I am researcher in the Department of Informatics and Statistics and academic director of the
Master in Data Science at
University Rey Juan Carlos in Madrid (Spain). I design and implement
methodologies and computing systems for big data analytics in different areas such as open
online communities, software engineering, content classification or opinion mining.
Felipe Ortega is researcher in the Department of Informatics and Statistics and Academic
Director of the Master in Data Science at University Rey Juan Carlos in Madrid (Spain).
He designs and implements methodologies and solutions for data analytics, with special
interest in complex datasets (usually known as big data). Over the last 10 years, he has
worked in different areas such as peer production and open online communities (Wikipedia,
open source projects, StackExchange, etc.), content classification, opinion mining, energy
efficiency, computer security or quality assurance. Felipe has more than 10 years of teaching
experience in undergraduate and graduate university programs and business schools, covering
a wide range of areas such as computer architecture, computer networks, computer security,
open source software, business models and (big) data analytics.
Felipe received a Master (2003) in Telecommunications Engineering from Alfonso X El Sabio University, and a Ph.D. (2009) in Computer Science from University Rey Juan Carlos. His Ph.D. dissertation presented for the first time a detailed comparison of the ten largest Wikipedia languages from a quantitative perspective, considering aspects such as editing effort, social organization and structure, content quality and editors retention. He also works to develop automated tools, like WikiDAT and methodologies to foster replicable/reproducible research about open online communities. He is also interested in reserach, promotion and training on libre software. He teaches in the Master on Libre software, where he has covered different aspects such as business models, motivations of developers, team organization, project management and data analysis.
I am a strong advocate of the Python and R programming languages for data analytics. My preferred IDEs are Spyder (Python) and RStudio (R, Markdown and LaTeX). I also love IPython.
I believe that many (big) data analytics problems can be solved with some combination of RDBMS, NoSQL and in-memory processing technologies.
I believe that human expertise and evaluation are the key factors in any decision-making process and they can only be augmented (not replaced) with evidence from data analysis.