The Experts below are selected from a list of 76869 Experts worldwide ranked by ideXlab platform

Maribel Yasmina Santos - One of the best experts on this subject based on the ideXlab platform.

  • the data scientist profile and its representativeness in the european e competence framework and the skills framework for the information age
    2017
    Co-Authors: Carlos Costa, Maribel Yasmina Santos
    Abstract:

    The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer Science, Statistics and mathematics.

  • a conceptual model for the professional profile of a data scientist
    2017
    Co-Authors: Carlos Costa, Maribel Yasmina Santos
    Abstract:

    Data-driven web applications support most of the activities in our current world, built upon databases and data services. Consequently, the Data Scientist emerges as a role of major importance, extracting value from data and creating even more data products with increased value. The term Data Scientist attracted a lot of attention during the last years, becoming relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others, such as Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist, showing that a significant part of its knowledge base and skills set are related with Information and Communications Technology (ICT), including programming, machine learning and databases, and also with areas such as computer Science, Statistics, mathematics and information systems.

Cherng-jyh Yen - One of the best experts on this subject based on the ideXlab platform.

  • Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade
    2012
    Co-Authors: M'hammed Abdous, Cherng-jyh Yen
    Abstract:

    Introduction According to a recent survey conducted by Campus Computing (campuscomputing.net) and WCET (wcet.info), almost 88% of the surveyed institutions reported having used an LMS (Learning Management System) as a medium for course delivery for both on-campus and online offerings. In addition to various student information management systems (SISs), LMSs are providing the educational community with a goldmine of unexploited data about students' learning characteristics, behaviours, and patterns. The turning of such raw data into useful information and knowledge will enable institutes of higher education (HEIs) to rethink and improve students' learning experiences by using the data to streamline their teaching and learning processes, to extract and analyse students' learning and navigation patterns and behaviours, to analyse threaded discussion and interaction logs, and to provide feedback to students and to faculty about the unfolding of their students' learning experiences (Hung & Crooks, 2009; Garcia, Romero, Ventura, & de Castro, 2011). To this end, data mining has emerged as a powerful analytical and exploratory tool supported by faster multi-core 64 CPUs with larger memories, and by powerful database reporting tools. Originating in corporate business practices, data mining is multidisciplinary by nature and springs from several different disciplines including computer Science, artificial intelligence, Statistics, and biometrics. Using various approaches (such as classification, clustering, association rules, and visualization), data mining has been gaining momentum in higher education, which is now using a variety of applications, most notably in enrolment, learning patterns, personalization, and threaded discussion analysis. By discovering hidden relationships, patterns, and interdependencies, and by correlating raw/unstructured institutional data, data mining is beginning to facilitate the decision-making process in higher educational institutions. This interest in data mining is timely and critical, particularly as universities are diversifying their delivery modes to include more online and mobile learning environments. EDM has the potential to help HEIs understand the dynamics and patterns of a variety of learning environments and to provide insightful data for rethinking and improving students' learning experiences. This paper is focused on understanding live video streaming (LVS) students' learning behaviours, their interactions, and their learning outcomes. More specifically, this study explores how the interaction of students with each other and with their instructors predicts their learning outcomes (as measured by their final grades). By investigating these interrelated dimensions, this study aims to enrich the existing body of literature, while augmenting the understanding of effective learning strategies across a variety of new delivery modes. This paper is divided into four sections. It begins by reviewing the literature dealing with the use of data mining in administrative and academic environments, followed by a short discussion of the way in which data mining is used to understand various dimensions of learning. The second section explains the purpose and the research questions explored in this paper. The third section describes the background of the study and details its methodological approach (sampling, data collection, and analysis). The paper concludes by highlighting key findings, by discussing the study's limitations, and by proposing several recommendations for distance education administrators and practitioners. Data mining applications in administrative and academic environments At the intersection of several disciplines including computer Science, Statistics, psychometrics (Garcia et al., 2011), data mining has thrived in business practices as a knowledge discovery tool intended to transform raw data into highlevel knowledge for decision support (Hen & Lee, 2008). …

Simon Jackman - One of the best experts on this subject based on the ideXlab platform.

  • estimation and inference are missing data problems unifying social Science Statistics via bayesian simulation
    2000
    Co-Authors: Simon Jackman
    Abstract:

    Bayesian simulation is increasingly exploited in the social Sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary†quantities such as goodness-of-fit Statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social Science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item—response models for the measurement of respondent's levels of political information in public opinion surveys, the estimation and analysis of legislators' ideal points from roll-call data, and outlier-resistant regression estimates of incumbency advantage in U.S. Congressional elections

Carlos Costa - One of the best experts on this subject based on the ideXlab platform.

  • the data scientist profile and its representativeness in the european e competence framework and the skills framework for the information age
    2017
    Co-Authors: Carlos Costa, Maribel Yasmina Santos
    Abstract:

    The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer Science, Statistics and mathematics.

  • a conceptual model for the professional profile of a data scientist
    2017
    Co-Authors: Carlos Costa, Maribel Yasmina Santos
    Abstract:

    Data-driven web applications support most of the activities in our current world, built upon databases and data services. Consequently, the Data Scientist emerges as a role of major importance, extracting value from data and creating even more data products with increased value. The term Data Scientist attracted a lot of attention during the last years, becoming relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others, such as Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist, showing that a significant part of its knowledge base and skills set are related with Information and Communications Technology (ICT), including programming, machine learning and databases, and also with areas such as computer Science, Statistics, mathematics and information systems.

Hamilton Madeline - One of the best experts on this subject based on the ideXlab platform.

  • Direct ellipsoidal fitting of discrete multi-dimensional data
    2020
    Co-Authors: Anwar Rafey, Hamilton Madeline, Nadolsky Pavel
    Abstract:

    Multi-dimensional distributions of discrete data that resemble ellipsoids arise in numerous areas of Science, Statistics, and computational geometry. We describe a complete algebraic algorithm to determine the quadratic form specifying the equation of ellipsoid for the boundary of such multi-dimensional discrete distribution. In this approach, the equation of ellipsoid is reconstructed using a set of matrix equations from low-dimensional projections of the input data. We provide a Mathematica program realizing the full implementation of the ellipsoid reconstruction algorithm in an arbitrary number of dimensions. To demonstrate its many potential uses, the fast reconstruction method is applied to quasi-Gaussian statistical distributions arising in elementary particle production at the Large Hadron Collider.Comment: 20 pages, 5 figures The Mathematica program implementing the ellipsoid reconstruction algorithm can be found here: https://www.physics.smu.edu/web/research/preprints/SMU-HEP-19-01

  • Direct Ellipsoidal Fitting of Discrete Multi-Dimensional Data
    2020
    Co-Authors: Hamilton Madeline
    Abstract:

    Multi-dimensional distributions of discrete data that resemble ellipsoids arise in numerous areas of Science, Statistics, and computational geometry. We describe a complete algebraic algorithm to determine the quadratic form specifying the equation of ellipsoid for the boundary of such multi-dimensional discrete distribution. In this approach, the equation of an ellipsoid is reconstructed using a set of matrix equations from low-dimensional projections of the input data. We provide a Mathematica program realizing the full implementation of the ellipsoid reconstruction algorithm in an arbitrary number of dimensions. To demonstrate its many potential uses, the direct reconstruction method is applied to quasi-Gaussian statistical distributions arising in elementary particle production at the Large Hadron Collider