The following exercises are a part of a new section (currently under construction) in this website, providing data analysis tasks that require business understanding alongside technical skills.
About this Exercise
Stack Exchange Is a network of sites, allowing different communities learn and share their knowledge.
Movies & TV Stack Exchange – is one of these sites, focused entirely on questions and answers regarding movies and series. The following database is built according to its structure, and simulates some of the data it has.
ERD General Description
This database consists of 4 tables
- Posts – Each post can have different comments, each post can be voted by different users (in order to improve its ranking)
- Votes – Each vote consists of a voting ID, user number (FK), post number (FK), and a date of creation
- Comments – The details regarding the various posts comments
- Users – The details of the users who commented / wrote a post / voted
The database creation script can be downloaded using the following link
The following exercises are constructed in ascending difficulty order. First ones are pretty easy, made for warm-up. The advanced exercises, on the other hand, can be quite challenging.
- How many post were made each year ?
- How many votes were made in each day of the week (Sunday, Monday, Tuesday, etc.) ?
- List all comments created on September 19th, 2012
- List all users under the age of 33, living in London
- Display the number of votes for each post title
- Display posts with comments created by users living in the same location as the post creator
- How many users have never voted ?
- Display all posts having the highest amount of comments
- For each post, how many votes are coming from users living in Canada ? What’s their percentage of the total number of votes
- How many hours in average, it takes to the first comment to be posted after a creation of a new post
- Whats the most common post tag ?
- Create a pivot table displaying how many posts were created for each year (Y axis) and each month (X axis)
-- Basic Analytics -- 1. How many post were made in each year SELECT COUNT(*) number_of_posts, YEAR(CreationDate) creation_year FROM posts GROUP BY YEAR(CreationDate) -- 2. How many votes were made in each day of the week (Sunday, Monday, Tuesday, etc.) ? SELECT COUNT(*) AS 'Number of Posts', DATENAME(DW, CreationDate) AS 'Day of the Week' FROM votes GROUP BY DATENAME(DW, CreationDate) -- 3. List all comments created on 2012-12-19 SELECT * FROM comments WHERE DATEDIFF(DAY, CreationDate, '2012-12-19') = 0 -- 4. List all users under the age of 33, living in London SELECT * FROM users WHERE age < 33 AND location LIKE '%London%' -- Advanced Analytics -- 1. Display the number of votes for each post title SELECT pst.Title, COUNT(*) number_of_votes FROM posts pst JOIN votes vt ON pst.Id = vt.postID GROUP BY pst.Title ORDER BY COUNT(*) DESC -- 2. Display comments created by users living in the same location as the post creator SELECT pst.Id AS 'post_id', pst.Title AS 'post_title', pst.OwnerUserID AS 'created_by_user', usr_p.Id AS 'user_id', usr_p.DisplayName AS 'creator_user_name', usr_p.location AS 'creator_location', cmt.UserId AS 'commentor_id', usr_c.DisplayName AS 'commentor_user_name', usr_c.location AS 'commentor_location' FROM posts pst JOIN users usr_p ON pst.OwnerUserID = usr_p.Id JOIN comments cmt ON cmt.postId = pst.Id JOIN users usr_c ON cmt.UserID = usr_c.Id WHERE usr_c.location = usr_p.location -- 3. How many users have never voted ? WITH "VOTE-CTE" AS ( SELECT id FROM users EXCEPT SELECT userID FROM votes ) SELECT COUNT(*) FROM "VOTE-CTE" -- 4. Display all posts having the highest amount of comments WITH "TOP-COMMENT-POSTS" AS ( SELECT pst.Title, COUNT(*) AS 'number_of_comments' , DENSE_RANK() OVER (ORDER BY COUNT(*) DESC) AS 'comment_count_ranking' FROM posts pst JOIN comments cmt ON pst.id = cmt.postid GROUP BY pst.Title ) SELECT Title FROM "TOP-COMMENT-POSTS" WHERE comment_count_ranking = 1 -- 5. For each post, how many votes are coming from users living in Canada ? -- What's their percentage of the total number of votes SELECT pst.Title, COUNT(*) number_of_votes, SUM(CASE WHEN usr.location LIKE '%canada%' THEN 1 ELSE 0 END) AS 'votes_from_canada', CAST(SUM(CASE WHEN usr.location LIKE '%canada%' THEN 1 ELSE 0 END) AS FLOAT) / CAST(COUNT(*) AS FLOAT) AS 'votes_percentage' FROM posts pst JOIN votes vt ON pst.Id = vt.postID JOIN users usr ON vt.UserID = usr.id GROUP BY pst.Title ORDER BY COUNT(*) DESC -- 6. How many hours in average, it takes to the first comment to be posted after a creation of a new post ;WITH "COMMENTS-TIMING-CTE" AS ( SELECT pst.id AS 'post_id', pst.Title AS 'post_title', pst.creationDate AS 'post_creation_date', cmt.creationDate AS 'comment_creation_date', DENSE_RANK() OVER (PARTITION BY pst.id ORDER BY cmt.creationDate) AS 'comment_rank' FROM posts pst JOIN comments cmt ON pst.Id = cmt.postID ) SELECT AVG(DATEDIFF(HOUR, post_creation_date, comment_creation_date)) AS 'avg_num_of_hours' FROM "COMMENTS-TIMING-CTE" WHERE comment_rank = 1 -- 7. Whats the most common post tag ? -- Note, each post may have 1 or more tags. -- The goal of this question is to find the most common *single* tag ;WITH "CTE-TAGS-SEP" (Tags) AS ( SELECT CAST(Tags AS VARCHAR(MAX)) FROM Posts UNION ALL SELECT STUFF(Tags, 1, CHARINDEX('><' , Tags), '') FROM "CTE-TAGS-SEP" WHERE Tags LIKE '%><%' ), "CTE-TAGS-COUNTER" AS ( SELECT CASE WHEN Tags LIKE '%><%' THEN LEFT(Tags, CHARINDEX('><' , Tags)) ELSE Tags END AS 'Tags' FROM "CTE-TAGS-SEP" ) SELECT TOP 1 COUNT(*), Tags FROM "CTE-TAGS-COUNTER" GROUP BY Tags ORDER BY COUNT(*) DESC -- 8. Create a pivot table displaying how many posts were created for each year (Y axis) and each month (X axis) SELECT * FROM ( SELECT YEAR(CreationDate) AS 'Year', DATENAME(MONTH,CreationDate) AS 'Month', id FROM posts ) AS S PIVOT ( COUNT(id) FOR [Month] IN ([January], [February], [March], [April], [May], [June], [July], [August], [September], [October], [November], [December]) ) AS PVT ORDER BY [Year]