课程

用 SQL 进行数据驱动决策

中级技能水平

更新时间 2025年11月

学习如何分析 SQL 表并向管理层汇报洞察。

免费开始课程

SQLData Literacy

4小时

15 视频

54 道练习

4,550 XP

68,103

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课程描述

在本课程中，您将学习如何使用 SQL 支持决策制定。课程基于一家在线电影租赁公司的案例研究，数据库包含客户信息、电影评分、演员背景等数据。您将学习如何编写 SQL 查询，分析客户偏好、客户参与度以及销售发展等问题。课程还涵盖用于联机分析处理（OLAP）的 SQL 扩展，帮助您更轻松地从多维汇总数据中获取关键信息。

先决条件

Data Manipulation in SQL

1

Introduction to business intelligence for a online movie rental database

The first chapter is an introduction to the use case of an online movie rental company, called MovieNow and focuses on using simple SQL queries to extract and aggregated data from its database.

Introduction to data driven decision making

Exploring the database

Exploring the table renting

Filtering and ordering

Working with dates

Selecting movies

Select from renting

Aggregations - summarizing data

Summarizing customer information

Ratings of movie 25

Examining annual rentals

2

Decision Making with simple SQL queries

More complex queries with GROUP BY, LEFT JOIN and sub-queries are used to gain insight into customer preferences.

Grouping movies

First account for each country.

Average movie ratings

Average rating per customer

Joining movie ratings with customer data

Join renting and customers

Aggregating revenue, rentals and active customers

Movies and actors

Money spent per customer with sub-queries

Income from movies

Age of actors from the USA

Identify favorite actors of customer groups

Identify favorite movies for a group of customers

Identify favorite actors for Spain

KPIs per country

3

Data Driven Decision Making with advanced SQL queries

The concept of nested queries and correlated nested queries is introduced and the functions EXISTS and UNION are used to categorize customers, movies, actors, and more.

Nested query

Often rented movies

Frequent customers

Movies with rating above average

Correlated nested queries

Analyzing customer behavior

Customers who gave low ratings

Movies and ratings with correlated queries

Queries with EXISTS

Customers with at least one rating

Actors in comedies

Queries with UNION and INTERSECT

Young actors not coming from the USA

Dramas with high ratings

4

Data Driven Decision Making with OLAP SQL queries

The OLAP extensions in SQL are introduced and applied to aggregated data on multiple levels. These extensions are the CUBE, ROLLUP and GROUPING SETS operators.

OLAP: CUBE operator

Groups of customers

Categories of movies

Analyzing average ratings

Number of customers

Analyzing preferences of genres across countries

GROUPING SETS

Queries with GROUPING SETS

Exploring nationality and gender of actors

Exploring rating by country and gender

Bringing it all together

Customer preference for genres

Customer preference for actors

用 SQL 进行数据驱动决策

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