SQL Sunday #1 - Essentials

For everyone taking a step to work in the realm of Data Science/Analytics, the first step is to learn SQL (Structured Query Language). As a Data Analyst for about 3 years now while studying for a master in Business Analytics, I've decided as a good review and to improve my knowledge in SQL that I will be posting weekly content on Sundays related to SQL. To start of this first post I'm going to list down 4 essential things to learn when working with SQL.

1. CASE statements
Second to the WHERE clause, I use CASE statements on a daily basis when I need to identify certain values to specific categories. I do advise to be mindful of the order of executions of each condition you create because it may give misleading results. CASE can be used to derive new columns or values based on specific criteria, making it easier to produce complex reports directly from your SQL queries without further processing.

CASE
    WHEN condition1 THEN result1
    WHEN condition2 THEN result2
    WHEN conditionN THEN resultN
    ELSE result
END;

2. WHERE clause
In my opinion, the WHERE clause is like a Swiss army knife. Once you understand how to use it properly, it can solve a wide variety of things whether to pull data based on a certain value in a column to limiting the amount of data I want to see reducing my query cost. Retrieving unnecessary rows from the database consumes resources and is inefficient. The WHERE clause helps you get only what you need, conserving both database processing time and network bandwidth.

SELECT column1, column2, ...
FROM table_name
WHERE condition;

*3. JOINs *
At this point of my career, I can confidently say that more than half of my work would not be possible without the ability to join data tables together. JOINs help with expanding my analysis into answering more complex questions that may need to look into multiple tables at once. Instead of retrieving data from multiple tables separately and then combining them in application code, SQL JOIN allows us to fetch the combined data directly, which is usually more efficient.

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
INNER JOIN table2
ON table1.matching_column = table2.matching_column;

4. Aggregate functions (AVG(),SUM(),MAX(),MIN(),COUNT())
Aggregate functions in SQL are indispensable for data analysis and reporting in relational databases. They allow for summarizing, analyzing, and computing data on large datasets to generate meaningful insights. By providing a means to analyze and summarize data, aggregate functions directly support business intelligence and decision-making processes. Business leaders can make informed decisions based on summarized data insights.