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

Introduction to business intelligence for a online movie rental database

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

Decision Making with simple 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.

Kueri bersarang

Film yang sering disewa

Pelanggan frekuen

Film dengan rating di atas rata-rata

Kueri bertingkat berkorelasi

Menganalisis perilaku pelanggan

Pelanggan yang memberikan penilaian rendah

Film dan penilaian dengan kueri berkorelasi

Kueri dengan EXISTS

Pelanggan dengan setidaknya satu rating

Aktor dalam film komedi

Kueri dengan UNION dan INTERSECT

Aktor muda yang bukan berasal dari AS

Drama dengan peringkat tinggi

Data Driven Decision Making with advanced 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

ROLLUP

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

Data Driven Decision Making with OLAP SQL queries

MovieNow

Dalam kursus ini, Anda akan mempelajari cara menggunakan SQL untuk mendukung pengambilan keputusan. Materinya berbasis studi kasus sebuah perusahaan penyewaan film daring dengan basis data berisi informasi pelanggan, penilaian film, latar belakang aktor, dan lainnya. Anda akan belajar menerapkan kueri SQL untuk mengkaji, misalnya, preferensi pelanggan, keterlibatan pelanggan, dan perkembangan penjualan. Kursus ini juga membahas ekstensi SQL untuk online analytical processing (OLAP), yang memudahkan Anda memperoleh wawasan kunci dari data teragregasi multidimensi.

Film dengan rating di atas rata-rata

Pengambilan Keputusan Berbasis Data dengan SQL

Latihan interaktif praktis