Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
A Data Science Approach To Defining A Data Scientist, Andy Ho, An Nguyen, Jodi L. Pafford, Robert Slater
A Data Science Approach To Defining A Data Scientist, Andy Ho, An Nguyen, Jodi L. Pafford, Robert Slater
SMU Data Science Review
In this paper, we present a common definition and list of skills for a Data Scientist using online job postings. The overlap and ambiguity of various roles such as data scientist, data engineer, data analyst, software engineer, database administrator, and statistician motivate the problem. To arrive at a single Data Scientist definition, we collect over 8,000 job postings from Indeed.com for the six job titles. Each corpus contains text on job qualifications, skills, responsibilities, educational preferences, and requirements. Our data science methodology and analysis rendered the single definition of a data scientist: A data scientist codes, collaborates, and communicates – …
Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko
Discovery Of Topological Constraints On Spatial Object Classes Using A Refined Topological Model, Ivan Majic, Elham Naghizade, Stephan Winter, Martin Tomko
Journal of Spatial Information Science
In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in …
Φ-Divergence Loss-Based Artificial Neural Network, R. L. Salamwade, D. M. Sakate, S. K. Mathur
Φ-Divergence Loss-Based Artificial Neural Network, R. L. Salamwade, D. M. Sakate, S. K. Mathur
Journal of Modern Applied Statistical Methods
Artificial Neural Networks (ANNs) can fit non-linear functions and recognize patterns better than several standard techniques. Performance of ANNs is measured by using loss functions. Phi-divergence estimator is generalization of maximum likelihood estimator and it possesses all its properties. A neural network is proposed which is trained using phi-divergence loss.