SQL Break-Down

In the last mail, you learned the basics as well as the advanced Excel, including the solutions to possible problems that you might face while learning Excel.

Today’s email is gonna be focused on the 2nd Data Science tool, SQL. Stay with me now because this is going to be a bit detailed!

Let’s begin by giving a brief intro to SQL…

SQL (Structured Query Language), is a language used to communicate with and manage databases. It’s like a special language that helps you ask questions and retrieve specific information from large sets of data stored in databases.

When it comes to the Basics, SQL is all about writing commands to interact with databases. 

You can use it to do things like:

• Create tables to store data

• Insert new data into those tables

• Retrieve specific data that you're interested in

• Update existing data

• Delete data you don't need anymore. 

It's like having a set of powerful tools to organize and manage your data efficiently.

With the basics covered, now let’s move to the Intermediate SQL Concepts:

• Joins - helps you link information from multiple tables based on a common value, like matching a student's ID in one table with their course ID in another.

• Group By - helps you by grouping your data based on a certain characteristic, like grouping all the candies by their type.

• Having - helps you filter data. However, Having is used specifically with Group By to filter grouped data based on some condition.

• Subqueries -  these are like mini-queries within a larger query. You can use them to break down complex problems into smaller, more manageable parts.

• Indexes - helps the database find data more quickly, especially when dealing with large amounts of data. By creating indexes on certain columns, you can speed up your queries.

These concepts build on the basics of SQL and allow you to perform more complex operations and analysis on your data.

Now let’s move towards the Advanced Stuff…

Here are 5 main Advanced SQL Techniques:

1. Window Functions - allows you to perform calculations across a set of rows related to the current row. For example, you can calculate running totals or find the rank of each row within a group.

2. Common Table Expressions (CTEs) - help break down complex queries into more manageable chunks by allowing you to define a subquery and reference it later in your main query.

3. Recursive Queries - useful for working with hierarchical data structures like organizational charts or bill of materials.

4. Transactions - ensures that a series of database operations either all succeed or all fail together. This is important for maintaining data integrity, especially in situations where multiple operations need to be executed as a single unit.

5. Stored Procedures and Functions - reusable blocks of SQL code that you can call from other parts of your application. Stored procedures are precompiled and stored in the database, while functions return a value and can be used within SQL queries.

These advanced techniques give you more power and flexibility in working with your data, allowing you to perform complex analyses and create efficient, scalable database solutions.

However, this alone isn’t enough because you’re still gonna face some problems while getting hands-on experience of SQL.

Difficulty in grasping the syntax and structure of SQL queries?

Start with simple SELECT statements to retrieve data from a single table. Gradually introduce concepts like filtering (WHERE clause), sorting (ORDER BY), and aggregating (GROUP BY). Practice writing queries regularly and refer to SQL cheat sheets or online tutorials for guidance.

Confusion when joining tables to retrieve data from multiple sources?

Learn the different types of joins (e.g., INNER JOIN, LEFT JOIN) and understand their purposes. Pay attention to common join conditions and how they relate to the data you're querying. Practice joining tables using sample datasets to reinforce understanding

Difficulty in dealing with NULL values and understanding their impact on query results?

Recognize that NULL represents missing or unknown data. Use IS NULL or IS NOT NULL conditions to filter NULL values appropriately. Consider using COALESCE or IFNULL functions to handle NULL values in calculations or aggregations. 

Be cautious when comparing NULL values, as they behave differently than other values in SQL comparisons.

Now you’ve gained a better understanding of SQL than 99% of people outside of this newsletter.

However, if you want to learn Data Science on a much deeper level, click the link down below and become the top 1% of Data Scientists.

JOIN THE TOP 1%

-Sasi

P.S.- Tomorrow we will dive deep into Python so stay tuned for that.