🔅 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

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📱Python in Excel: Getting Started with Data Analysis

📖 Types of Keys in SQL

🔅 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

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📦 Exercise Files

📖 SQL cheat sheet - Every JOIN explained

🔰 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.

🔅 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

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📱Python Data Structures: Dictionaries

📁 The in demand skills of a data analytics

📖 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.

🔅 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

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📱Top Five Things to Know in Advanced SQL

🔗 YouTube Channels to learn Data Analysis