State Unemployment Claims

In today’s Reproducible Finance post, we will explore state-level unemployment claims which get released every Thursday. The last few weeks have shown huge spikes in those claims, of course, due to the coronavirus and statewide lockdown orders, and it got me wondering how these times will look to data scientists in the future. Let’s start by importing unemployment insurance claims data for Georgia. This is a data series that’s reported by all 50 states.

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Some Select COVID-19 Modeling Resources

There is an incredible amount of COVID-19 related material available online. While many dashboards, data sets, shiny apps and models represent significant contributions towards fighting the pandemic, we seem to have reached a point where we should be thinking about standards of quality, and should be exploring avenues for cooperation before launching more individual efforts. Below are a few examples of what I believe are exemplars of different types of COVID-19 related contributions.

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Close Encounters of the R Kind

Affiliation Harrison – Center for Strategic and Budgetary Analysis, Washington DC Cara – Department of the Air Force (Studies, Analyses, and Assessments - AF/A9), Washington DC Disclaimer The views expressed in this article represent the personal views of the author and are not necessarily the views of the Department of Defense (DoD) or the Department of the Air Force. This post is an effort to condense the ‘buzz’ surrounding the explosion of open source solutions in all facets of analysis – to include those done by Military Operations Research Society ( MORS) members and those they support – by describing our experiences with the R programming language.

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February 2020: "Top 40" New R Packages

One hundred sixty-four new packages made it to CRAN in February. Here are my “Top 40” picks in eleven categories: Computational Methods, Data, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualizations. Computational Methods delayed v0.3.0: Implements mechanisms to parallelize dependent tasks in a manner that optimizes the computational resources. Functions produce “delayed computations” which may be parallelized using futures. See the vignette for details. tergmLite v2.

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Comparing Machine Learning Algorithms for Predicting Clothing Classes: Part 4

Florianne Verkroost is a Ph.D. candidate at Nuffield College at the University of Oxford. She has a passion for data science and a background in mathematics and econometrics. She applies her interdisciplinary knowledge to computationally address societal problems of inequality. This is the fourth and final post in a series devoted to comparing different machine learning methods for predicting clothing categories from images using the Fashion MNIST data by Zalando.

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Simulating COVID-19 interventions with R

Tim Churches is a Senior Research Fellow at the UNSW Medicine South Western Sydney Clinical School at Liverpool Hospital, and a health data scientist at the Ingham Institute for Applied Medical Research. This post examines simulation of COVID-19 spread using R, and how such simulations can be used to understand the effects of various public health interventions design to limit or slow its spread. DISCLAIMER The simulation results in this blog post, or any other results produced by the R code described in it, should not be used as actual estimates of mortality or any other aspect of the COVID-19 pandemic.

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Outlier Days with R and Python

Welcome to another installment of Reproducible Finance. Today’s post will be topical as we look at the historical behavior of the stock market after days of extreme returns and it will also explore one of my favorite coding themes of 2020 - the power of RMarkdown as an R/Python collaboration tool. This post originated when Rishi Singh, the founder of tiingo and one of the nicest people I have encountered in this crazy world, sent over a note about recent market volatility along with some Python code for analyzing that volatility.

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Comparing Machine Learning Algorithms for Predicting Clothing Classes: Part 3

Florianne Verkroost is a Ph.D. candidate at Nuffield College at the University of Oxford. She has a passion for data science and a background in mathematics and econometrics. She applies her interdisciplinary knowledge to computationally address societal problems of inequality. This is the third post in a series devoted to comparing different machine learning methods for predicting clothing categories from images using the Fashion MNIST data by Zalando. In the first post of this series, we prepared the data for analysis and used my “go-to” Python deep learning neural network model to predict the clothing categories of the Fashion MNIST data.

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COVID-19 epidemiology with R

Tim Churches is a Senior Research Fellow at the UNSW Medicine South Western Sydney Clinical School at Liverpool Hospital, and a health data scientist at the Ingham Institute for Applied Medical Research, also located at Liverpool, Sydney. His background is in general medicine, general practice medicine, occupational health, public health practice, particularly population health surveillance, and clinical epidemiology. Introduction As I write this on 4th March, 2020, the world is on the cusp of a global COVID-19 pandemic caused by the SARS-Cov2 virus.

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Comparing Machine Learning Algorithms for Predicting Clothing Classes: Part 2

Florianne Verkroost is a Ph.D. candidate at Nuffield College at the University of Oxford. She has a passion for data science and a background in mathematics and econometrics. She applies her interdisciplinary knowledge to computationally address societal problems of inequality. This is the second post in a series devoted to comparing different machine and deep learning methods to predict clothing categories from images using the Fashion MNIST data by Zalando.

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