SQL Roadmap
https://www.linkedin.com/posts/sql-analysts_a-beginners-roadmap-for-learning-sql-activity-7217377637046878208-eodZ

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SQL Cheatsheet
👇👇
https://www.linkedin.com/posts/sql-analysts_sql-cheat-sheet-here-is-a-quick-cheat-activity-7218105684964327424-GGHs?utm_source=share&utm_medium=member_android

Got an Interview tomorrow? Want to practice SQL & Python online?

Applying to jobs every day is great, but remember to practice too! When you get an interview unexpectedly, you'll be well-prepared and ready to shine!

Here are a some websites that can help you with practice and cracking the interviews:

https://www.linkedin.com/posts/sql-analysts_got-an-interview-tomorrow-want-to-practice-activity-7218187268685983744-W5KC

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Simple explanation of APPEND & MERGE two tables

Let’s think of SQL as a way to talk to a big toy box full of different toys.

### Imagine This:

1. Toy Box = Database: This is where all your toys (data) are stored.

2. Toys = Tables: Each type of toy is in its own section, like cars in one area and dolls in another.

3. Picking Toys = Queries: When you want to play with certain toys, you ask the toy box (database) to give them to you.

### Basic Commands:

1. SELECT: This is like saying, "Show me my cars!"
- Example: SELECT * FROM Cars; means "Show me all the cars!"

2. WHERE: This is like saying, "Show me only the red cars!"
- Example: SELECT * FROM Cars WHERE color = 'red';

3. INSERT: This is like adding a new toy to the toy box.
- Example: INSERT INTO Cars (color) VALUES ('blue'); means "Add a blue car!"

4. UPDATE: This is like changing a toy's color.
- Example: UPDATE Cars SET color = 'green' WHERE color = 'red'; means "Change all red cars to green."

5. DELETE: This is like taking a toy out of the toy box and saying goodbye.
- Example: DELETE FROM Cars WHERE color = 'blue'; means "Remove the blue car."

### Summary:

- SELECT = Look at your toys.
- WHERE = Choose specific toys.
- INSERT = Add new toys.
- UPDATE = Change your toys.
- DELETE = Remove toys.

That’s it! SQL helps you play with and organize your toys (data) in the best way!

Here are examples of how SQL is used in a product company:

### 1. Product Inventory Management

Scenario: Managing product details and stock levels.

- SELECT: View all products in inventory.
- Query: SELECT * FROM Products;

- WHERE: Find products that are out of stock.
- Query: SELECT * FROM Products WHERE stock_quantity = 0;

- INSERT: Add a new product to the inventory.
- Query: INSERT INTO Products (name, price, category, stock_quantity) VALUES ('Wireless Headphones', 150.00, 'Electronics', 100);

- UPDATE: Update the stock quantity after a sale.
- Query: UPDATE Products SET stock_quantity = stock_quantity - 1 WHERE product_id = 101;

- DELETE: Remove a discontinued product.
- Query: DELETE FROM Products WHERE product_id = 101;

### 2. Order Processing

Scenario: Managing customer orders.

- SELECT: View all customer orders.
- Query: SELECT * FROM Orders;

- WHERE: Find orders for a specific customer.
- Query: SELECT * FROM Orders WHERE customer_id = 123;

- INSERT: Add a new order.
- Query: INSERT INTO Orders (customer_id, order_date, total_amount) VALUES (123, '2024-07-14', 299.99);

- UPDATE: Update the status of an order.
- Query: UPDATE Orders SET status = 'Shipped' WHERE order_id = 456;

- DELETE: Cancel an order.
- Query: DELETE FROM Orders WHERE order_id = 456 AND status = 'Pending';

### 3. Customer Management

Scenario: Keeping track of customer information.

- SELECT: View all customer details.
- Query: SELECT * FROM Customers;

- WHERE: Find customers from a specific city.
- Query: SELECT * FROM Customers WHERE city = 'New York';

- INSERT: Add a new customer.
- Query: INSERT INTO Customers (name, email, phone, city) VALUES ('Jane Doe', 'jane@example.com', '555-1234', 'Los Angeles');

- UPDATE: Update a customer's contact information.
- Query: UPDATE Customers SET phone = '555-5678' WHERE customer_id = 123;

- DELETE: Remove a customer record.
- Query: DELETE FROM Customers WHERE customer_id = 123;

### 4. Sales Analytics

Scenario: Analyzing sales data for insights.

- SELECT: View total sales for a specific period.
- Query: SELECT SUM(total_amount) FROM Orders WHERE order_date BETWEEN '2024-01-01' AND '2024-06-30';

- WHERE: Find top-selling products.
- Query: SELECT product_id, SUM(quantity) FROM OrderDetails GROUP BY product_id ORDER BY SUM(quantity) DESC LIMIT 5;

- INSERT: Log a new sales report.
- Query: INSERT INTO SalesReports (report_date, total_sales) VALUES ('2024-07-01', 15000);

- UPDATE: Adjust sales forecasts based on new data.
- Query: UPDATE SalesForecast SET forecast_amount = 20000 WHERE forecast_month = '2024-07';

- DELETE: Remove outdated sales reports.
- Query: DELETE FROM SalesReports WHERE report_date < '2023-01-01';

In a product company, SQL is crucial for managing inventory, processing orders, handling customer data, and analyzing sales. These commands help streamline operations and provide valuable insights for decision-making.

𝗖𝗧𝗘𝘀 (𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀)

CTEs can make complex queries more readable and easier to maintain.

They are excellent for breaking down complex queries into simpler, more manageable parts.

CTEs are most efficient for organizing query logic but don't inherently improve performance.

Since CTEs do not store their results. They act as temporary views executed every time they are referenced.


𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗿𝘆 𝗧𝗮𝗯𝗹𝗲𝘀

But we also have Temporary tables, and they come with Performance through Persistence.

Temporary tables store data in the database's temporary storage (e.g., the tempdb in SQL Server).

On Temporary tables, you can also define indexes!!!

This physical storage and the indexes will make the reads or joins X times faster.


𝗧𝗟;𝗗𝗥:

CTEs are excellent for organizing and simplifying complex SQL queries without storing intermediate results, making them easy to read and maintain.

Temporary Tables are better suited for performance optimization of large datasets, indexing, and reducing the need for repeated computations.

CTEs are used because of their readability, but the performance on Temporary Tables is hard to ignore for large datasets.

To effectively learn SQL for a Data Analyst role, follow these steps:

1. Start with a basic course: Begin by taking a basic course on YouTube to familiarize yourself with SQL syntax and terminologies. I recommend the "Learn Complete SQL" playlist from the "techTFQ" YouTube channel.

2. Practice syntax and commands: As you learn new terminologies from the course, practice their syntax on the "w3schools" website. This site provides clear examples of SQL syntax, commands, and functions.

3. Solve practice questions: After completing the initial steps, start solving easy-level SQL practice questions on platforms like "Hackerrank," "Leetcode," "Datalemur," and "Stratascratch." If you get stuck, use the discussion forums on these platforms or ask ChatGPT for help. You can paste the problem into ChatGPT and use a prompt like:
- "Explain the step-by-step solution to the above problem as I am new to SQL, also explain the solution as per the order of execution of SQL."

4. Gradually increase difficulty: Gradually move on to more difficult practice questions. If you encounter new SQL concepts, watch YouTube videos on those topics or ask ChatGPT for explanations.

5. Consistent practice: The most crucial aspect of learning SQL is consistent practice. Regular practice will help you build and solidify your skills.

By following these steps and maintaining regular practice, you'll be well on your way to mastering SQL for a Data Analyst role.

SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.

Let’s dive into a comprehensive guide on SQL for Data Science!

I have broken it down into three key sections to help you:

𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.

𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.

𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.