Interactive Course

Data-Driven Decision Making in SQL

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

  • 4 hours
  • 15 Videos
  • 54 Exercises
  • 3,505 Participants
  • 4,550 XP

Loved by learners at thousands of top companies:

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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.

  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.

  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.

  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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Irene Ortner
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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Tim Verdonck
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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Bart Baesens
Bart Baesens

Professor in Analytics and Data Science at KU Leuven

Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics. He co-authored more than 250 scientific papers and 10 books some of which have been translated into Chinese, Kazakh and Korean, and sold more than 20,000 copies of these books world-wide. Bart received the OR Society’s Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award for best EJOR paper. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.

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Collaborators
  • Hadrien Lacroix

    Hadrien Lacroix

  • Mona Khalil

    Mona Khalil

Prerequisites
Datasets
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