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Course on data transformation processes and data integration in Data Science. Basic visualisation

3 weeks (32h.)

You will learn how to design and execute a data extraction, transformation and loading process; Visualise data with Python. Following the ETL model (Extraction, treatment and loading).

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10% lump sum discount

Academic information

Why study this course?
  • Efficiency in data management: ETL enables the automation of data integration processes, which saves time and reduces human error in preparing data for analysis.
  • Improve data quality: During the transformation process, data can be cleaned and cleansed, which improves the quality and accuracy of the data to be used for analysis.
  • Data integration: ETL enables the integration of data from different sources, allowing companies to get a complete view of their business and make more informed decisions.
  • Real-time data analysis: ETL allows companies to obtain real-time data and process it for analysis, enabling faster, data-driven decision making.
  • For those familiar with Python and SQL. People interested in learning how to design and execute a data extraction, transformation and loading process.
Program aims
  • Extracting information
  • Performing transformations with data
  • Implementation of data loads
  • Data visualisation with python
  • Running a complete ETL model
Professional skills

There are several career opportunities for someone with ETL skills: ETL Developer: An ETL developer is responsible for designing, developing and maintaining ETL systems. Data analyst: A data analyst is responsible for analysing large data sets and providing valuable information to the business. Data Architect: A data architect is responsible for designing the structure of a company's data and defining how data is stored, integrated and used. A business consultant helps companies make informed data-driven decisions. Data quality specialist: A data quality specialist is responsible for ensuring that data is accurate, complete and consistent.

Methodology
  • Practical activities from minute 1
  • For each unit there will be a multiple-choice test and at the end of the course a real case will have to be solved.

Study plan

When it comes to analysing data, it usually comes from different sources and in different formats, which makes it less useful. Hence the importance of applying data pre-processing (or ETL, extraction, treatment and loading), for which you will learn about the Talend Open Studio suite. In addition, we teach you how to visualise data with Python, a process by which you will be able to answer questions and, ultimately, make decisions.

The data warehouse. Datawarehouse and its tools; ETL extraction, transformation and loading processes.

This course focuses on data warehousing using a Datawarehouse and the Extract, Transform and Load (ETL) process. It covers the evolution of the ETL process from extraction, transformation and loading to the ELT process, which involves extracting, loading and transforming data. In addition, the creation of ETL processes for the effective handling of large amounts of data in a Datawarehouse is addressed.

Effective visualisation of information

This course deals with data visualisation and explores the theoretical principles of visualisation. It examines the visualisation process and delves into the context of visualisation, the use of colour, the principles of Gesalt, and data and attribute relationships. In addition, real cases of visualisation using graphs are presented, data visualisation libraries are described, and how to generate graphs in Python using Matplotlib and Seaborn is taught. Finally, concrete examples of data visualisation are given.

*The academic program may be subject to changes in line with the changing demand for specific skills in the market. Your employability is our goal.