SQL Tutorial for Beginners - Learn SQL using 30 scenarios

Learn to install a school server on Mac and to install a SQL Server on Windows, as an installation introduction for SQL beginners.

Install docker on mac and set up a Microsoft SQL Server container, then connect with Azure Data Studio to restore the AdventureWorks 2017 backup and explore 71 tables.

Install SQL Server on Windows, set up a data studio tool, and connect with the saved connection string to restore the Adventure Works sample database for practice.

Connect to the school server, inspect the sample database, and sort country names alphabetically using asc, then desc to practice data ordering.

Learn to analyze product feedback in sql by querying a four-column table (product, reviewer, rating, comments), use length and order by to surface the longest comments for deeper insight.

Select four specific columns from the production table and display only those, then limit results to the top 10 percent with rounding to the nearest whole number of rows.

Identify unique credit card types in the sales table using the distinct function to remove duplicates. Explore selecting the card type column and viewing the deduplicated results.

Scenario 5 shows querying the product_review table for product_id, reviewer, and rating, then using a case statement to display descriptive text such as poor, good, very good, and excellent.

Learn to query a sample database, select three columns (bill of materials, product assembly, start date), and replace nulls with zero in product assembly using is null—without altering data.

Learn how to alias a table and specific columns in SQL, renaming product model and product description in a query and using shorter aliases.

Traverse a sample database table, inspect its columns and six rows, and apply the not operator to filter out the row with the name arc from the display.

Learn to filter purchase orders for products by applying two conditions: site value and price less than 35 dollars. Combine these criteria with the and operator to filter the results.

In scenario 10, query a sample production table using three columns: name, product number, and list price, and use the between operator to find prices between 10 and 20 dollars.

Apply SQL scenario 11 on the production table to filter by product id with equal, greater-than, and less-than operators, including orders over 500 and dates before 2013.

Scenario 12 teaches how to query the production.product table to return product name and list price, and filter results using the in operator to specify three price values.

Scenario 13 demonstrates filtering the person.country_region table to show country names starting with v using the like operator and the % wildcard. You can also filter for va names.

Scenario 14 demonstrates filtering non-null values in the production table to identify scrap reasons, using the is not null operator to exclude empty entries.

Learn to find product names for orders with quantity over 20,000 by querying the production table and using a subquery with the any operator.

Scenario 16 demonstrates selecting two columns, name and product number, from the Production table and applying upper and lower functions to convert values to uppercase and lowercase for consistent formatting.

Select the two columns name and product number from a table and use left and right functions to extract the leftmost two characters and the rightmost four digits.

Join the production.work_order and production.product tables in scenario 18 using an inner join on the product id to display caller id, product id, and the product name.

Combine the production and product tables to display product_id and name alongside sales figures, using a left join on the common product_id and aliases for clarity.

Join the product and product_review tables using a right join to display each product name next to its comments, using aliases a and b.

Join production.product with production.productsubcategory using a full join to map each product to its subcategory name, selecting product id and subcategory name, then check for null product names.

In scenario 22, inspect the production.product table to count distinct product_number values and verify there are no duplicates.

Explore a sample database by selecting four columns from the sales order detail, filter by product 7 and the year 2011, and compute total revenue by summing the line total.

Filter records for product 7 in 2011, select four columns (sales order, product, line total, modified date), and compute the average line total to reveal the average sale price.

Identify the lowest stock for product 944 in the production dot product inventory by using the min function, filtering to the 944 records to reveal a minimum quantity of 81.

Scenario 26 demonstrates using the max function on the quantity column of production.product_inventory to find the largest stock for product 7 4 7.

Explore scenario 27 by selecting state code and state name, then join them with a hyphen using concat, and alias the result as state.

Scenario 28 demonstrates calculating the amount lost by multiplying unit price by projected quantity in a table with product, unit price, order quantity, line total, and projected quantity.

Group the inventory by product id and use the min function on quantity to find the lowest quantity per product across locations.

Identify products with fewer than three locations, then compute each product's minimum quantity using min, count, group by, and having to filter by location count.