 575 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 70 985 مشتركاً.

بحسب آخر البيانات بتاريخ 26 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -11، وفي آخر 24 ساعة بمقدار -29، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 10.67‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.43‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 7 573 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 723 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 0.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل database, learning, linkedin, udemy, 029k|.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 27 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

70 985
المشتركون
-2924 ساعات
-537 أيام
-1130 أيام
أرشيف المشاركات
📋 Checklist to become Data Analyst
📋 Checklist to become Data Analyst

🔰 SQL CheatSheet 🔰
+1
🔰 SQL CheatSheet 🔰

📖 Data Structure Cheat Sheet
📖 Data Structure Cheat Sheet

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

🔅 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

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

📱Data Science 📱Data Science Reporting with Quarto for Python

🔅 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

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

🖥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