Learn SQL by Doing: 100+ Practical Challenges

Walk through returning each user's top three most viewed articles with sql. Learn to use a cte, window functions, and row_number with partition by user and order by views.

Walk through unifying creators and moderators into a single result set with joins and union options, selecting name and role in MySQL.

Pivot daily ticket status data by day to count open, resolved, and escalated tickets, using case when labels and group by status_date in a practical SQL walkthrough.

Analyze the App Sessions data to identify each app's most active hour using MySQL, counting sessions and ranking peak hours with window functions (partition by app name, order by sessions).

Identify users who logged in from two or more cities on the same day by aggregating by user and date, counting distinct cities, and applying a having clause to flag.

Identify high-intent shoppers who added more than two items in a day but never placed an order, using cart items and orders tables, via an anti-join approach.

Identify the latest valid sale for each active team (completed, at least 500, by an active employee) and show all active teams with nulls when no sale exists.

Learn to compute the lowest movie price by applying eligible discounts via a cross join of movies and offers, filtering by the offers' minimum required price, and selecting best price.

Follow a solution walkthrough for detecting the first overlapping subscription per user in sql, using a self-join and window functions to select the later start date.

Identify the top three products per section by units sold, including ties for third place, using a window function rank with partition by section in MySQL Workbench via a subquery.

Identify binge versus sporadic viewing by calculating days since each user's last watch using lag and date diff in SQL, and label sessions accordingly.

Analyze weekend vs weekday signups and revenue using a weekday function in MySQL, labeling days as weekday or weekend and aggregating total signups and subscription fees.

Discover how to compute the days between a user's first and second book orders, for users with at least two purchases, using partition by user ID and date difference.

Identify gaps between patient visits using SQL; highlight cases exceeding 180 days and return patient ID, previous visit, current visit, and the gap in days via lag.

Demonstrate how to identify tickets with delays over 24 hours using timestamp diff in MySQL, returning ticket id, user id, created timestamp, response timestamp, and delay in hours.

Learn how to return the latest appointment per patient by using a window function in MySQL Workbench, partitioned by patient id and ordered by appointment date, with rank = 1.

Identify customers with a return rate over 50% by aggregating total orders and total returns from orders and order returns tables, then compute the return percentage in MySQL.

Identify users with a failed auto-renewal who manually renewed within 1 to 7 days by joining the subscription_failures and manual_renewals tables.

Identify leads that move from new lead to qualified candidate within seven days and report lead id, the new lead date, the qualified date, and days to convert.

Learn to compute recency, frequency, and monetary values from an orders table over 90 days, and segment customers into champions, loyal, and at risk using a case-based labeling approach.

learn sql by doing with a solution walkthrough that counts gender groups, handles nulls as unspecified, and standardizes values using case when and group by.

Learn to compute an employee hierarchy depth with a top-down recursive sql query, starting at top-level managers (depth zero) and propagating depths to all reports.

Discover a solution walkthrough that combines clients and freelancers with union all, then labels each user as client, freelancer, or both, outputting a unified name and role list.

Identify users active on both website and app in the last 60 days, listing their user IDs, latest website login, latest app login, and total platform logins.

Identify users who completed only one of two actions—view or apply—by joining view and apply tables in sql, returning user IDs with action dates using anti joins or left joins.