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Course on Data transformation Integration Processes in Data Science. Basic Visualization

Continuous Availability
3 Weeks (32h)
No timetable | 2h/day recommended

Students learn to design and execute an extract, transform, and load (ETL) process; Visualize data with Python. The ETL model (extract, transform, and load) | 10% discount for payment in full.

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Academic Information

Why this course?

  • Efficient data management: ETL allows data integration processes to be automated, saving time and reducing human error when preparing data for analysis.
  • Improved data quality: During the transformation process, data can be cleaned and filtered, which improves the quality and accuracy of the data to be used for analysis.
  • Data integration: ETL enables data from different sources to be integrated, so companies get a complete overview of their business and can make better informed decisions.
  • Real-time data analysis: ETL enables companies to collect data in real time and process it for analysis, meaning decision-making is faster and driven by data.
  • For connoisseurs of Python and SQL. People interested in learning how to design and execute an ETL process.
Course aims
  • Extract information
  • Transform data
  • Implementation of data loads
  • Data visualization with Python
  • Running a complete ETL model
Professional competencies of the course

There are a range of career opportunities for those with ETL skills: ETL developer: An ETL developer is responsible for designing, developing, and maintaining ETL systems. Data analyst: A data analyst analyzes large sets of data and provides valuable insights to businesses. Data architect: A data architect is responsible for designing a company's data structure and defining how data is stored, integrated, and used. A business consultant can then help companies make informed decisions based on the data. Data quality specialist: A data quality specialist is responsible for ensuring that data is accurate, complete, and consistent.


  • Practical activities right from the start
  • In each unit there is a multiple-choice test and at the end of the course students have to resolve a real-world case.
Study Plan

Study Plan

When it comes to analyzing data, it often comes from various sources and is in different formats and is consequently less useful. For this reason, it is important to prepare data prior to integration (or ETL), and that’s why students will learn about the Talend Open Studio suite. We also teach how to visualize data with Python, a process through which students will then be able to answer questions and, ultimately, make decisions.

Data Store. The data warehouse and extract, transform and load (ETL) tools and processes.

The course focuses on data storage using a data warehouse and the extract, transform and load (ETL) process. It looks at the how the ETL process has evolved. Additionally, students learn about the creation of ETL processes for the effective management of large quantities of data in a data warehouse.

Effective Visualization of Information

This course addresses data visualization and explores the theoretical principles. Students will examine the visualization process and study the visualization context, use of color, gesalt theory principles, as well as data and attribute relationships. They will also look at real cases of visualization using graphs, data visualization libraries, and how to generate graphs in Python using Matplotlib and Seaborn. Finally, we will look at specific examples of data visualization.

*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.
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