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Business Intelligence and Data Warehousing

Business Intelligence and Data Warehousing

Learn with our expert faculty

facultyChristian Brownlees
PhD, University of Florence
UPF and BSE
Director
facultyBesim Bilalli
PhD, Poznan University of Technology and UPC

Instructor
facultyPetar Jovanovic
PhD, UPC and Université libre de Bruxelles

Instructor
facultySergi Nadal
PhD, UPC and Université libre de Bruxelles

Instructor

INTENSIVE COURSE

Business Intelligence and Data Warehousing

Applications will open soon!
  ONLINE
Regular Fee 1100 €
Reduced Fee 600 €

10% early-bird discount applies to payments made on or before March 20, 2023 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: March 20, 2023

  ONLINE
Regular Fee 1100 €
Reduced Fee 600 €

10% early-bird discount applies to payments made on or before March 20, 2023 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: March 2, 2023

  ONLINE
Regular Fee 1100 €
Reduced Fee 600 €

10% early-bird discount applies to payments made on or before March 20, 2023 at 23:59 (CET)

See below for reduced fee eligibility

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Course overview

This online 12-hour course will provide you with the state-of-the-art tools employed for business intelligence and data warehousing. The aim is both to give you an overview of modern solutions for data management, as well as hands-on practice working with relational databases, data warehouses/business intelligence applications, and Big Data systems. 

The course will be organized in three modules: 

  • Introduction to Databases and SQL
  • Data Warehousing and Business Intelligence
  • Big Data and NoSQL

Become familiar with the most important tools used for data warehousing

In the first two modules, the lectures will give an overview on the respective technologies used in transactional and analytical data processing for relational data. In the third module we will focus on NoSQL and Big Data systems. 

After successful completion of the course you will have an understanding of how and where to use which system, and you will have learned: 

  • When a database management system is required for a data-oriented task
  • How to process and query data using SQL
  • The difference between operational and decision support systems
  • The main components of a Data Warehouse
  • How to model and query multidimensional data using SQL
  • How to build and execute Extract-Transform-Load processes to feed your Data Warehouse
  • The difference between Relational and NOSQL databases
  • How to process and query data using a NOSQL system

Get up to speed on the latest developments in data warehousing in a short time

After finishing each module, a hands-on exercise will be used to reinforce the contents seen throughout the module. Thus, you will learn basic SQL syntax and queries to manage a relational database, build and deploy a data warehouse together with web dashboards to display interesting data, and finally learn the basics of managing big data using NoSQL systems.

This course will be taught online but it will be live and interactive.

Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Schedule for Week 1
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Lecture Practical Lecture Practical Lecture
Schedule for Week 2
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Practical        

Course content

Read an outline of topics and bibliography for this course

Course outline

  1. Data ecosystems and the need of Database Management Systems (DBMS)
  2. Introduction to SQL: Querying and managing data
  3. Introduction to Data Warehousing: Multidimensional modeling and OLAP tools
  4. Extraction, Transformation and Load processes and Data visualization (Key performance indicators/Dashboarding)
  5. Overview of Big Data management and processing Fundamentals of NOSQL databases

References

  • Antonio Badia. SQL for Data Science - Data Cleaning, Wrangling and Analytics with Relational Databases. Data-Centric Systems and Applications. Springer, 2020.
  • Ralph Kimball et al. The data warehouse lifecycle toolkit. John Wiley & Sons, 2008.
  • Alejandro Vaisman and Esteban Zimányi. Data warehouse systems. Data-Centric Systems and Applications. Springer, 2014.
  • Matteo Golfarelli and Stefano Rizzi. Data warehouse design: Modern principles and methodologies. McGraw-Hill, Inc., 2009.
  • Ralph Kimball and Joe Caserta. The data warehouse ETL toolkit. John Wiley & Sons, 2004.
  • Pramod J. Sadalage and Martin Fowler. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley, 2012.
  • M. Tamer Özsu and Patrick Valduriez. Principles of Distributed Database Systems. Springer, 2020.

Prerequisites

Participants are expected to be familiar with data structures such as arrays, sets and dictionaries as well as with programming languages, preferably Python.

Students must have their own laptop or desktop computer and a good Internet connection to be able to follow and fully benefit from the course.

Participant Profile

This course is ideal for professionals, researchers, and Master's or PhD students who want to learn about data warehousing and get insights from large collections of data for their business or research projects.

The course may also be of interest to:

  • Master's graduates in Economics, Finance or a similar field who obtained their degree more than five years ago and are interested in learning the latest developments and trends in the field of Data Science
  • Managers of a team of data scientists and whose role consists of overviewing and directing the efforts of the team.

Fees

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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