SQL Subqueries: Real-World Exercises for All Levels

Table of contents

1. 1. Subqueries

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   Queries within queries? Discover how to build and use subqueries.

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   1. Introduction
      1. Introduction
      2. Get to know the data – table country
      3. Get to know the data – tables city and mountain
      4. Get to know the data – tables trip and hiking_trip
   2. Very simple subqueries
      1. Get to know subqueries
      2. Subqueries with various logical operators
      3. Subqueries with functions
   3. Subqueries with multiple records in the results
      1. The operator IN
      2. The operator IN with subqueries
      3. The operator ALL
      4. The operator ANY
   4. Correlated subqueries
      1. Get to know correlated queries
      2. Aliases for tables
      3. The operator IN with correlated subqueries
      4. The operator EXISTS
      5. The operator EXISTS with NOT
      6. The operator ALL in correlated subqueries
      7. The operator ANY in correlated subqueries
   5. Other subqueries
      1. Subqueries in the FROM clause
      2. Subqueries in the FROM clause
      3. Subqueries in the SELECT clause
      4. Subqueries – summary

2. 2. Subqueries with the Orchestras Dataset

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   Use subqueries to analyze relationships and trends within an orchestra performance dataset.

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   1. Introduction
      1. Get to know the orchestras database
      2. Average ratings per country
   2. Exercises
      1. Good orchestras that are newer than the Chamber Orchestra
      2. Orchestras from cities visited in 2013
      3. Same number of members as the Musical Orchestra
      4. The average number of members in orchestra
      5. The biggest orchestras
      6. Orchestras that played at home in 2003
      7. The highest-paid musicians
      8. The most experienced musicians
      9. Members who earn more than violinists
      10. German orchestras in Ukraine
      11. Congratulations

3. 3. Subqueries in the Store Sales Data

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   Dive into the retail store database using subqueries. Analyze sales trends and customer behavior to extract detailed insights.

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   1. Introduction
      1. Get to know the store database
      2. Purchase, product and category
      3. Number of purchases for some employees
   2. Exercises
      1. Average unit price in category
      2. Difference between the maximum and minimum price
      3. Products in categories
      4. Purchases in categories
      5. Purchases with two conditions
      6. Percentage of money spent by the customer on the purchase
      7. The number of expensive products in each category
      8. Products in categories, 2
      9. Discontinued, active, and all products in a category
      10. Congratulations

4. 4. Subqueries in the University Data

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   Use subqueries to navigate university data, from student enrollments to faculty assignments and course offerings.

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   1. Introduction
      1. Get to know the university database
      2. Students and courses
      3. Lecturers and their courses
   2. Exercises
      1. Students younger than Noel Durrand
      2. Grades higher than or equal to the average
      3. Courses with more lecture hours
      4. Lots of courses
      5. Number of students with the same or better grades
      6. Above the average
      7. Who passed (and who didn't)
      8. Higher than the average... again!
      9. Congratulations

5. 5. Subqueries in the Sports Data

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   Analyze running competition results with subqueries to evaluate performance trends and athlete statistics across various events.

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   1. Introduction
      1. Get to know the running database
      2. Country's best in 100 meters
      3. Kenyan runners-up
   2. Exercises
      1. Above-average run times
      2. Faster than Bolt
      3. Frequent participants
      4. Country average times
      5. Statistics by country
      6. Congratulations

6. 6. Subqueries in Blog Traffic Analysis

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   Use subqueries to analyze blog traffic and identify key trends in reader engagement and content popularity.

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   1. Introduction
      1. Get to know the blog database
      2. The number of articles by author and category
      3. Traffic in article categories in May 2020
   2. Exercises
      1. Articles of a specific category
      2. Articles with less traffic than the average
      3. The percentage of authors without any articles
      4. The date of the highest traffic
      5. The product with the highest number of views
      6. The date of the highest traffic for each article
      7. Articles with and without traffic
      8. Articles with more traffic than their category average
      9. The most viewed product in each category
      10. Congratulations

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