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.

Introductie tot datagedreven besluitvorming

De database verkennen

De tabel renting verkennen

Filteren en sorteren

Werken met datums

Films selecteren

Selecteren uit renting

Aggregaties - data samenvatten

Klantinformatie samenvatten

Beoordelingen van film 25

Jaarlijkse verhuuren analyseren

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.

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

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

In deze cursus leer je hoe je SQL gebruikt om besluitvorming te ondersteunen. De cursus is gebaseerd op een casestudy over een online filmluurbedrijf met een database met klantinformatie, filmbeoordelingen, achtergrondinformatie over acteurs en meer. Je past SQL-queries toe om onder andere klantvoorkeuren, klantbetrokkenheid en omzetontwikkeling te onderzoeken. Ook komen SQL-uitbreidingen voor online analytical processing (OLAP) aan bod, die het makkelijker maken om kerninzichten te halen uit multidimensionale geaggregeerde gegevens.

Data Manipulation in SQL

Leer hoe je een SQL-tabel analyseert en inzichten rapporteert aan het management.

Data-driven beslissingen nemen met SQL

Medewerker data-analist in SQL

SQL voor bedrijfsanalisten

De database verkennen

Data-driven beslissingen nemen met SQL

Praktische interactieve oefening