Program on

Data Visualization

Program on Data Visualization
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Program on Data Visualization

The digital transformation process in which we are immersed in almost all sectors of society, and the need to understand and extract value from the growing amount of data we generate, has led to the emergence of technologies that allow us to explore and process that data: Big Data ecosystems. Many organizations compete to turn data into valuable information, and data visualization stands out as a tool for its ease of use and its applications in all areas.

Data Visualization allows us to explore and understand large volumes of data, discover patterns that allow us to make decisions, optimize processes, and discover new use cases and monetization. This program aims to provide the student with general training on Data Visualization, the business advantages and benefits it can bring, and specific training on the most important tools currently in use.

The program has a markedly practical content, to help the student become fully familiar with the tools studied.


  • Learn the fundamental concepts of Data Visualization for the correct treatment and processing of data through visualizations, which allow them to be converted into useful information and transmit this correctly.
  • Learn 3 of the leading tools in the data visualization market, starting from the concepts and fundamentals of data visualization learned.
  • Identify the types of visualization and ideal tools to respond to specific problems.
  • Understand the current implementation of Data Visualization in the business environment through the analysis of real use cases.

Course Program

Module 1. Introduction to data visualization

Introductory week to data visualization: what it is, where it comes from, what it is used for, why it is so important in the current setting. Types of visualizations and their most effective use.

General aspects of communication and visual perception, critical issues in the creation of visualizations and in the presentation of information, will also be discussed.


  • History of data visualization
  • Data visualization basics
  • Open Data
  • Visualization types and tools
  • Communicate your message effectively
  • Visual perception

Specific objectives

  • Learn the basics of visualization (problems to which it responds, real utility...)
  • Learn the types of charts and when to use each one.
  • Understand the visual problems of the graphs to know how to avoid them, or use them to reinforce the message
Module 2. Introduction to Power BI

Power BI is one of the leading tools in the Business Intelligence sector and one of the most complete, with links to a multitude of data sources and very powerful data transformation and analysis possibilities, as well as dynamic dashboard generation capabilities. and very versatile.

In this module, the tool will be studied in depth, from the ingestion and basic treatment of data to the creation of reports and the most complex modeling to get the best performance out of Power BI.

All this will be done in a very practical way, with data sets and use cases so that the student understands first-hand the real utility of the tool and its options, and learns how to solve data problems with Power BI and extract information and value from large amounts of data with Power BI.


  • ETL process configuration
  • Report creation
  • Data modeling with DAX
  • Report publishing and the Power BI service

Specific objectives

  • Learn how to load data into Power BI and the necessary transformations to be able to generate useful dashboards with it.
  • Learn about the most comprehensive data modeling options, which allow you to use very large volumes of data in Power BI.
  • Perform complete exercises with real datasets in order to understand first-hand the usefulness of the tool.
Module 3. Introduction to Tableau and CARTO

Another of the great leaders in this sector, apart from Power BI, is Tableau. The experience with Power BI allows us to speed up with Tableau and focus on the differences it has with Power BI and the strengths of Tableau, and when it is more convenient to use the different options that each offers.

One of the great types of data is geographic data, and although the previous tools have some capabilities to work with them, this module also covers one of the most important tools for that scenario: CARTO. The tool will be thoroughly worked on by creating maps with real data and the analytical capabilities, and the most important use cases that CARTO enables will be explored.


  • Data transformation with Tableau
  • Creating charts and dashboards with Tableau
  • Creation of static and dynamic maps with CARTO
  • Geographic analytics in CARTO

Specific objectives

  • Learn how to load data into Tableau and the transformations required to generate useful dashboards.
  • Know the main differences between Tableau, Power BI and other similar tools.
  • Learn to load data into CARTO and generate static and dynamic maps
  • Know the types of geographic analytics available and their possible uses
Module 4. Project and use cases

In order to consolidate everything learned, from the more theoretical part to the use of the tools, part of this module is dedicated to carrying out a slightly more complete data exercise, in which the student will have to apply, according to their criteria, the analytics and visualization techniques learned that are necessary to solve the project that is proposed.

In addition, in this module, renowned professionals from the Big Data and Business Intelligence sector will present real use cases in companies, which will allow the student to see possible outputs and applications of the content learned in the course.


  • Visualization project
  • Use case: People Analytics
  • Big Data at Telefónica: analysis of business communications
  • Big Data at Telefónica: IoT - vehicle fleets
  • Use Case: Outliers Collective
  • More Big Data use cases to be determined

Specific objectives

  • Carry out a complete data project, from data acquisition, to cleaning, transformation and visualization, with one of the tools studied.
  • Know use cases in important companies in the sector, to understand possible outputs and the power of what has been studied in the business environment.


ÁS Álvaro Sánchez Pérez Tech Lead – Telefónica Tech IoT & Big Data. Tech Lead – Telefónica Tech IoT & Big Data. DM Delia Majarín HR Project Manager – Experta People Analytics – Telefónica Digital HR Project Manager – Experta People Analytics – Telefónica Digital
DB Daniel Burrueco Business Intelligence Consultant Business Intelligence Consultant ÓM Óscar Marín Miró Co-founder – Outliers Collective Co-founder – Outliers Collective