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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

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Data Science

تُعد قناة Data Science (@sql_databases) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 71 041 مشتركاً، محتلاً المرتبة 2 273 في فئة التعليم والمرتبة 4 764 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 71 041 مشتركاً.

بحسب آخر البيانات بتاريخ 05 يونيو, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -54، وفي آخر 24 ساعة بمقدار 6، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 12.21‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 2.97‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 8 672 مشاهدة. وخلال اليوم الأول يجمع عادةً 2 110 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 06 يونيو, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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📱Data Analysis 📱SQL Practice: Basic Queries

🔅 SQL Practice: Basic Queries 📝 Practice writing basic queries in SQL in this hands-on, interactive course with coding chal
🔅 SQL Practice: Basic Queries 📝 Practice writing basic queries in SQL in this hands-on, interactive course with coding challenges in CoderPad. 🌐 Author: David Gassner 🔰 Level: Beginner ⏰ Duration: 17m 📋 Topics: SQL 🔗 Join Data Analysis for more courses

📖 Roles and Responsibilities in Big Data Technology
📖 Roles and Responsibilities in Big Data Technology

📊 9 Key Database Types 🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial). 🔗 Blockchain: Secure, immut
📊 9 Key Database Types 🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial). 🔗 Blockchain: Secure, immutable ledgers (BigchainDB, IBM Blockchain). 🌐 Distributed: Scales across servers (Cassandra, Amazon DynamoDB). ⚡️ In-Memory: Lightning-fast access (Redis, Memcached, H2). 🗂 NoSQL: Flexible, schema-free (MongoDB, Couchbase, HBase). 📋 Relational: Structured with tables & SQL (MySQL, PostgreSQL, Oracle). 🧩 Object-Oriented: Models complex objects (db4o, Object DB). 🕸 Graph: Perfect for relationships (Neo4j, Amazon Neptune). ⏱️ Time-Series: Optimized for timestamps (InfluxDB, Prometheus). Pick the right tool for your data challenge.

📖 6 Steps of Data Cleaning Every Data Analyst Should Know
📖 6 Steps of Data Cleaning Every Data Analyst Should Know

📱Data Analysis 📱NoSQL Essential Training

🔅 NoSQL Essential Training 📝 Get a high-level view of the basics of NoSQL, from how it differs from relational databases to
🔅 NoSQL Essential Training 📝 Get a high-level view of the basics of NoSQL, from how it differs from relational databases to its pros and cons. 🌐 Author: Melanie McGee 🔰 Level: Beginner ⏰ Duration: 43m 📋 Topics: NoSQL 🔗 Join Data Analysis for more courses

📖 Data Analytic Skills that will get you hired
📖 Data Analytic Skills that will get you hired

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📖 Data Science
📖 Data Science

📖 Data Visualization CheatSheet
📖 Data Visualization CheatSheet

📦 Exercise Files

📱Data Analysis 📱Learning Apache Airflow

🔅 Learning Apache Airflow 📝 Get an introduction to Apache Airflow—its uses, structure, how to get it up and running, and ho
🔅 Learning Apache Airflow 📝 Get an introduction to Apache Airflow—its uses, structure, how to get it up and running, and how to create and execute workflows. 🌐 Author: Janani Ravi 🔰 Level: Advanced ⏰ Duration: 2h 10m 📋 Topics: Apache Airflow, IT Automation 🔗 Join Data Analysis for more courses

Here’s a practical, code-first playbook for exploring numerical data 👇 How we approach EDA (with Python code + outputs): - B
+3
Here’s a practical, code-first playbook for exploring numerical data 👇 How we approach EDA (with Python code + outputs): - Basics: shape, dtypes, and missing values. - Descriptives: mean/median, variance, and percentiles for quick sanity checks. - Distributions: histograms, boxplots, density to spot skew and spread. - Relationships: scatter plots and a correlation heatmap to find signals. - Outliers: z-scores/IQR to flag anomalies worth investigating. - Scaling: MinMax vs Z-score depending on the model and metric. - Segments: groupby comparisons to surface patterns you miss in globals. - Decisions: tie insights back to the question and next steps. What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.

📁 Master Excel like a pro Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost yo
📁 Master Excel like a pro Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost your productivity!

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📖 SQL Joins
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📖 SQL Joins

📱Data Analysis 📱SQL Practice: Intermediate Queries