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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

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📊 مؤشرات الجمهور والحراك

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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 أغسطس, 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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📖 Types of Keys in SQL
📖 Types of Keys in SQL

📱Data Analysis 📱Python in Excel: Getting Started with Data Analysis

🔅 Python in Excel: Getting Started with Data Analysis 📝 Explore the core concepts and fundamental skills of working with da
🔅 Python in Excel: Getting Started with Data Analysis 📝 Explore the core concepts and fundamental skills of working with data using Python in Microsoft Excel. 🌐 Author: Joe Marini 🔰 Level: Intermediate ⏰ Duration: 1h 40m 📋 Topics: Data Analysis, Microsoft Excel, Python 🔗 Join Data Analysis for more courses

📊 Your Data Analyst journey doesn’t start with tools — it starts with a roadmap. From mastering Excel & SQL ➝ understanding
📊 Your Data Analyst journey doesn’t start with tools — it starts with a roadmap. From mastering Excel & SQL ➝ understanding statistics ➝ working with Python & visualization tools ➝ building real-world projects — a clear Data Analyst roadmap can save you months of confusion and wrong learning choices. If you’re serious about breaking into analytics in 2026, you don’t need random tutorials. You need structured learning, hands-on practice, and industry-relevant skills.

📖🔰 Pandas vs SQL: Most Common Operations Comparison
📖🔰 Pandas vs SQL: Most Common Operations Comparison

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80% of data problems can be solved with just 16 SQL functions. I’ve been working with data for years and this truth keeps pro
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80% of data problems can be solved with just 16 SQL functions. I’ve been working with data for years and this truth keeps proving itself: You don’t need fancy tools. You need to master the fundamentals. For data analysts, data scientists, and data engineers: SQL isn’t optional. Because data lives in databases. And databases speak SQL-ish. Most problems fall into 2 categories: Aggregate functions (summarise data): SUM() - Total revenue COUNT() - Total orders AVG() - Average purchase value MIN() - Smallest sale MAX() - Biggest transaction STRING_AGG() - Combine text values Window functions (compare rows): ROW_NUMBER() - Pagination RANK() - Leaderboards with ties DENSE_RANK() - Performance tiers NTILE() - Split into quartiles LEAD() - Compare current vs next LAG() - Compare current vs previous FIRST_VALUE() - Highest value per group LAST_VALUE() - Lowest value per group SUM() OVER() - Running totals AVG() OVER() - Moving averages Aggregates collapse rows → one summary result Window functions keep all rows → add calculations across them

📦 Exercise Files

📱Data Analysis 📱MySQL Installation and Configuration

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📁 Mastering SQL
📁 Mastering SQL

📖 SQL Basics
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📖 SQL Basics

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📖 Brain of Data Analyst
📖 Brain of Data Analyst