Single Table Operation: Lecture 3: IN, BETWEEN, LIKE, CASE WHEN


ID Title Level FTPR Lecture
11 Actors' last name easy
24%
2.3
12 Actors' last name ending in 'EN' or 'RY' easy
30%
2.3
13 Actors' first name medium
13%
2.3
14 Good days and bad days hard
4%
2.3
15 Fast movie watchers vs slow watchers hard
5%
2.3
22 Average cost per rental transaction easy
43%
2.3
24 Films with more than 10 actors medium
20%
2.3
25 Shortest film easy
46%
2.3
26 Second shortest film easy
29%
2.3
27 Film with the largest cast easy
27%
2.3
28 Film with the second largest cast medium
25%
2.3
29 Second highest spend customer medium
16%
2.3
30 Inactive customers in May easy
21%
2.3
31 Movies that have not been returned easy
27%
2.3
32 Unpopular movies hard
16%
2.3
33 Returning customers hard
8%
2.3
34 Stocked up movies easy
28%
2.3
35 Film length report easy
26%
2.3
102 Histogram by visit session duration mobile hard
12%
2.3
131 Churned accounts fintech hard
10%
2.3
136 Extremely late orders food delivery easy
5%
2.3
138 Happy restaurants food delivery easy
10%
2.3
151 Salary report human resource easy
16%
2.3
155 Driver with the highest cancellation rate ride sharing easy
22%
2.3
156 Cancellation rate by unbanned users ride sharing hard
14%
2.3
160 Sellers with no sales by day ecommerce hard
7%
2.3
176 Employees' annual bonus human resource easy
11%
2.3
177 Purchases by platform report ecommerce medium
9%
2.3
178 Members who worked at both Microsoft and Google human resource medium
36%
2.3