info_stream_views +----------+-----------+--------------+----------+------------+ | post_id | viewer_id | relationship | duration | ds | +----------+-----------+--------------+----------+------------+ | 101 | 9001 | Friend | 85.4 | 2023-09-18 | | 102 | 9002 | Unconnected | 63.0 | 2023-09-19 | | 103 | 9003 | Followee | 12.0 | 2023-09-20 | | 104 | 9004 | Unconnected | 70.1 | 2023-09-20 | +----------+-----------+--------------+----------+------------+ ​ post_reactions +----------+-----------+-------------+------------+ | post_id | viewer_id | post_action | ds | +----------+-----------+-------------+------------+ | 101 | 9001 | like | 2023-09-18 | | 102 | 9002 | comment | 2023-09-19 | | 102 | 9003 | like | 2023-09-19 | | 104 | 9004 | reshare | 2023-09-20 | +----------+-----------+-------------+------------+ ##### Scenario Analyst must write SQL/Python to understand engagement with info-stream content over the last week. ##### Question Using the tables below, write a query to compute the number of DISTINCT posts that received more than 60 seconds of viewing time from unconnected audiences in the past 7 days. Write a second query that returns the average number of reactions per post coming from (a) friends and (b) unconnected viewers during the past 7 days. ##### Hints Filter ds for last 7 days, group by relationship, count distinct post_id for first query, aggregate reactions and divide by distinct posts for second.