Data Science

🔅 Introduction to PostgreSQL 📝 Get an introduction to PostgreSQL—what it is, what it can do, and how to start using it. 🌐 Author: Sarah Conway Schnurr 🔰 Level: Beginner ⏰ Duration: 48m 📋 Topics: PostgreSQL 🔗 Join Data Analysis for more courses

🔰 Math Topics every Data Scientist should know

🔗 YouTube Channels to learn Data Analysis

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Unlocking the power of data analysis starts with understanding its foundation. Dive deep with me into the most pivotal distributions every data scientist should have in their toolkit. From Gaussian to Binomial, knowing these distributions is a game-changer in the realm of Data Science.

🔅 Top Five Things to Know in Advanced SQL 📝 Learn advanced SQL concepts and practice them with hands-on exercises. 🌐 Author: Kendall Ruber 🔰 Level: Advanced ⏰ Duration: 1h 35m 📋 Topics: SQL 🔗 Join Data Analysis for more courses

📖 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

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🔅 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

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🔅 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