Este libro trata sobre conceptos elementales junto con scripts cortos de código basado en R Project para hacer análisis inteligente de datos. La relación entre la teoría y la práctica es fundamental en la comprensión de una disciplina, así la aplicación de procedimientos y funciones específicas en tareas elementales es el propósito de este texto. La idea central del texto tiene origen en la asignatura denominada “Análisis Inteligente de Datos”, una cátedra en la que el profesor aporta con elementos fundamentales basados en conceptos y ejercicios prácticos usando R Project. Hoy en día, la disponibilidad de herramientas para la minería de datos es sin duda muy grande. Usuarios con conocimientos básicos pueden aprovechar de utilitarios intuitivos implementados en poderosos entornos de desarrollo. Nosotros hemos querido dar un enfoque al texto hacia una audiencia con mayor relación a la programación y software. Específicamente que constituya una guía básica para estudiantes que inician en el campo de la Inteligencia de datos.

Association rule mining has been studied from various perspectives, all of which have made valuable contributions to data science. However, there are promising research lines, such as the inclusion of continuous variables and the combination of numerical and categorical attributes for a supervised classification variety. This research presents a new alternative for solving the numerical association rule-mining problem from an optimization perspective by using the VMO (Variable Mesh Optimization) meta-heuristic. This work includes the ability for classification when categorical data are available from a defined rule schema. Our technique implements an optimization process for the intervals of continuous variables, unlike others that discretize these types of variables. Some experiments were carried out with a real dataset to evaluate the quality of the rules obtained; in addition to this, this technique was compared with four population-based algorithms. The results show that this implementation is competitive in classification cases and has more satisfactory results for completely numerical data.

Objectives: To obtain a model based in Multi-Criteria Decision Making (MCDM) for the teacher ranking of an Ecuadorian University. Methods/Analysis: The model is adjusted to both governmental and institutional regulations; secondly we analyze the quality requirements of control state institutions and finally, in order structuring the problem through trees of hierarchical objectives according informant type and the modeling of preferences is necessary to get good utility functions; for that use attributes defined by the same university and state control institutions for the higher education with qualitative and quantitative scales. Findings: We obtain the evaluation model for the university professor supported by the concept of multiple criteria such as administrative management, research, teaching and community engagement, the qualitative/quantitative attributes are transformed by the definition of utility functions at intervals between 0 and 1; the established functions come from evaluation models established by the control institutions and the modeling of preferences. In addition, hierarchical target trees are defined for informants such as students, teachers, authorities and peers. Application/Improvement: The Quevedo State Technical University has obtained a model of support decision making for the classification of teachers. This could be implemented through software.

Data repositories currently constitute essential programs within institutions. In fact, universities are the primary institutions that promote the creation, management and storage for the safekeeping of a variety of documents, data and/or projects. This work is carried out within the framework of institutional need and the application of knowledge by the students and professors in the software degree program at a university in Ecuador. This project centers on a web application that makes it possible to manage, upload and download resources. This initial version focuses on the resources related to academic projects, data sets and algorithms. The scalability characteristics have been carefully introduced to ensure a progressive evolution of other types of resources and functionalities, in accordance with educational needs. Several aspects of the web interfaces are based on the characteristics of other existing repositories, and a series of tests related to functionality have been conducted in order to move beyond the introductory stage of use, to begin with, by the university community. This project is carried out according to a conception based on contributions, in which the agents, in principle, are members of the institution. The intensive and collaborative use of this utility will contribute to the growth of a data culture within the institution. Furthermore, the collection and organization of data for the purposes of study and research by the different university degree programs makes it possible to analyze and contrast them from different perspectives.