SQL Tutorial: Introduction to data driven decision making
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Welcome to this course on data driven decision making. My name is Irene Ortner and together with Profesor Bart Baesens and Professor Tim Verdonck we will guide you through this course.
We assume that you are already familiar with basic SQL queries.
Still, we will quickly refresh some basic SQL statements
and you will learn how you can apply them to extract valuable business insights from your data.
Then you will learn about new SQL techniques to summarize data such as the SQL OLAP extensions the CUBE, ROLLUP and GROUPING SETS operators which are specifically developed as business intelligence tools.
Throughout this course, we will work with a Postgres database from a fictional movie rental company called MovieNow.
MovieNow offers an online platform for streaming movies. Customers can rent a movie for 24 hours.
For all movies, the company stores additional information such as the genre or the main actors.
MovieNow also stores information about customers
and movie ratings.
Here we will give an overview of the tables in the database.
First, in the 'customers' table, we have a column 'customer_id', a number which is a unique identifier for each customer, then we have name, country, gender and date of birth. The final column is the date when the account for MovieNow was created.
The columns for the table 'movies' include a unique identifier movie_id, the title of the movie, the movie genre, the runtime, the release year, and, finally, what it costs to rent the movie.
The table 'renting' records all movie rentals. 'renting_id' is a unique identifier for each movie rental. The column 'customer_id' tells us which customer rented the movie and 'movie_id' tells us which movie the customer rented. The rating a customer gives after w
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