Tag: Patterns

Feed: R-bloggers. Author: Javier Fernández-López. [This article was first published on long time ago..., and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.  Hi there, This is the best meme I’ve found during these days… Well, here it is my “BUT” contribution. Some weeks ago The Washington Post published this simulations about how “social distancing” could help to “flat the curve” of COVID-19 infections. I fell in love with these simulations because their simplicity ... Read More

Feed: R-bloggers. Author: R on OSM. [This article was first published on R on OSM, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. In our last post, we discussed using the historical average return as one method for setting capital market expectations prior to constructing a satisfactory portfolio. We glossed over setting expectations for future volatility, mainly because it is such a thorny issue. However, we read an excellent tutorial on GARCH models ... Read More

Feed: R-bloggers. Author: An Accounting and Data Science Nerd's Corner. Downloading and data cleaning Downloading the data form the Oxford homepage is straightforward. Automatic column detection by read_xlsx() fails so I provide columns manually. suppressPackageStartupMessages({ library(kableExtra) library(dplyr) library(tidyr) library(lubridate) library(tidycovid19) library(ggplot2) library(stringr) library(readxl) library(gghighlight) library(RCurl) }) dta_url The file is organized by country-date and sorted by date. As in essence interventions data is event driven for each country (meaning that interventions happen infrequently at certain dates), I sort the data by country-date to get a better view on its structure. Also, I adjust some names and concentrate on the policy ... Read More

Feed: Redis Labs. Author: Simon Prickett. Redis University is pleased to announce the immediate availability of free, self-paced, on-demand online courses. A lot has changed in recent weeks, and many of us are now at home, perhaps a bit more than we’d like to be. To help make this shelter-in-place time as productive as possible, we figured this would be a great time to give the Redis community the chance to complete as many courses as you can, all at your own pace. Take as many courses as you like—just be sure to complete them before they close on May ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Cheryl To. #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 (physically) as possible. We’re in the midst of a global pandemic, the worst we’ve seen in a century, yet we still see people out and about, on the streets, congregating in large groups, against the unambiguous advice of experts and health professionals.Why jeopardize public safety? Is it due to a lack of information? A quick online search will produce results on how the virus ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. When the coronavirus broke out in the Wuhan province of China in December 2019, Taiwan was immediately in harm’s way.  Taiwan is less than 100 miles from the China mainland, and as recently as January there were a dozen roundtrip flights between Wuhan and Taipei every week. Yet as of March 19, Taiwan has only 108 cases of the coronavirus and one death.  Why? Taiwan used the data from 2003 SARS (Severe Acute Respiratory Syndrome) epidemic that infected over 8,000 globally and killed 774, including 299 in the state ... Read More

Feed: Recent Announcements. Contributor Insights reports show the top contributors impacting system performance and the number of unique contributors in a dataset. Contributor Insights report data can be displayed on CloudWatch dashboards, graphed alongside CloudWatch metrics, and added to CloudWatch alarms. You can enable Contributor Insights for CloudWatch Logs or on your Amazon DynamoDB tables. With DynamoDB, you can view the most accessed and throttled items at a glance by enabling Contributor Insights on a table or global secondary index. With CloudWatch Logs, Contributor Insights allows you to create rules that evaluate patterns in structured log events in real-time. You ... Read More

Feed: Recent Announcements. Amazon CloudWatch Contributor Insights for DynamoDB is now available in the AWS GovCloud (US) Regions. CloudWatch Contributor Insights for DynamoDB is a diagnostic tool that provides an at-a-glance view of your DynamoDB tables’ traffic trends and helps you identify your tables’ most frequently accessed keys (also known as hot keys). You can monitor each table’s item access patterns continuously and use CloudWatch Contributor Insights to generate graphs and visualizations of the table’s activity. Using this information, you can better understand the top drivers of your application’s traffic and respond appropriately to unsuccessful requests ... Read More

Feed: Recent Announcements. Amazon CloudWatch Contributor Insights for DynamoDB is now generally available. CloudWatch Contributor Insights for DynamoDB is a diagnostic tool that provides an at-a-glance view of your DynamoDB tables’ traffic trends and helps you identify your tables’ most frequently accessed keys (also known as hot keys). You can monitor each table’s item access patterns continuously and use CloudWatch Contributor Insights to generate graphs and visualizations of the table’s activity. Using this information, you can better understand the top drivers of your application’s traffic and respond appropriately to unsuccessful requests.   ... Read More

Feed: Recent Announcements. Previously, we launched the now popular RA3.16xlarge nodes for the largest workloads with 48vCPUs, 384 GiB of memory, and support for up to 64TB of Redshift managed storage per node. RA3.4xlarge nodes offer one-fourth compute (12 vCPU), and memory (96 GiB) compared to RA3.16xlarge at one-fourth of the price. By choosing the RA3 node size and number of nodes, you can now provision the right amount of compute for your workload. RA3 nodes are built on the AWS Nitro System and feature high bandwidth networking and large high-performance SSDs as local caches. RA3 nodes leverage your workload ... Read More