This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
Traditional data analytics has been revamped; this is the age of augmented analytics. Organizations are now making assured, confident decisions without relying on specialized data… Read More »Augmented Analytics: Democratizing Insights and Empowering Business Users
The world is on the constant lookout for technologies that can help us further improve how we go about not only our daily lives but… Read More »Top Ways in Which Big Data Has Fortified Geolocation Apps
In 1927, W. O. Kermack y A. G. McKendrick described the first mathematical model for infectious diseases using a set of differential equations. This model… Read More »Nonlinear regression of COVID19 infected cases.
This article was written by Jim Frost. Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables… Read More »Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
About 4 months ago, I wrote for this blog an article entitled “List of Quantum Clouds”. In that article, I listed 17 “quantum clouds”. By now,… Read More »Proxy Quantum Clouds and the JupyterHub
Hypothesis testing can be an overwhelming topic to grasp if you’re new to the subject. As well as dealing with all of the different terminology,… Read More »Steps of Hypothesis Testing in One Picture
This article was written by Andy Kiersz. From Ian Stewart’s book, these 17 math equations changed the course of human history A 2013 book by… Read More »The 17 equations that changed the course of history
Bayesian Model for COVID-19 Spread Prediction At present time, there are different methods, approaches, data sets for for modeling COVID-19 spread [1, 2, 3, 4,… Read More »Bayesian Model for COVID-19 Spread Prediction
Check out the following paper unveiled last night in ArXiv: An Open-Source, Industrial-Strength Optimizing Compiler for Quantum Programs, by Robert S. Smith, Eric C. Peterson,… Read More »Rigett's New Quantum Optimized Compiler
#FlattenTheCurve. #StayHome. #QuarantineLife. They’ve all been trending, and it seems like everybody knows that it’s time to stay at home, indoors, and as far apart… Read More »Using Data To Combat COVID-19
