Igor Barahona

Igor Barahona

Here I present an investigation on the evolution and use of vocabulary in data science in the last 13 years. Based on a rigorous statistical analysis, a database with 12,787 documents containing the words "data science" in the title, abstract or keywords is analyzed. It is proposed to classify the evolution of this discipline in three periods: emergence, growth and boom. Characteristic words and pioneering documents are identified for each period. By proposing the distinctive vocabulary and relevant topics of data science and classified in time periods, these results add value to the scientific community of this discipline.

Dalina Aidee Villa, Igor Barahona, Luis Javier Álvarez

Latent Semantic Analysis (LSA) was initially conceived by the cognitive psychology at the 90s decade. Since its emergence, the LSA has been used to model cognitive processes, pointing out academic texts, compare literature works and analyse political speeches, among other applications. Taking as starting point multivariate method for dimensionality reduction, this paper propose a semantic space for Spanish language. Out results include a document text matrix with dimensions 1.3 x10^6 and 5.9x10^6, which later is decomposed into singular values. Those singular values are used to semantically words or text.

Daria Micaela Hernandez, Monica Becue-Bertaut, Igor Barahona

This paper provides a global vision of the scientific publications related with the Systemic Lupus Erythematosus (SLE), taking as starting point abstracts of articles. Through the time, abstracts have been evolving towards higher complexity on used terminology, which makes necessary the use of sophisticated statistical methods and answering questions including: how vocabulary is evolving through the time? Which ones are most influential articles? And which one are the articles that introduced new terms and vocabulary? To answer these, we analyze a dataset composed by 506 abstracts and downloaded from 115 different journals and cover a 18 year-period.

Igor Barahona, Alex Riba, James Freeman

Analytical tools in business management are understood as a combination of information technologies and quantitative methods used to assist stakeholders to make better decisions. The contemporary business environment is dramatically changing by the massive accumulation of data. Now, as never before, the use of analytical tools must be expanded to take advantage of this growing digital universe. This article will apply the laddering technique to see how personal values (or managerial functions) influence a companys adoption of analytical tools. A set of ten in-depth interviews are conducted with CEOs, analytics consultants, academics and businessmen in order to establish quantitative relations among attributes, consequences and personal values. Two easy-to-read outputs are provided to interpret our results. The most important links are quantitatively associated through an implication matrix, and then visually represented on a hierarchical value map. Guidelines for improving the use of analytical tools are provided in the last section