Online training courses to improve your data knowledge

Educación online
June 10 2020

We live in an era in which training has become an essential element, both to enter and to progress in an increasingly competitive labour market, as well as to be part of research projects that can lead to great improvements in our lifetime.

Summer is coming and with it, a renewed training offer that does not rest at all in the summer season, but quite the opposite. Every year, the number of courses related to data science, analytics or open data increases. The current labour market demands and requires professionals specialized in these technological fields, as reflected by the EC in its European Data Strategy, where it is highlighted that the EU will provide financing "to  expand  the  digital  talent  pool  with  in  the  order  of  250000  people  who will  be  able  to  deploy  the  latest  technologies  in  businesses  throughout  the  EU”.

In this sense, the possibilities offered by new technologies to carry out any type of online training, from your own home with the maximum guarantees, help more professionals to use this type of course each year.

From datos.gob.es we have selected a series of online courses, both free and paid, related to data that may be of interest to you:

  • We started with the Machine Learning and Data Science Course taught by the Polytechnic University of Valencia, which stands out for offering its future students the learning necessary to extract technical knowledge from the data. With a 5-week program, this course introduce R language and, among other things, different preprocessing techniques and data visualization.
  • The Modern Methods in Data Analytics course is another option if you are looking to expand your data training and learn English at the same time. The University of Utrecht will begin to teach this course completely online from August 31, totally focused on the study of linear models and longitudinal data analysis, among other fields.
  • Another of the English courses that will begin on June 16 is a 9-week training programme focused on Data Analytics and which is taught by the Ironhack International School. This is a recommended course for those who want to learn how to load, clean, explore and extract information from a wide range of datasets, as well as how to use Python, SQL and Tableau, among other aspects.
  • Next we discover the course on Business Digitization and Big Data: Data, Information and Knowledge in Highly Competitive Markets, taught by FGUMA (General Foundation of the University of Malaga). Its duration is 25 hours and its registration deadline is June 15. If you are a professional related to business management and / or entrepreneurship, this course will surely be of interest to you.
  • R for Data Science is another course offered by the FGUMA. Its main objective is to show an introductory view to the R programming language for data analysis tasks, including advanced reports and visualizations, presenting techniques typical of computer learning as an extra value. As with the previous course, the deadline for registration for this training is June 15.
  • For its part, Google Cloud offers a completely online and free learning path for data professionals seeking to perfect the design, complication, analysis and optimization of macrodata solutions. Surely this Specialized program: Data Engineering, Big Data, and Machine Learning on GCP fits into the training you had planned.

In addition to these specific courses, it is worth noting the existence of online training platforms that offer courses related to new technologies on an ongoing basis. These courses are known as MOOC and are an alternative to traditional training, in areas such as Machine Learning, Data Analytics, Business Intelligence or Deep Learning, knowledge that is increasingly demanded by companies.

This is just a selection of the many courses that exist as data related training offerings. However, we would love to count on your collaboration by sending us, through the comments, other courses of interest in the field of data to complete this list in the future.