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 Exercises11,937 Learners
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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

    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.
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  2. 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.
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  3. 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.
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  4. 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.
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In the following tracks
SQL for Business Analysts
Hadrien LacroixMona Khalil
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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