BigDataFr recommends: Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data Subjects: Databases (cs.DB) […] This research explores the potential to analyze bank card payments and ATM cash withdrawals in order to map and quantify how people are impacted by and recover from natural disasters. Our approach defines a disaster-affected community’s economic recovery time […]
Author: Big Data
[arXiv] BigDataFr recommends : Big Data analytics. Three use cases with R, Python and #Spark #datascientist
BigDataFr recommends: Big Data analytics. Three use cases with R, Python and Spark Subjects: Applications (stat.AP); Learning (cs.LG) […] Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained […]
[Datasciencecentral] BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology
BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology […] “Many products and applications exist today that can help us take steps toward healthier living. » – Ben Bajarin Big data is everywhere. Yes it’s true that it has always been there but, we are just recently finding ways to best utilize it. […]
[arXiv] BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data
BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data Subjects: Methodology (stat.ME); Computation (stat.CO) […] This paper introduces a class of Monte Carlo algorithms which are based upon simulating a Markov process whose quasi-stationary distribution coincides with the distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo in […]
[Datasciencecentral] BigDataFr recommends: Finding Career Opportunities in AI
BigDataFr recommends: Finding Career Opportunities in AI […] If you’re a data scientist thinking about expanding your career options into AI you’ve got a forest and trees problem. There’s a lot going on in deep learning and reinforcement learning but do these areas hold the best future job prospects or do we need to be […]
[arXiv] BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework Comments: This research was supported by National Science Foundation grant NSF-1549981 Subjects: Learning (cs.LG); Human-Computer Interaction (cs.HC) […] Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation […]
[Datasciencecentral] BigDataFr recommends: 5 Free Statistics eBooks You Need to Read This Autumn
BigDataFr recommends: 5 Free Statistics eBooks You Need to Read This Autumn […] Did you have a good, relaxing break over the summer? Are you refreshed and re-energised, looking forward to a new start, a new you and brushing up on your data analysis skills? If so, I’ve thrown together a collection of a few […]
[arXiv] BigDataFr recommends: Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
BigDataFr recommends: Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data Subjects: Machine Learning (stat.ML) […] Clustering datasets with a mix of continuous and categorical attributes is encountered routinely by data analysts. This work presents a method to clustering such datasets using Homogeneity Analysis. An Optimal Euclidean representation of mixed datasets is obtained […]
[arXiv] BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization
BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence Subjects: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC) […] We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. […]
[Datasciencecentral] BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Full – #iot
BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Part 1 and 2 […] This two part blog is based on my forthcoming book: Data Science for Internet of Things. It is also the basis for the course I teach Data Science for Internet of Things Course. I will be […]