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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.
Introduction to business intelligence for a online movie rental databaseFree
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.Introduction to data driven decision making50 xpExploring the database50 xpExploring the table renting100 xpFiltering and ordering50 xpWorking with dates100 xpSelecting movies100 xpSelect from renting100 xpAggregations - summarizing data50 xpSummarizing customer information100 xpRatings of movie 25100 xpExamining annual rentals100 xp
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.Grouping movies50 xpFirst account for each country.100 xpAverage movie ratings100 xpAverage rating per customer100 xpJoining movie ratings with customer data50 xpJoin renting and customers100 xpAggregating revenue, rentals and active customers100 xpMovies and actors100 xpMoney spent per customer with sub-queries50 xpIncome from movies100 xpAge of actors from the USA100 xpIdentify favorite actors of customer groups50 xpIdentify favorite movies for a group of customers100 xpIdentify favorite actors for Spain100 xpKPIs per country100 xp
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.Nested query50 xpOften rented movies100 xpFrequent customers100 xpMovies with rating above average100 xpCorrelated nested queries50 xpAnalyzing customer behavior100 xpCustomers who gave low ratings100 xpMovies and ratings with correlated queries100 xpQueries with EXISTS50 xpCustomers with at least one rating100 xpActors in comedies100 xpQueries with UNION and INTERSECT50 xpYoung actors not coming from the USA100 xpDramas with high ratings100 xp
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.OLAP: CUBE operator50 xpGroups of customers100 xpCategories of movies100 xpAnalyzing average ratings100 xpROLLUP50 xpNumber of customers100 xpAnalyzing preferences of genres across countries100 xpGROUPING SETS50 xpQueries with GROUPING SETS50 xpExploring nationality and gender of actors100 xpExploring rating by country and gender100 xpBringing it all together50 xpCustomer preference for genres100 xpCustomer preference for actors100 xp
In the following tracksSQL for Business Analysts
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.
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.
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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