Day 1: Introduction to SQL Built-in Functions

Introduction: SQL built-in functions are pre-defined operations that perform calculations, manipulate data, and transform query results. These functions simplify complex tasks and enhance the power of SQL queries. This session introduces the concept of SQL functions, their categories, and how to use them effectively to process and analyze data.

Learning Objectives: By the end of this session, you will be able to understand what SQL built-in functions are and why they are useful, identify different categories of SQL functions: scalar, aggregate, string, numeric, and date/time, use basic SQL functions in queries to manipulate and calculate data, and recognize the syntax and usage of common SQL functions.

Scope: This session covers an overview of SQL built-in functions, focusing on their types and basic usage. You will learn how to apply functions to columns in your queries to perform calculations, format data, and extract information.

Background Information: SQL functions are categorized based on their purpose:

• Scalar functions operate on a single value and return a single value (e.g., UPPER(), ROUND()).
• Aggregate functions perform calculations on multiple rows and return a single summary value (e.g., SUM(), AVG()).
• String functions manipulate text data (e.g., CONCAT(), SUBSTRING()).
• Numeric functions perform mathematical operations (e.g., ABS(), CEILING()).
• Date/time functions handle date and time values (e.g., NOW(), DATEPART()).

Hands-On Example: Suppose you have a table called Sales:

-- Convert product names to uppercase
SELECT UPPER(ProductName) AS ProductNameUpper
FROM Sales;

-- Round sales amount to 2 decimal places
SELECT ROUND(SaleAmount, 2) AS RoundedAmount
FROM Sales;

-- Get the current date and time
SELECT NOW() AS CurrentDateTime;

-- Concatenate first and last names
SELECT CONCAT(FirstName, ' ', LastName) AS FullName
FROM Sales;
                

Interpretation: The first query converts product names to uppercase for consistent formatting. The second rounds sales amounts to two decimal places for financial reporting. The third retrieves the current date and time from the database server. The fourth combines first and last names into a full name, demonstrating string manipulation.

Supplemental Information:

• SQL Functions Overview (W3Schools)
• SQL Built-in Functions (TutorialsPoint)
• SQL Functions Explained (YouTube)

Discussion Points:

• What are the benefits of using built-in SQL functions?
• How do scalar and aggregate functions differ?
• When would you use string functions versus numeric functions?
• How can date/time functions help in data analysis?
• Are there any limitations or considerations when using SQL functions?

Day 2: Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)

Introduction: Aggregate functions in SQL perform calculations on multiple rows of data and return a single summarized value. These functions are essential for data analysis, reporting, and summarizing large datasets. This session introduces the most commonly used aggregate functions: SUM, AVG, COUNT, MIN, and MAX, and demonstrates how to apply them in SQL queries.

Learning Objectives: By the end of this session, you will be able to understand the purpose and syntax of key aggregate functions, use SUM to calculate the total of numeric columns, use AVG to find the average value, use COUNT to count rows or non-null values, use MIN and MAX to find the smallest and largest values, and apply aggregate functions in queries to summarize data.

Scope: This session covers the use of aggregate functions in SQL, including their syntax and practical examples. You will learn how to summarize data across rows and how these functions can be combined with other SQL clauses.

Background Information: Aggregate functions operate on sets of rows and return a single value. They are often used with the GROUP BY clause to calculate summaries for groups of data. These functions ignore NULL values except for COUNT(*), which counts all rows regardless of NULLs.

Hands-On Example: Suppose you have a table called Sales:

-- Calculate total sales amount
SELECT SUM(SaleAmount) AS TotalSales
FROM Sales;

-- Calculate average sale amount
SELECT AVG(SaleAmount) AS AverageSale
FROM Sales;

-- Count the number of sales transactions
SELECT COUNT(*) AS TotalTransactions
FROM Sales;

-- Find the minimum sale amount
SELECT MIN(SaleAmount) AS SmallestSale
FROM Sales;

-- Find the maximum sale amount
SELECT MAX(SaleAmount) AS LargestSale
FROM Sales;
                

Interpretation: The queries calculate key summary statistics: total sales, average sale amount, total number of transactions, smallest sale, and largest sale. These aggregate functions provide valuable insights into the dataset and are foundational for reporting and analysis.

Supplemental Information:

• SQL Aggregate Functions (W3Schools)
• Aggregate Functions Tutorial (TutorialsPoint)
• SQL Aggregate Functions Explained (YouTube)

Discussion Points:

• How do aggregate functions simplify data analysis?
• What is the difference between COUNT(*) and COUNT(column_name)?
• How do aggregate functions handle NULL values?
• When would you use MIN and MAX in business reporting?
• How can aggregate functions be combined with GROUP BY?

Day 3: Grouping Data with GROUP BY

Introduction: The GROUP BY clause in SQL is used to arrange identical data into groups, making it possible to perform aggregate calculations for each group. This is essential for summarizing data, generating reports, and analyzing trends within categories. This session introduces the syntax and usage of GROUP BY, showing how it works with aggregate functions to produce grouped summaries.

Learning Objectives: By the end of this session, you will be able to understand the purpose and syntax of the GROUP BY clause, use GROUP BY with aggregate functions to summarize data by categories, write queries that group data by one or more columns, and recognize the difference between grouped and ungrouped aggregate queries.

Scope: This session covers the use of GROUP BY in SQL queries, including grouping by single or multiple columns and combining with aggregate functions like SUM, AVG, COUNT, MIN, and MAX. You will learn how to generate meaningful summaries for business analysis and reporting.

Background Information: The GROUP BY clause is used after the WHERE clause and before the ORDER BY clause in a SQL statement. It groups rows that have the same values in specified columns into summary rows, such as totals or averages for each group. Aggregate functions are typically used in the SELECT statement with GROUP BY.

Hands-On Example: Suppose you have a table called Sales:

-- Total sales amount by product
SELECT ProductID, SUM(SaleAmount) AS TotalSales
FROM Sales
GROUP BY ProductID;

-- Average sale amount by salesperson
SELECT SalespersonID, AVG(SaleAmount) AS AverageSale
FROM Sales
GROUP BY SalespersonID;

-- Count of sales transactions by region
SELECT Region, COUNT(*) AS TransactionCount
FROM Sales
GROUP BY Region;
                

SQL GROUP BY Clause (W3Schools)

GROUP BY Tutorial (TutorialsPoint)

SQL GROUP BY Explained (YouTube)

Discussion Points:

• Why is grouping data important in business analysis?
• How does GROUP BY work with aggregate functions?
• What is the difference between grouping by one column and multiple columns?
• Can you use GROUP BY without aggregate functions?
• What are some common mistakes when using GROUP BY?

Day 4: Filtering Groups with HAVING

Introduction: The HAVING clause in SQL is used to filter groups created by the GROUP BY clause. Unlike the WHERE clause, which filters individual rows before grouping, HAVING filters aggregated data after grouping. This session introduces the syntax and usage of HAVING to refine grouped query results based on aggregate conditions.

Learning Objectives: By the end of this session, you will be able to understand the difference between WHERE and HAVING clauses, use the HAVING clause to filter groups based on aggregate conditions, write queries that combine GROUP BY and HAVING to produce filtered summaries, and apply multiple conditions in the HAVING clause using logical operators.

Scope: This session covers the use of the HAVING clause in SQL queries, focusing on filtering grouped data. You will learn how to apply conditions on aggregates like SUM, COUNT, AVG, and how to combine HAVING with GROUP BY for advanced data analysis.

Background Information: The HAVING clause was introduced to address the limitation of the WHERE clause, which cannot filter aggregated data. It is placed after the GROUP BY clause and filters groups based on aggregate function results. This allows for queries like finding groups with totals above a certain threshold or counts exceeding a specific number.

Hands-On Example: Suppose you have a table called Sales:

-- Find products with total sales greater than 1000
SELECT ProductID, SUM(SaleAmount) AS TotalSales
FROM Sales
GROUP BY ProductID
HAVING SUM(SaleAmount) > 1000;

-- Find salespersons with more than 10 sales transactions
SELECT SalespersonID, COUNT(*) AS TransactionCount
FROM Sales
GROUP BY SalespersonID
HAVING COUNT(*) > 10;

-- Find regions with average sale amount less than 500
SELECT Region, AVG(SaleAmount) AS AverageSale
FROM Sales
GROUP BY Region
HAVING AVG(SaleAmount) < 500;
                

Interpretation: The first query returns products whose total sales exceed 1000. The second lists salespersons with more than 10 transactions. The third identifies regions where the average sale amount is below 500. These examples demonstrate how HAVING filters groups based on aggregate criteria, enabling targeted analysis.

Supplemental Information:

• SQL HAVING Clause (W3Schools)
• HAVING Clause Tutorial (TutorialsPoint)
• SQL HAVING Explained (YouTube)

Discussion Points:

• How does HAVING differ from WHERE in filtering data?
• Why is HAVING necessary when working with grouped data?
• Can you use multiple conditions in a HAVING clause?
• How does HAVING interact with aggregate functions?
• What are some practical use cases for the HAVING clause?

Day 5: Performing Calculations and Using Expressions

Introduction: SQL allows you to perform calculations and use expressions directly within your queries to manipulate data, create new computed columns, and derive insights. This session introduces how to write arithmetic expressions, use built-in functions in calculations, and combine multiple operations to transform data dynamically.

Learning Objectives: By the end of this session, you will be able to write arithmetic expressions in SQL queries, use SQL functions within expressions to manipulate data, create computed columns using expressions, combine multiple operations and functions in a single query, and understand operator precedence and how it affects calculations.

Scope: This session covers the use of arithmetic operators (+, -, *, /), expressions, and functions in SQL queries. You will learn how to perform calculations on columns, create new calculated fields, and use expressions for data transformation and analysis.

Background Information: SQL supports arithmetic operations and expressions that can be used in the SELECT statement and elsewhere. Expressions can include column names, constants, operators, and functions. Understanding operator precedence (e.g., multiplication before addition) is important to ensure correct results. Calculated columns are useful for generating new insights without altering the underlying data.

Hands-On Example: Suppose you have a table called Orders:

-- Calculate total price including tax (10%)
SELECT OrderID, SaleAmount, SaleAmount * 1.10 AS TotalPriceWithTax
FROM Orders;

-- Calculate discount price (20% off)
SELECT OrderID, SaleAmount, SaleAmount * 0.80 AS DiscountPrice
FROM Orders;

-- Calculate profit as difference between sale and cost
SELECT OrderID, SaleAmount, CostAmount, SaleAmount - CostAmount AS Profit
FROM Orders;

-- Combine functions and expressions: round profit to 2 decimals
SELECT OrderID, ROUND(SaleAmount - CostAmount, 2) AS RoundedProfit
FROM Orders;
                

Interpretation: The first query calculates the total price by adding 10% tax to the sale amount. The second applies a 20% discount. The third computes profit by subtracting cost from sale amount. The fourth rounds the profit to two decimal places. These examples illustrate how expressions and functions can be combined to perform dynamic calculations in SQL.

Supplemental Information:

• SQL Arithmetic Operators (W3Schools)
• SQL Expressions Tutorial (TutorialsPoint)
• SQL Calculations Explained (YouTube)

Discussion Points:

• How do arithmetic expressions enhance SQL queries?
• What is operator precedence, and why is it important?
• How can calculated columns be used in reports and analysis?
• Can expressions include both columns and constants?
• What are some common use cases for combining functions and expressions?