Data Science

📖 SQL data types

📖 Top 10 Database Scaling Techniques You Should Know: 1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to speed up data retrieval. 2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Upgrade your database server by adding more CPU, RAM, or storage to handle increased load. 3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time. 4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance. 5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability. 6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load. 7. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠: Reduce the overhead of opening/closing database connections by reusing existing ones, improving performance under heavy traffic. 8. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Split large tables into smaller, more manageable parts (partitions), each containing a subset of the columns/features from the original table. 9. 𝐃𝐞𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Store data in a redundant but structured format to minimize complex joins and speed up read-heavy workloads. 10. 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬: Pre-compute and store results of complex queries as separate tables to avoid expensive recalculation, reducing database load and improving response times.

📁 The in demand skills of a data analytics

🔅 PREMIUM CHANNELS -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 Web Development -◦-◦--◦--◦-◦--◦--◦-◦-- 220k| 🔰 Linkedin Learning 143k| 🔰 Udemy Premium 134k| 🔰 Web Development -◦-◦--◦- 121k| 🔰 Python 3 099k| 🔰 JavaScript Training 091k| 🔰 Machine Learning -◦-◦--◦- 071k| 🔰 Data Analysis and Databases 069k| 🔰 Artificial Intelligence 064k| 🔰 Linux and DevOps -◦-◦--◦- 064k| 🔰 React and NextJs 050k| 🔰 100 Days of Python 049k| 🔰 OpenAI Mastery -◦-◦--◦- 049k| 🔰 Business and Finance 044k| 🔰 Best Telegram Channels 042k| 🔰 Udemy Learning -◦-◦--◦- 040k| 🔰 Zero to Mastery 040k| 🔰 Mobile Apps 036k| 🔰 Linkedin Learning Courses -◦-◦--◦- 035k| 🔰 Codedamn Courses 034k| 🔰 React 101 031k| 🔰 Crypto Tutorials -◦-◦--◦- 031k| 🔰 Coding Interview 025k| 🔰 Telegram's Shorts 023k| 🔰 The Coding Space -◦-◦--◦- 023k| 🔰 Linux Training -◦-◦--◦--◦-◦--◦--◦-◦-- 🔰 Add Your Channel -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 2hrs on top & 8hrs in channel!

🔅 Python Data Structures: Dictionaries 📝 Learn how to use dictionaries to store and retrieve unordered data in Python. 🌐 Author: Deepa Muralidhar 🔰 Level: Beginner ⏰ Duration: 57m 📋 Topics: Data Structures, Python 🔗 Join Data Analysis for more courses

🔰 The 4 Types of SQL Joins SQL joins combine rows from two or more tables based on a related column. Here are the different types of joins you can use: 1⃣ Inner Join Returns only the matching rows between both tables. It keeps common data only. 🔢 Left Join Returns all rows from the left table and matching rows from the right table. If a row in the left table doesn’t have a match in the right table, the right table’s columns will contain NULL values in that row. 🔢 Right Join Returns all rows from the right table and matching rows from the left table. If no matching record exists in the left table for a record in the right table, the columns from the left table in the result will contain NULL values. 🔢 FULL OUTER JOIN Returns all rows from both tables, filling in NULL for missing matches.

📖 SQL cheat sheet - Every JOIN explained

🔅 PREMIUM CHANNELS -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 Web Development -◦-◦--◦--◦-◦--◦--◦-◦-- 220k| 🔰 Linkedin Learning 143k| 🔰 Udemy Premium 134k| 🔰 Web Development -◦-◦--◦- 121k| 🔰 Python 3 099k| 🔰 JavaScript Training 091k| 🔰 Machine Learning -◦-◦--◦- 071k| 🔰 Data Analysis and Databases 069k| 🔰 Artificial Intelligence 064k| 🔰 Linux and DevOps -◦-◦--◦- 064k| 🔰 React and NextJs 050k| 🔰 100 Days of Python 049k| 🔰 OpenAI Mastery -◦-◦--◦- 049k| 🔰 Business and Finance 044k| 🔰 Best Telegram Channels 042k| 🔰 Udemy Learning -◦-◦--◦- 040k| 🔰 Zero to Mastery 040k| 🔰 Mobile Apps 036k| 🔰 Linkedin Learning Courses -◦-◦--◦- 035k| 🔰 Codedamn Courses 034k| 🔰 React 101 031k| 🔰 Crypto Tutorials -◦-◦--◦- 031k| 🔰 Coding Interview 025k| 🔰 Telegram's Shorts 023k| 🔰 The Coding Space -◦-◦--◦- 023k| 🔰 Linux Training -◦-◦--◦--◦-◦--◦--◦-◦-- 🔰 Add Your Channel -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 2hrs on top & 8hrs in channel!

Python Developer (Twitter) Data Science Sandwich ! #python #datascience #machinelearning #deeplearning #ai #artificialintelligence #programming #developer #softwaredeveloper #computerscience #calculus #linearalgebra #algebra #maths #mathematics

🔅 Advanced NoSQL for Data Science 📝 Explore the fundamentals of NoSQL. Learn the differences between NoSQL and traditional relational databases, discover how to perform common data science tasks with NoSQL, and more. 🌐 Author: Dan Sullivan 🔰 Level: Advanced ⏰ Duration: 1h 54m 📋 Topics: Data Science, NoSQL 🔗 Join Data Analysis for more courses

📖 Types of Keys in SQL

🔅 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 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.

🔅 PREMIUM CHANNELS -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 Web Development -◦-◦--◦--◦-◦--◦--◦-◦-- 221k| 🔰 Linkedin Learning 143k| 🔰 Udemy Premium 134k| 🔰 Web Development -◦-◦--◦- 121k| 🔰 Python 3 099k| 🔰 JavaScript Training 091k| 🔰 Machine Learning -◦-◦--◦- 070k| 🔰 Data Analysis and Databases 069k| 🔰 Artificial Intelligence 064k| 🔰 React and NextJs -◦-◦--◦- 064k| 🔰 Linux and DevOps 050k| 🔰 100 Days of Python 049k| 🔰 OpenAI Mastery -◦-◦--◦- 049k| 🔰 Business and Finance 044k| 🔰 Best Telegram Channels 041k| 🔰 Udemy Learning -◦-◦--◦- 040k| 🔰 Zero to Mastery 040k| 🔰 Mobile Apps 036k| 🔰 Linkedin Learning Courses -◦-◦--◦- 035k| 🔰 Codedamn Courses 034k| 🔰 React 101 031k| 🔰 Crypto Tutorials -◦-◦--◦- 031k| 🔰 Coding Interview 025k| 🔰 Telegram's Shorts 023k| 🔰 The Coding Space -◦-◦--◦- 023k| 🔰 Linux Training -◦-◦--◦--◦-◦--◦--◦-◦-- 🔰 Add Your Channel -◦-◦--◦--◦-◦--◦--◦-◦--◦--◦-◦--◦- 🔰 2hrs on top & 8hrs in channel!

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