1. Introduction to Business Intelligence for a Online Movie Rental Database
1.1Introduction to data driven decision making
1.2 Exploring the database
1.3 Exploring the table renting
1.4 Filtering and ordering
1.5 Working with dates
1.7 Selecting movies
1.8 Select from renting
1.9 Aggregations - summarizing data
1.10 Summarizing customer information
1.11 Ratings of movie 25
1.12 Examining annual rentals
2. Decision Making with Simple SQL Queries
2.1 Grouping movies
2.2 First account for each country
2.3 Average movie ratings
2.4 Joining movie ratings with customer data
2.5 Join renting and customers
2.6 Aggregating revenue, rentals and active customers
2.7 Movies and actors
2.8 Money spent per customer with sub-queries
2.9 Income from movies
2.10 Age of actors from the USA
2.11 Identify favorite actors of customer groups
2.12 Identify favorite movies for a group of customers
2.13 Identify favorite movies actors fro Spain
2.14 KPIs per country
3. Data Driven Decision Making with Advanced SQL Queries
3.1 Nested query
3.2 Often rented movies
3.3 Frequent customers
3.4 Movies with rating above average
3.5 Correlated nested queries
3.6 Analyzing customer behavior
3.7 Customers who gave low ratings
3.8 Movies and ratings with correlated queries
3.9 Queries with EXISTS
3.10 Customers with at least one rating
3.11 Actors in comedies
3.12 Queries with UNION and INTERSECT
3.13 Young actors not coming from the USA
3.14 Dramas with high ratings
4. Data Driven Decision Making with OLAP SQL Queries
4.1 OLAP: CUBE operator
4.2 Group of customers
4.3 Categories of movies
4.4 Analyzing average ratings
4.5 ROLLUP
4.6 Number of customers
4.7 Analyzing preferences of genres across countries
4.8 GROUPING SETS
4.9 Queries with GROUPING SETS
4.10 Exploring nationality and gender of actors
4.11 Exploring rating by country and gender
4.12 Bringing it all together
来源：CSDN
作者：agoldminer
链接：https://blog.csdn.net/agoldminer/article/details/103648677