Data-Driven Decision Making in SQL

Learn how to analyze a SQL table and report insights to management.

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4 Hours15 Videos54 Exercises12,254 Learners
4550 XP

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Course Description

In this course, you will learn how to use SQL to support decision making. It is based on a case study about an online movie rental company with a database about customer information, movie ratings, background information on actors and more. You will learn to apply SQL queries to study for example customer preferences, customer engagement, and sales development. This course also covers SQL extensions for online analytical processing (OLAP), which makes it easier to obtain key insights from multidimensional aggregated data.

  1. 1

    Introduction to business intelligence for a online movie rental database

    Free

    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.

    Play Chapter Now
    Introduction to data driven decision making
    50 xp
    Exploring the database
    50 xp
    Exploring the table renting
    100 xp
    Filtering and ordering
    50 xp
    Working with dates
    100 xp
    Selecting movies
    100 xp
    Select from renting
    100 xp
    Aggregations - summarizing data
    50 xp
    Summarizing customer information
    100 xp
    Ratings of movie 25
    100 xp
    Examining annual rentals
    100 xp

In the following tracks

SQL for Business Analysts

Datasets

MovieNow

Collaborators

Hadrien LacroixMona Khalil

Prerequisites

Intermediate SQL
Bart Baesens Headshot

Bart Baesens

Professor in Analytics and Data Science at KU Leuven

Bart Baesens is professor in Analytics and Data Science at the Faculty of Economics and Business of KU Leuven, and a lecturer at the University of Southampton (UK). He has done extensive research on big data & analytics, credit risk analytics and fraud analytics. He regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and fraud & credit risk management strategy.
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Tim Verdonck Headshot

Tim Verdonck

Professor at KU Leuven

Tim Verdonck is a professor in Statistics and Data Science at the Department of Mathematics of KU Leuven (Belgium). He is also a visiting professor at the School of Economics, Management and Statistics at the University of Bologna (Italy), where he gives a course in the Master in Quantitative Finance. He is chairholder of the BNP Paribas Fortis Chair in Fraud Analytics, which investigates the use of predictive analytics in the context of payment fraud. Tim Verdonck is also chairholder of the Allianz Chair Prescriptive Business Analytics in Insurance. His research interests are in the development and application of robust statistical methods for financial, actuarial and economic data sets.
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Irene Ortner Headshot

Irene Ortner

Consultant @ Applied Statistics

Irene did her PhD in Statistics at Vienna University of Technology. During a postdoc at KU Leuven she focused in her research on fraud and anomaly detection with statistical and machine learning tools. Now she works as consultant and data scientist for Applied Statistics bridging the gap between science and business applications.
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Lloyds Banking Group

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Harvard Business School

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Decision Science Analytics, USAA