SQL For Data Analysis

Learn inner join basics by linking customer and order data on customer_id to show each order with the customer name, and note outer join for customers with no orders.

Explore left outer join and right outer join to retrieve matching and non-matching rows, with examples using orders and customers tables.

Join the customers and orders tables on customer_id to fetch name and amount, compare inner join, left outer join, and see how left joins include customers with no orders.

Apply SQL aggregations to analyze data by counting rows, summing values, averaging, and finding minimum and maximum spending, with a brief note on handling NULL.

Null indicates no data, not zero, and can result from missing values; some businesses define logic for null, such as treating null membership as non-premium.

Learn the difference between null and empty string in SQL, see how missing values affect stored data, and use is null to filter null records.

The count function returns the number of rows that meet a given criteria. For gender it returns four, while the name column yields three because one value is null.

Use the sum function to calculate the total amount paid by customer 8269 from the payment table, noting that null values are treated as zero.

Use the max, min, and average functions to compute the maximum, minimum, and average values in a column.

Use group by to compute total amounts paid per customer, then apply order by to sort results by customer ID. Expand grouping to include staff for per-staff totals.

Use the distinct keyword to return unique values from the payment table, such as identifying distinct customers. This lesson demonstrates how distinct reduces duplicates to a set of unique rows.

Learn how to filter aggregated data using the having clause to select groups where sum(amount) exceeds 100.

Learn how to use the case statement to categorize customers as super or standard based on their esos amount, returning values when conditions are met.

Learn how subqueries fetch related data across tables, such as payments for customers over 30 or updating points for those with total paid over 1000.

Clean data in two use cases shows how to extract country codes and mobile numbers and format dates from strings using left, right, position, lower, upper, concatenate, pipe, and cast.